# EyesClear — full page content Concatenated text of the load-bearing pages of www.eyesclear.com. Source of all product facts: EyesClear_About_updated.docx, July 2026. --- ## Home URL: https://www.eyesclear.com/ Two AI-supported compliance tools Bring efficiency to financial-crime case resolution and reporting. EyesClear works alongside your existing providers to reduce effort and cost without forcing a platform replacement. Turn every alert into a decision-ready case. EyesClear uses the data you hold and rapidly accesses all relevant external data that you don’t have. Assesses both, identifies the risks and drafts the narrative. Your analysts review, decide and sign off. ✓ Your data and the AI never leave your perimeter — on-prem or private cloud. ✓ Public-record enquiries go out with a name only — never a customer file. Start screening See a case resolved Five free searches on the entities that matter to you · results in minutes One queue · every source 5 open MONITORING EC-4471 HIGH Drafted SCREENING EC-4470 MED Drafted KYC REVIEW · Powered by Investigations EC-4469 LOW Auto-closed ONBOARDING · Powered by Investigations EC-4468 HIGH Drafted AUDIT EC-4467 MED In review Evidence assembled · entities resolved · narrative drafted Awaiting the analyst’s signature One queue · complete evidence · human sign-off The Platform — proven in live banking environments 9 years in continuous production 7 banking clients live 5+ markets in production 10,000 transactions per second Two tools one decision Two separate tools that can work together EyesClear provides two separate AI-supported tools for financial-crime compliance. The EyesClear Platform runs on-premise or in the bank's private cloud for its own data and workflows. The EyesClear Investigations tool operates in the cloud for sourced public-record research. They can work together, but each is available and usable separately. On-premise or private cloud EyesClear Platform Brings your transactions, customer records, alerts and KYC into one place, assesses them, and prepares each case for a decision. Detection, case management and reporting on a single data fabric. It runs on your own infrastructure, alongside the AML and sanctions systems you already have. How the Platform works → Cloud-based · Separate tool · New EyesClear Investigations Screens a company or a person against the public records and reports every finding with its data source. No confidential data is released outside of the bank. It runs on its own, and feeds whatever case system you already use. Free to try for financial institutions and their regulators. What Investigations does → Create a free account → Each stands on its own. Together they are uniquely powerful to close alerts. Decisions compliance can sign How it works One queue. Three clear steps. A signature at the end. Everything that needs a decision — a monitoring alert, a screening hit, a KYC review, an onboarding breach, an audit finding — lands in one queue. EyesClear prepares the case. Your analyst signs it. The decision then flows back into your systems without re-keying. 1 Rules-based collection Transactions, customer records and internal data — and, through EyesClear Investigations, the public records on the counterparty you hold nothing about. Gathered by rules you set, so every step is repeatable and auditable. 2 AI analysis The AI summarises and triages that evidence, maps the entity network and drafts the narrative. The risk is visible at a glance. 3 Human decision Your analyst reviews, decides and signs. Approved actions — account holds, information requests, customer outreach — go back to your systems, every one logged. Value created Lower cost per case The evidence is assembled and the narrative drafted before an analyst opens the case. Faster regulatory response A new typology runs across the full history overnight, and every AI output traces back to source. Less undetected financial crime Analyst time moves from assembling evidence to judging it, across more of the book. Effort comes out of every case — whether EyesClear runs the operation or works alongside what you already have. The Platform in full → Where the efficiency comes from → Why EyesClear is different Two AI architectures, each used where it belongs Every vendor has AI narratives now. The question is which AI, running where . One architecture for the whole problem forces a compromise: keep everything inside the bank and you give up the frontier; send everything out and you export the bank’s data. EyesClear declines both — frontier models work the public record, private on-premise AI works the bank’s own data, and the two meet in one case file that an analyst signs. Frontier models on the public record Best-in-class reasoning on the half of the case you do not hold. EyesClear Investigations works the open record with frontier commercial models, and reports every finding with the quote and the source it came from. What leaves is the name being screened and the search terms derived from it — never a customer file — and no provider trains on those queries or on the reports. What Investigations does → How a report is produced → Private AI on the bank’s own data AI value without data-export risk. The Platform and every inference it makes run on the bank’s own infrastructure — on-premise or private cloud, behind a private LLM gateway. No customer data and no model inference goes to an external cloud or a third-party vendor. Security & LLM governance → Two architectures, one case file The analyst never has to think about which AI did the work. Internal evidence and public evidence arrive in the same case, assessed together and traceable to source on both sides. Whichever side the work came from, one analyst reviews it and one signature closes it. The split is an engineering decision about where evidence lives, not something the compliance team has to operate. How the Platform works → Nine years in production, at the current frontier Current AI, on a system banks already run. Seven banking clients across five or more markets, nine years in continuous production — and the AI inside it is the current generation, not a narrative feature added to a legacy engine. When a better model arrives it is swapped in and re-run against a fixed set of test cases with known-correct outcomes, so a change in behaviour is detected rather than assumed. The maker-checker control around it does not change. Clients → How we compare → Risk mitigated, control retained, effort removed — because the AI is chosen for where the evidence sits, not for where it is easiest to run. Why EyesClear is different Problems and answers Four problems. Four answers. Financial-crime compliance is one of the largest cost centres in any bank, and the pressure lands on the same team every time. Four problems drive it, and one platform answers each. The problem Never enough people for the volume Alert volumes grow with the book and with every new typology. Headcount does not. The team absorbs it by spending longer on each case or by looking at fewer of them — and the research that sits in no system at all gets done by hand, one search at a time. The answer More of the book, by the same team The evidence is assembled and the narrative drafted before an analyst opens the case — inside the bank and outside it alike. The counterparty research someone would otherwise do by hand comes back as a sourced report. Those hours return as coverage: more of the book reviewed, by the same people. The problem Incomplete evidence Raising an alert is the easy part. The cost sits downstream: investigators wait on slow queries, then hand-write each SAR narrative in two to four hours. And on an incoming payment you hold everything on your own customer and nothing on the party who sent the money. The answer Both sides, in one case file The AI assembles, analyses and drafts — working with the data you hold and through EyesClear Investigations, the public records you do not have. The analyst decides and signs off. The problem Audit readiness is a scramble An examiner asks how a case was decided eighteen months ago. The answer is spread across a case system, an email thread and somebody’s memory. The web research behind it leaves no trace at all — nobody recorded what was searched, and the page the analyst read may not say the same thing today. The answer A file that is already examiner-ready Evidence, case history and decisions are held centrally, with full audit trails and instant retrieval for any period. Every AI output traces back to source, and every public-record finding carries the quote and the date it came from — so the outside evidence is as defensible as the inside evidence. Maker-checker records who requested each change and who approved it. The problem Managers have no line of sight Headline numbers arrive monthly, in a spreadsheet, detached from the cases behind them. By the time a backlog or a drift in quality is visible, it has been true for weeks. And nobody can see whether two analysts researched the same counterparty to the same depth. The answer A control plane over the operation Headline numbers drill down to the cases behind them, live. External research runs as a repeatable step rather than as one analyst’s search habits, so what gets checked is the same across the team. A remediation run across the affected history is one action away, through the same maker-checker queue. That work spans multiple workstreams, usually spread across multiple systems, each with its own data, queues and reports. See how EyesClear consolidates them → One platform owns the outcome, and one signature closes it. Four problems, four answers In practice What the analyst and the manager actually do Seven steps, from the moment alert is created up to the case conclusion — same platform, same data, same maker-checker control. 01 The consolidated queue Alerts from every source system land in one place. One queue instead of five logins. 02 The case, fully assembled Everything the analyst needs is on one screen — including, through EyesClear Investigations, what the public records say about the counterparty you hold nothing on. The case starts from evidence, not from a search. 03 Capture, designed by your team Compliance designs what a case captures, and the form goes live without a vendor change request. 04 The maker-checker process The AI drafts. The analyst reviews, edits and signs. Nothing closes without that signature. 05 Straight to the filing The resolved case flows into the regulatory report, fields already populated. 06 Remediation New supervisory input arrives, and the relevant historical cases are re-reviewed through the same queue. 07 The manager’s control plane Headline numbers drill down to the cases behind them. A remediation run is one action away. A lookback in days, not months — through the same maker-checker queue. The Platform in practice Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo --- ## Platform URL: https://www.eyesclear.com/platform-overview Platform overview An AI-powered AML compliance platform for case resolution EyesClear is where a bank consolidates its alerts and analysis needs and resolves them. Rules collect the evidence — from the bank’s own systems, and from the public records outside them through EyesClear Investigations. AI analyses it and drafts the narrative, and an analyst decides and signs. The confirmed outcome flows back into the bank’s systems without re-keying. 01 The consolidation Anything that needs a decision arrives in one queue Compliance work spans ten workstreams, usually spread across five to ten separate systems, each with its own data, queues and reports. EyesClear takes everything that needs investigating and puts it in one place. Customer onboarding CDD and risk scorecards Sanctions screening Names, PEP lists, payments Transaction monitoring Real-time and fraud signals AML monitoring Typologies, alerts, tuning Periodic KYC reviews Ongoing and event-driven Alert investigation Case work, SAR drafting Regulatory reporting SAR/STR, CTR, CRS/FATCA Compliance controls QA, testing, four-eyes Risk assessment Enterprise-wide, at least annual Training and audits Staff awareness, examinations One team, ten workstreams — typically spread across five to ten separate systems, each with its own data, queues and reports. Any of these can raise a case A transaction-monitoring alert A sanctions or name-screening hit A KYC review falling due An onboarding scorecard breach A control or audit finding One queue instead of five logins. 02 The five stages Every case moves through the same five stages From alert to authorised action Input A case is raised An alert or analysis request arrives from any bank system. Collect Evidence is gathered Rules collect the approved internal and external data. Analyse AI prepares the case AI summarises, triages, resolves entities and drafts the narrative. Decide An analyst decides The analyst reviews, edits and signs. Nothing closes automatically. ✓ Human approval Output Action flows back The authorised outcome returns to bank systems without re-keying. AI is the maker. Your analyst is the checker. Every final action requires a human decision. Three of those stages are where the Platform does the work. Rules-based collection The Platform gathers the evidence by rules, so every step is repeatable and auditable. Two sources, because an alert needs both: the bank’s own transactions, customer records and internal data, and — through EyesClear Investigations — the public records on the counterparty the bank holds nothing about. AI analysis The AI summarises and triages that evidence, maps the entity network and drafts the narrative. The risk is visible at a glance. Human decision The analyst reviews, decides and signs. AI is the maker; the analyst is the checker. 03 The outcome Then the decision flows onward, without re-keying Back to the raising system The system that raised the alert gets the disposition. No re-keying, no second queue. Straight into regulatory reporting A confirmed case flows into the filing, fields already populated from the case record. Protective action in the bank Account holds, information requests and customer outreach — each on a human’s sign-off, every action logged to the case file. Each executed only on a human’s sign-off, with every action logged. 04 Oversight Maker-checker governs the case. This governs the operation. Every case is signed by an analyst. The compliance manager gets a control plane over the whole operation — and can act on what it shows without raising a project. A view across the whole population Volumes and ageing, risk concentrations, analyst throughput, outcomes by source system, with AI-supported views that surface risk and efficiency opportunities. Every headline number drills down to the cases and the evidence behind them. Remediation, straight from the dashboard When the digging surfaces a gap — a missing typology, an under-alerted segment or period — the manager triggers a remediation run there and then. The cases flow into the same maker-checker queue. No project, no vendor statement of work. Scheduled reports to the inbox Case ageing, SLA positions, alert volumes, remediation progress — delivered automatically at the cadence you choose. Oversight does not depend on logging in, and the pack is always exam-ready. Oversight that turns into action, and a pack that is always exam-ready. Management oversight and control 05 What sets it apart Key distinguishing characteristics of the Platform Four properties that follow from running the Platform inside the bank rather than beside it. Your data never leaves the building AI value without data-export risk. The Platform, and all AI inference with it, runs on the bank’s own infrastructure — on-premise or private cloud. Nothing is exported to a cloud service or a third-party vendor. Security & LLM governance → Apply new typologies to history — overnight Regulatory response measured in days and weeks, not quarters. New guidance lands, or a new pattern emerges. You update the typology, re-run it across the entire transaction history that night, and review the new alerts the next morning. No vendor statement of work, no scoping documents, no multi-month project. AML remediation → Zero-assembly deployment Faster time-to-value and no integration issues. Detection, case management and reporting arrive as one platform on a single data fabric — not a detection engine with a case system bolted on afterwards. The fabric connects to the warehouses and lakes you already run, so there is no new data-lake programme. Analyst empowerment, with human approval on every close Operational efficiency with control, and a clear audit trail. The AI drafts narratives and assembles evidence. Compliance officers edit rules and typologies directly. Every AI output still traces back to source and needs an analyst’s approval before it counts. The services → AI value without data-export risk, and a regulatory answer in days rather than quarters. What sets the Platform apart Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo --- ## Investigations URL: https://www.eyesclear.com/investigations Cloud-based investigation tool · New Evidence from the public records, quoted and sourced Resolving an alert takes evidence from two places: the data the bank holds, and the data it does not. EyesClear Investigations covers the second — it screens a company or a person against public sources and returns a report in which every claim carries the quote it came from. It records what sources say and stops there: an allegation is labelled an allegation, and a charge is not a conviction. It runs on its own and feeds whatever case system you already use, and it is free for financial institutions and their regulators. 01 Why it exists An alert has two sides. You hold one of them. Take an incoming payment. The beneficiary is your customer: you have the account, the history, the KYC file, the behaviour over years. The party who sent the money is a name on a message from another bank, and you hold nothing on them at all. No amount of internal analysis closes that gap, because the missing half was never inside the bank to begin with. It is in the public records — what the counterparty has been reported doing, where it trades, and who with. Somebody has to go and look, and today that somebody is an analyst with a search engine and no audit trail. Investigations is that half, done properly. It searches sources that are already public and it goes out with a name — never a customer file, never a transaction, never an alert. That is what lets it sit outside the perimeter while the Platform stays inside it: the two are joined in the workflow and separate in the architecture, which is the arrangement the security page sets out in full. How the Platform works → Security & LLM governance → Your data stays inside. Only a name goes out, and only to sources that are already public. Both sides of the transaction 02 What makes it usable A finding you can put in front of an examiner Search is easy and evidence is hard. The difference is whether a finding can still be defended when somebody checks it. Every claim carries its quote Each relationship and each risk finding is reported with a verbatim quote, and that quote is checked against the page it was taken from before the finding is kept. Anything that cannot be located in its source is discarded rather than reported. Nothing is inferred. Reporting, never judging A finding records what a source says, with its date, its severity and its provenance. An allegation is labelled an allegation, an investigation is not a charge, and a charge is not a conviction. Where nothing is found, the report says nothing was identified in the sources searched — never that the subject is clean. Written to be filed The report is built to go into a case file and be read months later by someone who was not there when it was made. Every finding traces back to a source, a date and the words that were actually published. A file that survives being read by someone who was not there when it was made. 03 Two ways to use it A separate tool that can connect to the Platform On its own An analyst screens a name and gets a report back. There is nothing to integrate and nothing to deploy, so a screening can be run on the day the account is opened. It suits onboarding checks, periodic review and enhanced due diligence. Inside the Platform When an alert fires, the public records are often what settles it. Investigations supplies the outside evidence that shows whether an alert is a genuine risk or a namesake, so the case is closed on evidence rather than on an analyst’s recollection. Start with one screening. Bring it inside the Platform when it earns its place. 04 Getting started Free for financial institutions and regulators Sign up with your work email. The service is open to people at financial institutions and at their regulators, and the eligibility check reads your email domain — so please use your work address rather than a personal one. A new account opens with 5 credits, and a screening spends one — five searches on the entities that matter to you. Create a free account Talk to us first No integration, no deployment — a screening can be run the day the account is opened. Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo --- ## Services URL: https://www.eyesclear.com/use-cases Use cases Where the two tools do the work Six moments where a compliance team loses time, and what EyesClear does in each. Some need the Platform, some need Investigations, and some need both. Each tool runs on its own, alongside whatever you already have. 01 The moments Six places a compliance team loses time These are not processes we invented. They are the points in the work you already do where the evidence is hard to get, and where one or both tools do it for you. Investigations You are onboarding a client you know nothing about A new company arrives with its owners, its officers and its history described by the company itself. Investigations screens the company and the people behind it against the public records before the account opens. What comes back goes into the file with its sources, so the CDD decision rests on more than the application form. Platform Investigations A payment arrives from a counterparty you have never seen An incoming SWIFT. You hold the beneficiary’s account, history and KYC file. You hold nothing at all on the party who sent the money. The Platform raises the alert and assembles everything you already have. Investigations supplies the half that was never yours, so the case is decided on both sides of the transaction rather than the half you own. Investigations Platform A regulator asks you to trace an entity and everything connected to it An instruction to act on a named entity — and on whatever it is connected to, which you have to identify before you can act at all. Investigations maps the entity’s public footprint and the parties around it. The Platform then finds every account and transaction in your own book that touches those names, and records what was done about each one. Platform New guidance lands and the back book has to be reviewed A typology changes. Every case already closed under the old one may need looking at again, and the usual answer is a consultant and a three-month project. The Platform applies the new typology across the full history overnight, and the alerts it raises arrive in the same queue the next morning, under the same maker-checker control. Platform A case has to become a filing The evidence is in and somebody has to write it up. A SAR narrative takes two to four hours by hand, and then the same facts are re-keyed into the report. The Platform drafts the narrative from the full evidence set. The analyst edits and signs it. The confirmed case flows into the regulatory report with its fields already populated. Platform Investigations An examiner asks how a decision was made Eighteen months later, someone has to reconstruct who decided what, on what evidence, and why. Evidence, case history and decisions are held centrally with full audit trails and instant retrieval. Every AI output traces back to source, and every public-record finding carries the quote and the date it came from. Take the tool the moment calls for. Neither one asks you to replace what works. Six moments, two tools 02 Efficiency levers Where the efficiency actually comes from Five effects that compound across the operation. They are targets to validate in a pilot, given as ranges because that is how they behave in practice. Efficiency lever How it works Indicative impact Single platform, zero switchover One view of transactions, CRM, KYC and enrichments. No re-keying, no switching between five to eight systems. Analyst time returned Automated workflows and case linking Dynamic workflows, case linking, email integration and auto-closure remove the manual handoffs and the copy-paste. Less handling per case AI narratives on centralised data The AI drafts the SAR narrative from the full dataset. The analyst reviews and approves. Hours become minutes Back-dated runs eliminate projects A new typology applied across full history in minutes, instead of a three-to-six-month consultant remediation. US$100K–500K+ per event Centralised proof and audit readiness All evidence, history and decisions in one place with full audit trails. No folder shuffling at exam time. Capacity freed at exam time Drafting a SAR narrative falls from two to four hours to minutes. Remediation to a new typology falls from months to overnight. The two numbers to watch in a pilot are average handling time per case, and analyst hours returned to higher-value work. Effort comes out of every case, whether EyesClear runs the operation or works alongside it. Business value and return on investment 03 Institutions Who runs it EyesClear is proven with mid-tier and regional banks. The same services are configured for the institution rather than rebuilt for it — a compliance function is the same shape wherever it sits. Retail and commercial banks Broker-dealers and investment firms Payment service providers Asset managers and fund administrators Virtual asset service providers 04 What runs underneath The capabilities behind those moments You do not have to hold all of this in your head to buy it. It is here because procurement asks, and because the moments above are only credible if the parts exist. AI-assisted alert resolution AI maker, analyst checker. The AI assembles the evidence and drafts a recommended disposition. The analyst reviews, edits and signs. Nothing closes without a human decision. Sanctions-alert processing Screening alerts resolved in the same queue as everything else. EyesClear does not screen payments in real time. It takes the sanctions and name-matching alerts your screening platform raises and resolves them through the same maker-checker workflow. Its own analysis can also flag sanctions concerns for referral to the screening team. SAR narrative creation Two to four hours of drafting becomes minutes. Narratives drafted from the alert and the full evidence set — internal records plus externally collected information — ready for review and submission. AI-supported customer risk scoring Risk that reflects what the customer is actually doing. Risk scores driven by each customer’s actual transaction behaviour, not just their CRM and KYC attributes, and refreshed as activity occurs. AI-supported internet investigation External context arrives with the case. Open-source collection that maps a customer’s connections to related parties and external entities, straight into the case file. This is EyesClear Investigations, described in full below — it screens against the public records and reports every finding with the quote it came from. Dynamic forms The compliance team designs the capture, not the vendor. Your team designs what a case captures — KYC refresh fields, asset declarations, any structured follow-up — and the form goes live without a vendor change request. What is keyed in lands in the case and is analysed like any other evidence. AML remediation (back-book review) Months of manual review compressed into days. Historical cases re-reviewed against new typologies or supervisory recommendations, using retroactive reprocessing. Regulatory reporting Local regulatory fit without a local development project. Cases are selected, structured and output in the format each regime requires — Türkiye’s MASAK ŞİB/STR, CRS and FATCA among them — with submission tracking. A new report type is configuration, not development. Compliance control support The control framework is maintained, not just installed. Ongoing support for your control framework: scenario and typology tuning, model-governance artefacts and examination support. How the Platform works → What Investigations does → Which moment would you start with? Most start where the backlog is: an alert queue that will not clear, a remediation that keeps being deferred, or an onboarding check that takes a week. Start screening Request a demo --- ## Security and LLM governance URL: https://www.eyesclear.com/security Security & LLM governance Data sovereignty by architecture, not by contract The EyesClear Platform runs inside the bank’s own environment, and the AI runs on that same private infrastructure. No customer data and no model inference leaves for an external cloud or a third-party vendor. The LLM is sandboxed inside the platform, given only in-house tools, and fed only the data the bank has explicitly granted. EyesClear Investigations is a separate, optional service that searches the public records from outside the perimeter; § 04 sets out exactly what it sends. 01 Security & data architecture Five properties of the deployment On-premise and data residency EyesClear deploys inside your own environment — on-premise or private cloud — and the AI runs on that same infrastructure. No customer data and no model inference goes to an external cloud or a third-party vendor. Data sovereignty is guaranteed by architecture, not by contract. Deployment topology Containerised microservices — data collection, message processing, alerting, case management, reporting and portal — on PostgreSQL with horizontal scaling, an in-memory cache for real-time evaluation, and a private LLM gateway. One engine serves real-time and back-dated processing as two modes. Data and confidentiality management Connection strings, integration credentials and API keys are held as encrypted, centrally managed parameters — never embedded in code or configuration. Connectivity is validated before a source goes live. Access control and segregation Role-based access profiles, departments and divisions set what each user can see and do, down to menu visibility and data partitioning. Analysts reach only the data their mandate permits, and entitlements are managed centrally. Connectivity Universal connectors take SWIFT and ISO 20022, real-time queues (MQ and Kafka), and database and file sources covering CRM, transactions, risk and KYC — mapped into the single data fabric. 02 LLM security Containment by design LLM technology is a genuine game changer, and it must be fully understood before it touches production. The value is too large to ignore. As with every process a bank operates, good governance is what removes the risk. Sandboxed by design The LLM runs inside EyesClear only. It has no access to bank systems, databases or applications. It sees the case data the Platform hands it, and nothing else. Containment is enforced by architecture, not by a prompt or a policy. In-house tools, deliberately Everything the agents can do is defined by tools written in-house. Each one is a known, audited capability with defined inputs and outputs. An LLM without tools is a parrot; an LLM with uncontrolled tools is a risk. Ours gets exactly the tools the workflow needs, and no more. Rules-based collection, not crawling Your analysts and technology teams configure every source, field and refresh. Some tools in the market crawl internal data instead. The results look impressive, but in our view that pattern is not secure enough for a bank. Nothing is read that was not explicitly granted. AI value without data-export risk. LLM security, enforced by architecture 03 Defensibility What an examiner can be shown Explainability The AI never decides on its own. Every output traces back to the source data and is reviewed, edited and approved by an analyst before it is acted on. There is no black-box score to defend. Auditability All evidence, case history and decisions are held centrally with full audit trails and instant retrieval for any period. Activity logging records who opened which screen and what they did — examination and internal audit without folder-shuffling. Model and change governance Maker-checker (four-eyes) governs every material configuration change — scenarios, thresholds, typologies, case templates, report types, user permissions — with a full record of who requested it and who approved it. How the Platform works → How we compare → FAQ → 04 EyesClear Investigations The one thing that runs outside, and what it sends EyesClear Investigations is a separate, hosted service that searches the public records. It is not part of the Platform deployment, it is provisioned separately, and the Platform does not require it. What it does not touch It has no connection to the Platform's data fabric and no access to customer records, transactions, alerts or cases. Nothing in the Platform is exposed by running it. What does leave The name being screened, and the search terms derived from it, are sent to the public sources and the language models the service uses. That is the whole of it — no customer file, no transaction, no alert. Treat a screening as a public-record enquiry about a named subject, and scope it the way your policy scopes one. Why it is not inside the perimeter The evidence it gathers is on the open web, so the search has to happen there. Putting the service inside the bank would not keep the enquiry inside the bank; it would only move where the request originated. What Investigations does → Two services, one boundary, and it is written down. Bring your security team The architecture questions are the ones we like being asked. A demo can start with the deployment diagram rather than the dashboard. Start screening Request a demo --- ## Investigations methodology URL: https://www.eyesclear.com/methodology Legal How EyesClear Investigations works A public description of how a screening report is produced — what is searched, where a language model makes a judgement, what a human decides, and what the results cannot tell you. Published so that anyone relying on a report, or named in one, can understand how it was made. Methodology Version 1.0 · 1 October 2026 This describes the method, not the implementation. It does not reproduce our prompts or source code. What a screening is A screening answers one question about one named party: what does the public records say about this company or person? The subject is chosen by our client — a regulated financial institution — because that party is, or is proposed to be, a customer or counterparty. We do not select subjects ourselves, and we do not select them by any personal characteristic. Each screening is a live lookup. We do not maintain a database of people. Nothing is pre-computed, and no profile accumulates between screenings. What it searches Reference lists , held by us and refreshed daily from the issuing authority: the OFAC Specially Designated Nationals and consolidated non-SDN lists, the UN Security Council Consolidated List, the EU consolidated financial sanctions list and Annex IV to Regulation 833/2014, the UK OFSI consolidated list, and US export-control lists from the Bureau of Industry and Security and the State Department. Registers and archives , queried live by name: company registers, insolvency and bankruptcy notices, court archives, watchlist aggregators and published news. The open web , searched by a language model with a web-search tool, which reads the pages it finds and cites the passage each statement rests on. A screening does not run every source. Which sources are worth consulting depends on the subject — its type, where it operates, and what the client asked — and the report records which ran, which were skipped, and why. Where a model makes a judgement, and where it does not We separate the mechanical from the evaluative, deliberately. Mechanical, done in code: searching a name; retrieving records; filtering sources by whether they can apply to this kind of subject at all. These steps are designed to be generous — they surface candidates rather than settle them, because a filter tuned to reject look-alikes will also reject real matches. Evaluative, done by a language model, and disclosed as such: Identity. Does this record concern the subject, or a different party with a similar name? This is the single most consequential judgement in the system. Seriousness. What stage did the matter reach, as the record states it — an allegation, an investigation, a charge, a penalty, a conviction? Relevance. Does the source say something a compliance analyst needs, or does it name the subject only in passing? We use commercial language models from established providers for these steps. No model is trained on our clients' queries or on the reports we produce. Never done by a model: predicting the outcome of any case, advising on any legal position, or deciding anything about a person. Those are outside the service by design. What happens to a candidate record A search returns candidates for the subject's name. Each candidate is assessed for identity. Those judged to concern a different party are set aside and recorded — they appear in the run detail as considered-and-rejected, and never in the report as findings. Surviving matters are placed on the severity scale and carry the source, a link to it, and a quotation supporting the point reported. Where identity cannot be settled, the matter is kept and labelled unconfirmed, requiring review. It is never silently discarded and never presented as established. The report is assembled with the coverage bounds for every check that ran. A human analyst reads it and decides what to do. The service does not act on anything. The severity scale Findings are placed on a scale that describes what the record shows, not our view of the person: Unverified claim — an assertion we could not tie to an authority's own record. Allegation — a claim made in proceedings or reporting, not determined. Investigation — an authority is or was examining the matter. Charge — a formal accusation has been brought. Penalty — a sanction, fine or regulatory measure has been imposed. Conviction — a court has determined the matter. An allegation is not a finding of wrongdoing. Nothing in a report asserts that any person is guilty of anything. What a report cannot tell you Absence is not evidence of absence. A screening that returns nothing means we found nothing, not that nothing exists. Sources go missing, sit behind paywalls, publish in languages or registers we did not reach, or are simply wrong. Coverage is bounded, and every check says how. For example, UK court judgments are searched through the National Archives' Find Case Law service, which covers England and Wales (plus UK-wide Supreme Court and Privy Council decisions), mostly from the early 2000s; Crown Court and County Court judgments are received only for selected cases and magistrates' courts are not routinely included; many decisions are given verbally and never transcribed; and settled or withdrawn proceedings produce no judgment at all. A clean court result is not a criminal record check. Every other source carries its own bound in the same way. Identity groupings are an assessment, not a certainty. Names are shared. Where we group findings under one identity, that is our system's best assessment and is stated as such. A report is a snapshot. It reflects what was findable at the moment it ran. Sources change, and we do not monitor them afterwards or update reports already produced. Because we hold no copy of any source collection, a record withdrawn or anonymised at source simply stops being returned to us. Sources are linked, not replaced Every finding links to the source it came from. We do not reproduce records in full, host copies, or index their text. Quotation is limited to a short extract supporting the specific point reported. The report tells the reader to check the cited source before acting on anything. That instruction is the point of the design, not a disclaimer attached to it. Testing and review We maintain a fixed set of test cases with known-correct outcomes and re-run them whenever the models or the method change, so that a change in behaviour is detected rather than assumed. We will test specifically for unequal treatment across communities, by re-running those cases with subject names substituted across linguistic and national origins and comparing the outcomes. Our use of court records is reviewed annually against the Ministry of Justice's nine principles for computational analysis, and the outcome of that review is recorded. Where it changes how the service works, this statement is updated to match. Licences and attribution We hold the licences our sources require and use each source within its terms. Where a licence requires attribution, we give it: Contains public sector information licensed under the Open Government Licence v3.0. Contains information licensed under the Open Justice – Licence v2.0. If a report is wrong about you If you appear in a report and believe we have misread a source or matched the wrong person, write to informationsecurity@eyesclear.com with the report reference and we will investigate. Where the fault is in our reading or our matching, we correct it. Where the fault is in the source, correcting the record usually belongs with the source itself, and we will say so. Nothing here limits any rights you have under data protection law. Further detail is available to The National Archives and to regulators on request. --- ## How we compare URL: https://www.eyesclear.com/how-we-compare How we compare How to compare AI compliance platforms For a bank, an AI compliance platform brings AI into regulated work such as AML alert resolution, investigations, case management and reporting. AI-drafted narratives are now near-universal, so the useful comparison is not whether a platform has AI. It is where the AI runs, who makes the decision, whether the output traces to evidence, and whether the bank’s data leaves its perimeter. 01 The providers Three types of AI compliance platform Agentic decisioning platforms Agentic AI that executes decisions within policy, built inside Tier-1 screening operations. Cloud AI agent overlays Cloud AI agents that sit on top of the systems a bank already runs. SaaS monitoring suites SaaS transaction monitoring and case management, paired with a copilot. The pattern is consistent. The alternatives bring AI to compliance either by taking the decision away from the analyst, or by taking the data out of the bank. EyesClear does neither. 02 Capabilities AI compliance platform evaluation checklist A shortlist should be tested against the operating model, not the AI label. Compare how each platform prepares a case, protects bank data, preserves human accountability, records its evidence and connects the signed outcome to the systems and reports around it. Key: ✓ full · ◐ partial · ✗ not available Capability EyesClear Agentic decisioning platforms Cloud AI agent overlays SaaS monitoring suites AI case preparation — narratives, evidence assembly, triage ✓ Full ✓ Full ✓ Full ✓ Full All AI inference on bank-owned infrastructure — no data export ✓ Full ◐ Partial ✗ Not available ✗ Not available Human decision on every close — maker-checker by design ✓ Full ◐ Partial ◐ Partial ✓ Full Compliance data fabric — connected to the bank’s existing sources ✓ Full ✗ Not available ✗ Not available ◐ Partial Cross-system alert consolidation — resolved in one place ✓ Full ◐ Partial ✗ Not available ✓ Full Actions fed back into bank systems on human sign-off ✓ Full ◐ Partial ◐ Partial ◐ Partial Retroactive reprocessing of full transaction history ✓ Full ✗ Not available ✗ Not available ◐ Partial Case management, workflow and dynamic forms ✓ Full ◐ Partial ✗ Not available ✓ Full Local regulatory reporting (MASAK ŞİB/STR, CRS/FATCA) ✓ Full ✗ Not available ✗ Not available ✗ Not available Sanctions/screening alert resolution ✓ Full ✓ Full ✓ Full ◐ Partial Proven with mid-tier and regional banks — deployment in days ✓ Full ◐ Partial ◐ Partial ◐ Partial Real-time payment screening / interdiction ✗ Not available ◐ Partial ✗ Not available ✗ Not available Browser copilot plugin over third-party systems ✗ Not available ✗ Not available ✗ Not available ✓ Full Multi-tenant SaaS delivery — nothing to run at the bank ✗ Not available ◐ Partial ✓ Full ✓ Full Detection, data fabric, case management and reporting as one platform, on your infrastructure. 03 Our gaps What EyesClear deliberately does not do The last three rows are not omissions. Whilst EyesClear may do these, in the recommended configuration, EyesClear does not sit in the real-time payment path, does not float a copilot over third-party screens, and is not a multi-tenant SaaS. EyesClear is the investigation and decision platform inside the bank. If you need payment interdiction at the moment of settlement, that is a different product category, and we will have to discuss separately. A human signature on every close, and local reporting produced natively. Our position, stated plainly Bring your shortlist The comparison worth having is against the alternatives on your own shortlist, on your own requirements. Start screening Request a demo --- ## FAQ URL: https://www.eyesclear.com/faq Frequently asked The questions due diligence actually asks Short answers to the questions that come up in evaluation: architecture, governance, coverage, what leaves the bank when Investigations screens a name, and what EyesClear deliberately does not do. Questions Can AML AI run fully on-premises? + Yes. EyesClear deploys inside the bank’s own environment — on-premise or private cloud — and the AI runs on that same infrastructure behind a private LLM gateway. No customer data and no model inference goes to an external cloud or a third-party vendor. Data sovereignty is guaranteed by architecture, not by contract. Who approves an AI-drafted SAR narrative? + An analyst. The AI drafts from the alert and the full evidence set; the analyst reviews, edits and signs. AI is the maker and the analyst is the checker — nothing closes and nothing files without that signature. How does retroactive typology reprocessing work? + One engine serves real-time and back-dated processing as two modes. You update the typology, re-run it across the entire transaction history the same night, and review the new alerts the next morning. No vendor statement of work, no multi-month remediation project. Does EyesClear replace our screening system? + No. EyesClear does not screen payments in real time. It takes the sanctions and name-matching alerts your screening platform raises and resolves them through the same maker-checker workflow. Its own analysis can also flag sanctions concerns for referral to the screening team. How long does deployment take? + Days, rather than a multi-month integration programme. Detection, case management and reporting arrive as one platform on a single data fabric that connects to the warehouses you already run, so there is no new data-lake programme to complete first. Which regulatory reports are supported? + Configurable report types, field catalogues and transformation rules generate Türkiye’s MASAK ŞİB/STR submissions on the current schema (Communiqué No. 30), the CRS and FATCA regimes, and other complex outputs — with submission tracking, retry handling and scheduling. A new report type is configuration, not development. How is the LLM prevented from accessing bank systems? + By architecture, not by policy. The LLM runs inside EyesClear only, with no access to bank systems, databases or applications. It sees the case data the Platform hands it, and nothing else. Everything the agents can do is defined by tools written in-house, each a known, audited capability with defined inputs and outputs. What data sources can EyesClear ingest? + Universal connectors take SWIFT and ISO 20022 (MT103, pacs.008, camt and related messages are handled natively), real-time queues such as MQ and Kafka, and database and file sources covering CRM, transactions, risk and KYC — all mapped into the single data fabric. Does the AI crawl our internal systems? + No. Data enters through rules-based collection that your analysts and technology teams control — every source, field and refresh is explicitly configured. Some tools in the market crawl internal data instead; in our view that pattern is not secure enough for a bank. Nothing is read that was not explicitly granted. What efficiency gain should a pilot validate? + Measure it rather than take a figure from us. The two numbers to watch are average handling time per case and analyst hours returned to higher-value work, both before the pilot and during it. Every case EyesClear touches carries its own timestamps and audit trail, so the comparison is drawn from your own book rather than quoted from someone else’s. Is EyesClear SaaS? + No. EyesClear is not multi-tenant SaaS. It runs inside the bank’s own environment as containerised microservices — that is the point of it. It is the investigation and decision platform inside the bank. What if we have no GPU infrastructure? + EyesClear can arrive with right-sized private AI hardware, so the LLM layer comes inside the perimeter as equipment rather than as a cloud dependency. Ask us where that option stands for your environment. Can our compliance team change rules without us calling you? + Yes — that is the design. Compliance officers edit rules and typologies directly, and your team designs what a case captures through dynamic forms that go live without a vendor change request. Every material change still passes through a maker-checker (four-eyes) workflow with a full record of who requested it and who approved it. Can we use EyesClear Investigations without the Platform? + Yes. Investigations runs on its own: an analyst screens a name and gets a report back, with nothing to integrate and nothing to deploy, so it can be used on the day an account is opened. It also feeds whatever case system you already run. Inside the Platform it becomes the external-evidence step of a case, but that is an option rather than a requirement. What leaves the bank when we screen a name? + The name being screened and the search terms derived from it, sent to the public sources and the language models the service uses. That is the whole of it — no customer file, no transaction, no alert. Investigations has no connection to the Platform’s data fabric and no access to customer records or cases, and no provider trains on the queries sent or on the reports produced. Treat a screening as a public-record enquiry about a named subject and scope it the way your policy scopes one. Why is Investigations hosted rather than on-premise? + Because the evidence it gathers is on the open web, so the search has to happen there. Putting the service inside the bank would not keep the enquiry inside the bank; it would only move where the request originated. The bank’s own data stays where it always was — the Platform and all of its AI inference run inside your perimeter. How do we defend a public-record finding to an examiner? + Every relationship and every risk finding is reported with a verbatim quote, and that quote is checked against the page it was taken from before the finding is kept. Anything that cannot be located in its source is discarded rather than reported, and nothing is inferred. A finding records what a source says, with its date, its severity and its provenance: an allegation is labelled an allegation, an investigation is not a charge, and a charge is not a conviction. Where nothing is found, the report says nothing was identified in the sources searched — never that the subject is clean. Is Investigations really free? + It is free for financial institutions and their regulators, with five screenings to start and no integration work. Sign up with a work email from a financial institution or a regulator. How would we defend the AI to an examiner? + The AI never decides on its own. Every output traces back to the source data and is reviewed, edited and approved by an analyst before it is acted on, so there is no black-box score to defend. All evidence, case history and decisions are held centrally with full audit trails, and activity logging records who opened which screen and what they did. If a question is not here, ask it — the architecture ones are our favourites. Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo --- ## Company URL: https://www.eyesclear.com/company Company The company behind the Platform EyesClear Ltd is a UK-registered company (England & Wales no. 10432953) with operations in London, Istanbul and Tampa. It is run by a team with senior backgrounds at Citi, EY, Lloyds and leading Turkish banks, and its registry and LEI records are public. 01 Team Who runs it More than 90 years of combined experience across banking, compliance, risk, operations and technology, with senior backgrounds at Citi, EY, Lloyds and leading Turkish banks. Erkin Oksel Founder & CEO Citi — CIB & Digital Controls. MBA, Oxford Saïd. Nine years building EyesClear. Uner Nabi Chairman EY Partner. More than 30 years’ experience with global financial institutions and regulators. Öznur Evliçoğlu COO More than 20 years in banking technology. Runs all live deployments from Istanbul. Behind them, a delivery team of software, data, QA, implementation and DevOps engineers running deployment and support across markets. Track record and markets → 02 Verify us Check the record before you talk to us Vendor due diligence starts with whether a company is what it says it is. Ours is on the public record. Companies House — England & Wales no. 10432953 → Bloomberg LEI (legal entity identifier) record → LinkedIn → Nine years in continuous production, and profitable. 03 Offices Three offices Canary Wharf, London · photo: IR Stone London Level39, One Canada Square London E14 5AB United Kingdom Istanbul Esentepe Mah., Ali Kaya Sk. Polat Plaza, A blok, 34394 Şişli Türkiye Tampa 4522 W Village Dr., Unit #1406 Tampa, FL 33624 United States +44 (0)203 675 1878 · feedback@eyesclear.com Our values → 04 Where this goes next The roadmap extends the same architecture Private AI, one data fabric, human decisions — in four directions. Deeper sanctions-alert intelligence AI-assisted classification and resolution of the sanctions and name-screening alerts your screening systems raise — the same maker-checker treatment monitoring alerts already get, on the same on-premise LLM layer and audit trail. Conversational investigation Analysts question the data fabric in plain language — counterparty networks, exposure over any period — with every answer traceable to source and under the same governance as every other AI output. A private AI appliance option For banks without GPU infrastructure, EyesClear can arrive with right-sized private AI hardware, so the LLM layer comes inside the perimeter as equipment rather than as a cloud dependency. Compliance data as an institutional asset The behavioural data compliance already assembles — customer activity, networks, transaction patterns — progressively serves Treasury, Operations and Audit. Compliance stops being pure overhead. Compliance stops being pure overhead. Where EyesClear goes next Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo --- ## Clients URL: https://www.eyesclear.com/clients Clients Seven banks. Five markets. Nine years. EyesClear serves reputable banks across a diverse set of markets. The Platform has been profitable and in continuous production for nine years, run from offices in London, Istanbul and Tampa. 9 years in continuous production 7 banking clients live 5+ markets in production 10,000 transactions per second Where it runs Proven with mid-tier and regional banks EyesClear is built for institutions carrying a Tier-1 compliance workload without a Tier-1 budget to assemble it from five separate vendors. Deployment is measured in days, and the Platform runs on the bank's own infrastructure. Client locations and offices. EyesClear serves reputable banks across a diverse set of markets. Nine years in continuous production, and profitable. Company and verification → What we deliver → Try it on a name you already know Investigations is free for financial institutions and their regulators — five screenings to start, nothing to integrate. Or book a demo and we will run the Platform on your own scenarios. Start screening Request a demo