AI & Intelligent Automation
Applied AI for document-heavy, regulated operations
Financial institutions handle enormous volumes of documents and institutional knowledge. We engineer AI systems that read, validate and explain, reducing repetitive effort while keeping specialists firmly in control.
For executives and engineers alike
How we approach AI in financial services
For business leaders, the question is where AI reduces effort and risk. For technology teams, it is how to make AI reliable, auditable and secure. Our approach answers both.
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Assist, don't replace
AI prepares, compares and highlights. Experts decide.
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Evidence for every output
Every extracted value and finding links back to its source.
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Measured on your data
We evaluate on the institution's own documents, not generic benchmarks.
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Deployed under your control
Private and on-premise options for sensitive information.
Document Intelligence
Turning financial documents into structured, usable data
Financial operations still depend on documents: invoices, bills of lading, certificates, guarantees and correspondence. We build pipelines that read, classify and extract information from them, with people kept firmly in control.
A typical document intelligence pipeline combines several techniques. Documents are classified by type, text is recovered using OCR, and information is extracted into structured fields using a combination of layout analysis, language models and vision language models. Every extracted value is linked back to the place in the document it came from.
For business teams, the outcome is less repetitive data entry and more consistent handling. For technology teams, it is a modular pipeline that can be tuned, measured and deployed within the institution's own infrastructure.
Document classification
Automatic identification of document types in mixed submissions.
OCR
Text recovery from scanned and photographed documents, including complex layouts.
Information extraction
Structured fields captured with source references for every value.
Vision language models
Models that read layout, tables and stamps as well as text.
Document summarization
Concise summaries that help specialists orient themselves quickly.
Human-in-the-loop review
Clear review screens where people confirm, correct and approve.
Intelligent Validation
Checking consistency across documents and data
Once information is structured, it can be checked: against the transaction, against business rules and against other documents in the same presentation.
Intelligent validation combines deterministic business rules with AI-assisted comparison. Rules handle what can be precisely specified; AI helps surface inconsistencies in free text and varied formats that rules alone miss.
Each finding is presented with its evidence, so a specialist can see exactly why it was raised and decide what to do.
Cross-document consistency
Comparison of names, amounts, dates and descriptions across a document set.
Rules-based checks
Deterministic validation against configurable business rules.
Evidence-based findings
Every flag links to the source text and the rule or reasoning behind it.
AI-Assisted Document Examination
Supporting specialists in trade document examination
Examining trade documents and reviewing guarantee wording requires expert judgement. AI can help by preparing the ground: extracting, comparing and highlighting what deserves a closer look.
We build assistance tools for trade document examination and bank guarantee analysis. They identify potential discrepancies, compare documents against transaction terms and surface relevant clauses, while leaving the decision with the examiner.
These systems are designed to assist specialists, not replace them. Their output is a set of evidence-backed suggestions that an expert reviews.
Discrepancy detection and advanced document reasoning are evolving capabilities. Their accuracy and production readiness depend on document quality, scope and implementation, and they are designed to operate with expert review.
Discrepancy identification
Potential discrepancies surfaced with supporting evidence for review.
Bank guarantee analysis
Clause identification and comparison against expected wording.
Examination workspace
Side-by-side review of documents, extracted data and findings.
Conversational AI
Enterprise assistants grounded in your own knowledge
Banks hold vast operational knowledge in procedures, product manuals, rulebooks and past cases. We build assistants that let staff ask questions in plain language and receive answers grounded in approved sources.
Our approach uses retrieval-augmented generation (RAG): relevant passages are retrieved from an approved knowledge base and supplied to a language model, which answers using that context and cites its sources. Business rules and access controls determine what each user can see.
This combination of enterprise knowledge, document understanding, business rules and secure integration is what makes an assistant useful in a regulated environment rather than merely impressive in a demo.
Banking knowledge assistants
Question answering over procedures, products and operational guidance.
Retrieval-augmented generation
Answers grounded in retrieved, approved content, with citations.
Natural-language search
Find the right document or clause without knowing the exact keywords.
Context-aware answers
Responses shaped by the user's role, entitlements and current task.
Enterprise LLM integration
Language models integrated with identity, logging and existing systems.
Responsible AI controls
Guardrails, audit logs, feedback loops and human oversight.
Private Enterprise AI
AI that runs where your data is allowed to be
Many financial institutions cannot send sensitive documents to public AI services. We design AI solutions that run on-premise or in private cloud environments under the institution's control.
Private deployment involves choices about models, hardware, serving infrastructure, monitoring and update processes. We help institutions make those choices pragmatically, balancing capability, cost and operational effort.
On-premise deployment
Models and pipelines deployed within the institution's data centre.
Private cloud deployment
Isolated environments with controlled network access.
Model selection & evaluation
Choosing and measuring models against the institution's own tasks.
Operational controls
Logging, monitoring, access control and change management for AI systems.
Exploring AI for documents or knowledge?
A focused proof of concept on your own documents is often the fastest way to learn what is achievable. Tell us what you are considering.