AI in Finance
Most finance AI stalls at a demo that stops short of a core system. We build the working version. Document intelligence, generative copilots, forecasting and anomaly detection, and conversational banking, each grounded in your data, cited back to a source, and wrapped in model governance an examiner can read. Senior engineers build it, harden it, and can stay to run it.
How we help
The point is fewer manual hours and earlier warnings, with a source behind every output.
Reclaim the hours lost to rekeying
Analysts spend hours copying figures out of statements and filings by hand. We put document intelligence between the source files and your ledger, so extracted fields arrive with a confidence score and a link back to the page. Reviewers check the flagged cases instead of retyping every line.
Answer questions without the hunt
Support and operations teams answer the same policy questions all day from PDFs no one wants to read. A grounded copilot retrieves the passage, cites it, and drafts the reply. Staff keep the final say. Handle time drops because the search step is gone, and the citation shows where each answer came from.
See the problem before it lands
Risk and finance leads find out about a bad transaction or a forecast miss after it has already landed. Anomaly detection and forecasting models score activity as it flows in and raise the outliers early. Your team sees a ranked queue with reasons attached, so the review starts with the cases that matter.
What we deliver
Six areas where we put models into finance work, each with a source and a human path.
Document AI & intelligent processing
Statements, KYC packs, loan files, and invoices arrive as scans and PDFs that staff rekey by hand. We build extraction that reads layout, pulls the fields, and attaches a confidence score plus the source coordinates. Low-confidence items route to a reviewer, so the audit trail records who confirmed what.
Generative-AI copilots
Employees dig through wikis, policy PDFs, and past tickets to answer one question. We build copilots that retrieve the relevant passages first, then draft an answer with citations back to the source. The model stays inside your content, and a person approves anything that leaves the building. Guardrails control hallucination risk.
Forecasting & anomaly detection
Spreadsheet forecasts break when volume shifts, and fraud or errors surface only after reconciliation. We build models that learn the patterns in your transaction and cash-flow history, then flag readings that drift from them. Each alert carries the features that drove it, so an analyst can judge the call instead of guessing.
Conversational banking
Call centers queue up for balance checks, card blocks, and payment questions that a bot could handle. We build assistants that read intent, pull account context through your APIs, and complete the routine request. Anything sensitive or ambiguous hands off to a human with the transcript, so the customer keeps the context instead of starting over.
Model risk & governance
A model in production with no paper trail is a finding waiting for the examiner. We set up versioning, validation records, bias testing, and monitoring for drift, so each deployed model has an owner and a documented history. When performance slips, the alert fires and the rollback path is already defined.
RAG over financial data
General models guess when you ask about your own contracts, filings, or policies, and a wrong number here is expensive. Retrieval-augmented generation grounds each answer in your documents and returns the passage it used. Access controls travel with the query, so the system answers only from records the user may see.
Our technology radar
Where we stand today. The Hold ring is the one that matters: these are positions we argue against, including when a client asks for them.
The default unless there is a reason not to.
- Retrieval over fine-tuning for company knowledge
- Schema-validated extraction
- Confidence routing to a human
- Evaluation sets before deployment, not after
- A governance record for every model in production
We use these on new work and watch them closely.
- Open-weight models on your own infrastructure
- Agentic workflows with explicit tool boundaries
- Structured output enforced at the API layer
We argue against these.
- Model output as the decision in a regulated flow
- Fine-tuning on customer data without a governance record
- Assistants over an unversioned knowledge base
- Presenting a pilot accuracy figure as a production number, which is the most common way these projects lose credibility
AI that clears audit and compliance review
We build and run the model, so the grounding, citations, and monitoring hold up when a regulator asks how a decision was made.
How we work
Discover
Systems, constraints, and the regulation you operate under get mapped before implementation starts, so the design accounts for what already runs and for what examiners will ask about.
Architect
A design that fits your stack. Integration-first, self-hostable, and built to change as rules, regulation, and volume do.
Build
Senior engineers ship in tight increments, each tested and reviewed as it goes, so the system is reviewable at every step instead of only at the end.
Harden
Security, compliance, and load-testing run inside the build, so controls, audit trails, and peak-volume behavior are proven before launch.
Run
The handover includes clean, documented systems, with the option to keep the same engineers operating them once they are live.
Why Oxagile
We ship to production
Plenty of vendors stop at a demo that works once on clean data. We build the pipeline, the monitoring, and the fallback paths that keep a model working after launch. The same senior engineers who write the code stay through hardening and the first weeks of live traffic.
Grounded, cited output
A finance answer with no source is a liability. Our systems retrieve from your own documents and return the passage behind each response, so a reviewer can trace the claim. Grounded retrieval and citations control hallucination risk, and a human stays on the path for anything that carries money or compliance weight.
Governance built in
Examiners ask who owns a model, how it was validated, and what happens when it drifts. We answer those questions in the build with versioning, validation records, bias testing, and drift monitoring. The controls and audit trail exist before launch, so the compliance conversation starts with documents already in hand.
You own what we build
The handover includes source code, documentation, and systems you can self-host on your own infrastructure. No opaque system, and no lock-in to a platform you cannot inspect. You decide if you run it yourself or keep the same engineers operating it once it is live.
What our architects hand you
Delivered from Presales and Discovery, yours to keep whatever you decide next.
Book an architecture sessionSystem Design
Target architecture against your systems, with failure domains and the order things get built.
API contracts
Every integration point written before an implementation exists.
Compliance scope assessment
Which systems fall in scope under each option, and what the design removes.
Cloud cost model
What it costs to run at your volume, by component.
Security review
Threat model for the paths that move money or hold personal data.
Migration and cutover plan
Parallel run, reconciliation, gradual shift, and a rollback point at each step.
Questions
The questions finance and risk teams raise before they commit to a build.
Do you connect to real systems or just build a demo?
We build for production. That means integration with your core, data pipelines that handle the messy inputs, monitoring, and fallback behavior when a model is unsure. A demo can follow to prove the idea, but the goal is a system your team runs against live data, with the controls and documentation an audit needs.
How do you keep the models from making things up?
We ground answers in your own documents through retrieval, and return the passage each response drew on. That gives a reviewer a source to check. For anything touching money or compliance, a person stays in the loop before the output is acted on. Together, grounding, citations, and that human step control hallucination risk.
Can this run on our own infrastructure?
Yes. We design integration-first and self-hostable, so the system can sit inside your cloud or data center behind your own controls. That matters when data residency or regulation rules out sending records to an outside service. Where you prefer a hosted model provider, we scope that with your security team rather than assume it.
What about model risk and examiner questions?
We treat governance as part of the build. Each model gets an owner, a validation record, bias testing, and drift monitoring, with versioning so you can see what changed and when. The audit trail records who approved a deployment and why. When a metric slips past its threshold, the alert and the rollback path are ready.
How long before we see something working?
It depends on data access and integration, so we scope that in discovery before quoting a date. A grounded copilot or a document extraction flow on a contained dataset can reach a working state in weeks. Wider rollout across systems takes longer, because that is where governance, load testing, and sign-off carry the weight.
AI in Finance, broken down
Each of these goes deeper than this page does: the systems we build, how they are put together, and what we hand over.
What we own
ProductAppraisal Intelligence
AI asset appraisal & valuation
Asset appraisal that reads the file instead of asking an analyst to. It extracts assets from PDFs, spreadsheets and photos, retrieves comparable historical sales, and produces a three-tier valuation with the reasoning cited back to its source. What used to be a week of desk research arrives as a client-ready report.
ProductVoiager
AI voice agents
Voice agents that handle the finance-ops calls nobody should be queueing for. They read intent, pull account context through your APIs, and resolve routine requests end of call. Anything outside policy escalates to a person with the transcript attached, so the customer never starts over.
Services in AI in Finance
What we’ve shipped
Case studies for this competency are in progress.
Get AI into production,
not just a demo.
Have a workflow begging for AI, whether documents, copilots or forecasting? Tell us the use case and we'll take it to production, governed for risk.