We build production AI that runs on your infrastructure, not ours.
Production AI systems deployed inside your own cloud or on-prem environment, for organizations that cannot send their data to public AI services. Financial services is where we have the deepest track record; we work the same way across other regulated and data-sensitive industries.
Built for regulated, data-sensitive industries
What we build
Seven services, one team.
Strategy, private deployment, embedded engineering, automation and modeling, delivered by the people who write the code. Hold a tile to see how it actually works.
All serviceseach layer only as good as the one beneath it
AI Strategy & Readiness Audit
A structured, vendor-neutral read on what's worth building, what the data will support, and what to skip.
Private AI Deployment
LLMs and agents running inside your VPC or on-prem, so regulated financial data never crosses a third-party API boundary.
Forward-Deployed AI Engineers
Senior engineers embedded in your team, building and operating production AI systems from inside your stack.
AI Agents & Workflow Automation
Agentic workflows for back-office finance: sign-off where mistakes are expensive, straight-through processing where they aren't.
Financial Data Engineering
Pipelines, warehouses and entity resolution that make your financial data point-in-time correct and actually queryable.
LLM Integration & RAG
Retrieval-augmented generation over your internal documents, filings and compliance corpora, with sources attached to every answer.
Model Fine-Tuning
Domain-tuned models on your proprietary financial data and terminology, evaluated against a baseline before they ship.
How we engage
Four ways to work with us.
From a fully embedded team to a standing advisory retainer. Start with the model that matches how much you want to own, and change later if it stops fitting.
What that looks like in practice
Numbers we can defend in a client review.
To a first production workflow, not a deck
Delivery and docs in English, German, Arabic
Runs in the client's own cloud or on-prem
Who we work with
Built for regulated, data-sensitive industries.
Financial services and industrial operations share the same constraint: the data is sensitive, the stakes are real, and a wrong answer is expensive.
Banking
Document intelligence, alert triage, and reporting automation for retail and commercial banks.
Asset & Wealth Management
Client reporting, research summarisation, and compliance monitoring for asset managers and wealth advisors.
Insurance
Claims triage, underwriting intelligence, and Solvency II-ready reporting for insurers.
Payments & Fintech
Onboarding, transaction monitoring, and dispute automation for payment processors and fintechs.
Accounting & Audit
Close automation, audit evidence extraction, and anomaly detection for accounting and audit teams.
Real Estate Finance
Underwriting, covenant monitoring, and lease abstraction for real estate lenders and fund managers.
Construction & Industrial
Tender intelligence, claims analysis, and site compliance for contractors and industrial firms.
Our approach
Data before models.
The same sequence every time: understand the decision, fix the data, prove it on a metric, then put it into the workflow.
See how we work- 01
Frame the decision
Start from the decision the system is meant to support and the work around it. Before any code, we agree on the one number that will tell us whether it is working.
- 02
Ground the data
Fix the data before the model: point-in-time correctness, reconciliation and lineage, so the system is not confidently wrong on its first day.
- 03
Prototype and evaluate
Build on real data with evaluation from the start. If it cannot beat the current process on the agreed metric, far better to learn that in week three than month six.
- 04
Integrate
Put it where the work happens. Sign-off where mistakes are expensive, monitoring for drift and cost, and an audit trail that survives a hard question.
- 05
Operate and hand over
Leave you able to run it without us: documentation, the eval suite, runbooks, and time spent with the people who will own it.
Inside the work
How the work actually happens
We sit inside client teams: pairing with your engineers, joining your compliance reviews, shipping into your infrastructure.
Technology
Technologies we work with.
No single-vendor lock-in: we pick the model, store and platform that fit your constraints.
Models & orchestration
Frontier and open-weight models, wired together with the frameworks that make agents and retrieval reliable.
Data & retrieval
The pipelines and stores that get financial data into a shape a model can search and reason over.
Platform & delivery
Infrastructure that runs inside your perimeter, on the cloud you already use or on-prem GPU.
Quality & observability
Evaluation and monitoring that catches a regression before a client or an examiner does.
From the field guide
Notes from the build.
Writing on architecture, data, governance and the parts that go wrong once AI hits production.
AI architecture for finance
Retrieval, agents and evaluation, designed around the constraints finance imposes.
The financial data layer
Point-in-time correctness, lineage and entity resolution, the layer most projects skip and then regret.
AI governance & compliance in finance
The EU AI Act, model risk management and supervisory expectations, turned into engineering you can ship.
Feedback
What working with us feels like
Representative feedback from engagements, anonymised. References available on request.
Their engineers sat with our data team from week one. No black box, no handoff document nobody reads.
Head of Data Platforms, retail bank
They scoped honestly. When something wasn’t worth automating, they said so instead of billing more hours for it.
COO, payments provider
Every model decision came with documentation our validation team could actually use, not a slide deck.
Model Risk Lead, insurer
The deployment never left our infrastructure. That was non-negotiable for us, and they built around it from day one.
CTO, wealth manager
We could put their evidence pack in front of our regulator without rewriting a single page.
Finance Transformation Director
They embedded with our fraud team instead of dropping a model on us and leaving. We understand what it does.
Head of Fraud Analytics
No data left our tenancy at any point in the engagement. They proved that in writing before we signed.
Group Data Protection Officer
The engineers who scoped the project were the same ones writing the code six months later.
VP Engineering, fintech
Tell us what you're trying to build.
A straight conversation about your data, your workflow, and whether AI is the right tool for the job. If it's not, we'll say so.