Problem 01
Hallucination is the wrong risk vocabulary
Regulators don't ask for 'no hallucination'. They ask for every output to be citation-bound and replayable. Most POCs chase benchmark accuracy and never produce the citation trail.
Solutions · Knowledge & Decisioning
Make 40 years of documents decisionable.
Extract, classify, and route regulated documents, every output citation-bound and replayable, cleared for FCA, PRA, and EU AI Act estates.
Solutions we provide
Six document workloads, made decisionable.
From a single policy wording to a forty-year archive, every workload ships with the same citation-bound, replayable evidence trail. Start with the document that's costing you most.
Why document-intelligence projects fail
Four reasons document-AI POCs never reach production.
The highest-value GenAI surface in regulated firms, and the one with the most consistent failure rate across banks, insurers, and wealth platforms.
Compliance is not a model feature. It's an end-to-end system.
- 9/10
- POCs stall before production in regulated firms
- 14×
- More documents in production than used in typical POC datasets
- 6+
- Months to answer a supervisor's question without replayability
The technology works. The systems around it don't.
- Problem 02
The real corpus is messy and untouched
Forty years of scanned PDFs, handwritten claims margins, policy wordings in fourteen versions. POCs run on cleaned subsets; production meets the real corpus and stalls.
- Problem 03
Domain experts re-review every output
Underwriters re-read every contract anyway; loss-adjusters re-read every wording. The AI becomes overhead, not leverage, and the business case collapses.
- Problem 04
Model decisions cannot be replayed
Six months on, the supervisor asks why the model classified a contract that way. The team can't reconstruct the reasoning, the corpus version, or the model card, and confidence collapses.
What changes once it ships
Four things a regulated firm can take to the board.
Not features: outcomes a head of underwriting, claims, or compliance can defend in the room.
Every output is defensible.
Each extracted fact, classification, and routing decision is bound to a source document, page, and chunk, and replayable on demand. “Why did the model decide this?” has a 60-second answer, not a forensic project.
- Per-output citation to source and page
- Sub-60-second trajectory replay from an audit query
- CP24/2 model cards per release
- Uncited outputs auto-route to human review
The AI becomes leverage, not overhead.
Domain experts stop re-keying and start deciding. 60–70% of extractions auto-route; only medium-risk and exceptions reach a human, and every override sharpens the next quarter's model.
- 60–70% of outputs auto-routed
- Confidence-scored human-in-the-loop
- Experts judge exceptions, not data-entry
- Overrides feed the quarterly retrain
It survives the real corpus.
Forty years of scanned PDFs, handwritten claims margins, and policy wordings in fourteen versions, not a cleaned demo subset. 94% top-1 accuracy on the production corpus, against 78% for a tuned-rules baseline.
- Scanned PDFs, handwriting, 14-version wordings
- OCR confidence routing on low-quality ingest
- 94% top-1 accuracy in production
- Evaluated across clean / messy / edge tiers
It clears, not just builds.
Regulator evidence is engineering output, not a final-stage scramble. Every build ships the EU AI Act Annex IV pack, 2LoD-approved model cards, and an on-prem sovereign fallback for data that can't leave the estate.
- FCA / PRA / EU AI Act-cleared
- Annex IV documentation pack per build
- 2LoD approval built into the timeline
- On-prem sovereign model fallback
Why BritonOne Technology
Why regulated firms choose BritonOne Technology
Not features, but four reasons a head of underwriting, claims, or compliance can defend in the room.
Regulator-fluent by design
FCA, PRA & EU AI Act evidence ships as engineering output, not a final-stage compliance scramble.
Evidence-bound, never a black box
Every output is citation-bound to its source and page, and replayable in under 60 seconds.
Cleared in eleven weeks
A phased path from demo to 2LoD-approved production, de-risked at every gate.
Experts decide, AI does the lifting
60–70% of outputs auto-route; your specialists judge the exceptions, not the data entry.
How we build
From first call to audit-cleared production.
Five stages, each with a bounded output you can hold us to. No black-box sprints, no big-bang launch: working software and the evidence trail, in step.
- Week 1–2
Discovery & architecture
We map the problem, the constraints, and the regulatory surface, then commit to a bounded scope and a costed plan, not an open-ended retainer.
- Before any code
Threat model & design review
Architecture, data flows, and a written threat model, signed off with your security and second-line leads before a line of production code ships.
- Every sprint
Build in vertical slices
Working software each sprint, behind feature flags, with tests and CI gates from commit one. You see progress you can run, not a status deck.
- Continuous
Evidence & hardening
Pen-test, audit trails, runbooks, and the documentation pack a regulator asks for, produced as engineering output, not a final-stage scramble.
- Before ramp
Shadow, clear & hand over
Run in shadow against live traffic, clear second-line and regulator review, then a documented handover, or a bounded retainer if you'd rather we stay.
Success stories
Programmes we have shipped
61%
Legal
Clause-level contract drafting for a commercial law firm
Cut first-draft time on regulated commercial contracts by 61%, each clause traceable to firm precedent.
TypeScriptAWS BedrockAnthropicOpenSearchReact
Country · UK
3.1x
Insurance
Underwriting copilot for a speciality insurer
Drafted first-pass quotes from submission packs 3.1x faster, every clause cited back to the wording it came from.
PythonAzure OpenAILangChainPostgrespgvector
Country · UK
18%
Foodtech
Churn prediction for a foodtech subscription platform
Cut voluntary churn 18% in two quarters with next-best-action scoring for a meal-subscription platform.
PythonXGBoostSnowflakedbtMLflow
Country · UK
22%
Retail
Demand forecasting for a grocery retailer
Cut waste 22% across 900 stores with SKU-level probabilistic forecasts.
PythonPyTorchDatabricksDelta Lake
Country · FR
47%
Insurance
Motor damage assessment from photos for an insurer
Settled 47% of motor claims from images alone, with fraud flags surfaced inline.
PythonPyTorchDetectron2AWS
Country · UK
£14M
Insurance
GenAI opportunity assessment for an insurance group
Mapped a £14M annual benefit pipeline across 40 use cases, sequenced by risk.
StrategyValue modellingRisk assessment
Country · DE
3x
Insurance
Claims platform rebuild for a general insurer
Replaced a 15-year-old claims system, tripling throughput per handler.
C#.NETAzureSQL ServerReact
Country · UK
4.8★
Retail
Loyalty and shopping app for a retailer
Launched a retail loyalty app to a 4.8-star rating and 200k users in 90 days.
SwiftKotlinReact NativeAWS
Country · UK
60%
Retail
Lakehouse build for a grocery retailer
Unified 40 sources into a governed lakehouse, cutting report build time 60% for a grocery retailer.
DatabricksDelta LakeSparkTerraform
Country · UK
45%
Automotive
Cloud warehouse migration for a carmaker
Migrated a legacy warehouse to the cloud, cutting query cost 45% for a carmaker.
SnowflakedbtFivetranTerraform
Country · DE
38%
Healthcare
Claims and billing analytics for a hospital group
Unified claims and billing data, cutting rejected-claim rework 38% for a hospital group.
SnowflakedbtFHIRPower BI
Country · UK
60→1
Insurance
Underwriting analytics dashboards for an insurer
Replaced 60 spreadsheets with one governed underwriting board pack for an insurer.
Power BITabularAzuredbt
Country · UK
3x
Foodtech
Self-service analytics for a foodtech platform
Self-service models tripled analyst throughput at a food-delivery platform.
LookerdbtBigQuery
Country · FR
100%
Insurance
Test automation for an insurance quote platform
Automated quote, coverage, and claims journeys for a US insurer, holding 100% of critical paths green across a fast release train.
SeleniumJavaTestNGJenkins
Country · US
30+ edge cases caught
Fintech
Exploratory and regression testing for a customer-management platform
Exploratory and regression testing across pipelines, analytics, and customer records surfaced more than thirty edge-case defects that scripted suites had missed, hardening a German fintech's CRM before a major rollout.
WebTestRailJiraPostman
Country · DE
p95 latency flat
Fintech
Scalability and endurance testing for an analytics platform
Kept p95 latency flat as audience data and concurrency scaled, proving the analytics dashboards would not slow as customers grew.
k6LocustGrafanaDatadog
Country · US
Who we are
About usBritonOne Technology is a full-cycle engineering company that builds and operates production software for regulated estates. Since 2017, we have shipped programmes that clear audit on the first pass across banking, insurance, wealth, healthcare, and biotech. Our teams pair deep domain knowledge with disciplined engineering, treating compliance, security, and resilience as first-class deliverables. From architecture through to live operations, we stay accountable for the systems we build, measuring success by uptime, audit outcomes, and defensible business results.
60+Senior engineers across UK and EU
Why choose us
Engineer experience, average9+ yrs
Specialist replacement window48h
Code and IP ownership, day one100%
Surprise invoicesZero
What document-intelligence clients say
Words from the regulated teams we shipped with.
Anonymised under MNDA, verifiable on reference call. Each quote is from a senior owner who carried the engagement through second-line review.
Cleared on first submission
“We had run three internal POCs on policy-extraction over two years. They all stalled at second-line risk review. BritonOne Technology built the citation-bound pipeline in eleven weeks; the FCA review accepted it on first submission. Twelve months later, 64% of policy extractions auto-route; the underwriting team is doing the judgement calls, not the data entry.”
Director of UnderwritingTop-5 UK motor insurer
Common questions about document intelligence
What heads of underwriting, claims, and compliance ask first.
