Predictive Modelling
Custom classification, regression, and forecasting models tuned to your business KPIs and risk tolerance.
AI & Machine LearningMachine Learning
Machine learning that survives production
Predictive modelling, feature engineering, MLOps pipelines, and production model serving, engineered for regulated estates and built to clear audit before launch.
0+Models in production
p0 < 80msMedian serving latency
0%Model-risk first-pass clearance
What we build
Machine learning services we ship end-to-end
Pick a single capability or compose them into a full programme. Every engagement is architect-led and runs through the same eval / governance gates.
MLOps Pipelines
Reproducible training, deployment, and rollback pipelines built on your existing CI/CD and identity stack.
Feature Engineering
Feature stores with lineage, point-in-time correctness, and serving parity between training and inference.
Governance & Eval Plane
Evaluation, observability, policy, and audit across every agent, tool, and workflow.
Model Serving
Low-latency online serving and batch inference with hardware-aware optimisation and autoscaling.
Experiment Tracking
Run-level lineage, parameter sweeps, and a single source of truth for what's deployed where.
Drift & Quality Monitoring
Live drift detection, fairness checks, and automated retraining triggers, gates that catch decay before users do.
Engineering depth
Four technical pillars behind every model we ship
Production ML is a systems problem, not a notebook problem. These are the surrounding capabilities that make a model survive contact with real traffic.
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01
Model architecture
Gradient boosting, deep nets, time-series, and ensembles, picked to match data shape and serving budget.
02
Data & feature engineering
Feature stores with point-in-time correctness, parity between training and serving, and full lineage.
03
Training infrastructure
Distributed training, GPU scheduling, and parameter-sweep automation on your cloud account, not ours.
04
Production serving
Low-latency online inference, batch scoring, and feature-store-backed serving for transactional systems.
Where it pays for itself
Machine learning applied across regulated industries
Selected programmes from the last 24 months, anonymised where required, specific where it matters.
Banking
Banking
Streaming features + boosted ensembles. 92% of card-not-present fraud caught at 38ms decision latency.
Insurance
Insurance
Per-claim severity and fraud-risk models with policy-aware feature engineering. £6.1m loss saved year one.
Healthcare
Healthcare
Calibrated risk scores integrated into care-team workflows. Cleared the trust's clinical safety review.
Wealth Management
Wealth Management
Production ML that scores customer suitability against product universe, with full lineage and MiFID-II evidence.
Retail
Retail
Per-SKU forecasts with promo and price elasticity built in. Stockouts down 18% across 280 stores.
Logistics
Logistics
Live shipment ETA with confidence intervals and exception classifiers wired into the WMS event bus.
Our engagement workflow
Seven stages from first call to ongoing support
Every engagement walks the same path, sized to your problem, but with the same verification gates baked in.
Phase 01Discovery
Two-week paid sprint. Architect-led. Output: regulator map, costed roadmap, signed scope.
Phase 02Planning
Pod composition, sequenced milestones, change-control governance, and risk register.
Phase 03Design
Reference architecture, threat model, design system, and acceptance criteria locked.
Phase 04Development
Weekly demos, trunk-based, CI/CD from day one. Code reviewed against spec at every gate.
Phase 05Testing
Unit, integration, e2e, security, performance, and accessibility, automated and gated.
Phase 06Deployment
Blue-green or canary, observability live before launch, rollback rehearsed.
Phase 07Support
Managed services or hypercare hand-off. Defined SLOs, named on-call, monthly reviews.
Success stories
Programmes we have shipped
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
31%
Fintech
Explainable credit scoring for a digital lender
Lifted approval rates 31% at constant loss, every decision reason-coded for the FCA.
PythonLightGBMSHAPAWS SageMaker
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
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
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
44%
Banking
Grounded knowledge copilot for a retail bank contact centre
Cut average handle time 44% across 2,600 agents, with answers grounded in approved policy only.
PythonAzure OpenAIAzure AI SearchKubernetes
Country · UK
89%
Fintech
Autonomous reconciliation for a payments processor
Resolved 89% of reconciliation breaks without a human, every action logged for audit.
PythonTemporalLangGraphPostgresAWS
Country · DE
7 min
Manufacturing
Self-healing incident remediation for a manufacturer's OT platform
Cut median incident remediation from 41 to 7 minutes across a manufacturer's OT platform, with a human approval gate on every action.
GoKubernetesLangGraphPrometheusOT connectors
Country · DE
73%
Healthcare
Agentic procurement triage for an NHS trust
Routed and matched 73% of purchase requests end to end, escalating only genuine exceptions.
PythonLangGraphAzureSQL Server
Country · UK
58%
Government & Public
Complaints triage and root-cause tagging for a government department
Auto-classified 58% of citizen complaints to root cause, feeding the department's reporting directly.
PythonspaCyTransformersPostgreSQL
Country · UK
12x
Wealth Management
Adverse-media screening for a private bank
Screened 12x more names at a lower false-positive rate, each hit evidenced.
PythonTransformersElasticsearchAzure
Country · CH
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
94%
Logistics
Shipping-document extraction for a logistics operator
Achieved 94% straight-through extraction on bills of lading and customs paperwork for a logistics operator.
PythonLayoutLMTesseractAzure
Country · UK
9 weeks
Pharma
AI governance and model-risk framework for a pharma company
Stood up a board-ready AI governance and model-risk framework for a pharma company in 9 weeks, cleared at first internal audit.
GovernanceModel riskGxPEU AI Act
Country · CH
£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
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
Why teams choose BritonOne Technology
Four reasons enterprise buyers come back
We don't compete on lowest day-rate. We compete on shipped outcomes inside environments that have to clear audit.
Senior-only delivery
Every engineer on every engagement is at least senior, typically eight to fifteen years deep in their craft. No bench rotations, no junior pyramid hidden behind a glossy proposal, no bait-and-switch after contract signature. The architect who scoped your engagement is the same person committing code by week three.
Audit-ready by default
FCA, PRA, EBA, BaFin, FINMA, HIPAA, SOC 2 Type II: every framework we work under is treated as a design constraint from day one, not a final-stage gate. Evidence trails, model-risk packs, change-control artefacts, and pen-test reports ship alongside the code, ready for second-line review without a remediation sprint.
Anti-drift delivery discipline
Small pods of three to seven engineers, each with a named delivery lead who owns scope, schedule, and outcomes from kickoff to hand-off, never a faceless team you have to chase for an answer. Weekly demos run against the signed scope, frequent verification gates catch regressions early, and quarterly outcome reviews measure real progress against the original business case rather than a moving target. Together those rituals catch scope drift before it has any chance to compound, so programmes that should take six months don't quietly stretch into eighteen, budgets stay anchored to what was agreed, and every milestone ships with a written, testable definition of done that both sides sign off before we move on.
Long-tail support beyond hand-off
We don't disappear the moment the engagement closes. Managed services, hypercare windows, named on-call rotations, or quarterly health checks: pick the depth that matches your operational risk profile. About seventy percent of clients return for a second programme, usually because the team that shipped the first one is still on the other end of the page.
Client Satisfaction Reviews
Words from the teams we have shipped with.
Anonymous under MNDA. Each quote is from a senior buyer who owned the engagement end to end across the services catalogue.
One Team Replacing Two Vendors
“BritonOne Technology replaced two of our incumbent vendors with one team. Faster sprints, fewer status meetings, more code shipped per week.”
VP EngineeringTier-1 European retail bank
Common pre-engagement questions
Things buyers ask before picking the first service
