Problem 01
Model outputs do not map to actions
Churn risk score: 0.74, and the customer team's reaction is 'so what?' A number with no intervention attached. With nothing to act on, adoption stalls and the model gathers dust.
Solutions · Customer & Risk Intelligence
Prediction that clears Consumer Duty.
Churn, next-best-action, lifetime value, and credit risk: predicted in real time, explainable per decision, and Consumer Duty-aligned.
Solutions we provide
Six prediction workloads, made actionable.
Every workload ships the same citation-bound reason code and continuous drift monitoring. Start with the decision costing you most.
Why customer-intelligence models stall
Four patterns that erode trust in the model.
Customer-prediction models live or die on whether the team acts on them. Most firms ship two or three that nobody uses daily.
A score is not an outcome. Adoption is the system around the model, not the model.
- 2–3
- Prediction models shipped per firm, almost none used daily by the customer team
- 18%
- Typical accuracy decay within six months when drift goes unmonitored
- 10–20%
- Customer-team adoption of a dashboard-only model with no action mapping
We shipped two churn models in three years. The retention team used neither.
- Problem 02
Customer-facing team cannot explain the score
The customer asks why they were offered a particular product, and the retention agent can't answer. The FCA Consumer Duty review flags the unexplainable journey, and trust in the model erodes.
- Problem 03
Model drift goes unmanaged
The model performs well at launch, then quietly decays 18% over six months. Nobody notices, because nothing is watching for feature or score drift, and the bad decisions are already booked.
- Problem 04
No model-risk evidence
PRA SS1/23 review asks for the model card and the team produces a slide deck instead. 2LoD blocks the next iteration, and the year-two roadmap collapses before it starts.
What changes once it ships
Four things a customer-facing firm can take to the board.
Not features: outcomes a chief data officer, head of customer, or compliance lead can defend in the room.
Every prediction is explainable.
Each score, recommendation, and decline is bound to feature attributions and a customer-facing reason code, and replayable on demand. “Why was this customer offered that?” has an answer the agent can say out loud.
- Per-decision feature attribution
- Customer-facing reason codes
- Foreseeable-harm review on sensitive decisions
- Any customer journey replayable for the FCA
Scores become actions, not numbers.
The action engine maps every score to a concrete intervention (retention offer, product, escalation) surfaced where the agent already works. The team stops asking “so what?” and starts acting.
- Score-to-intervention mapping built in
- Recommendations inline in the CRM
- Suppression rules and experiment framework
- 94% of interactions use the recommendation
It holds on the real customer estate.
A versioned, point-in-time-correct feature store assembled from product, transaction, support, and behaviour signals, not a one-off extract. Performance is watched continuously, so the model the team trusts on day 300 is still the model that was approved.
- Customer-360 from product, transaction & support signals
- Versioned, point-in-time-correct features
- Feature, label & performance drift detection
- Drift-triggered retrain outside the quarterly cadence
It clears Consumer Duty, not just builds.
Regulator evidence is engineering output, not a final-stage scramble. Every release ships a PRA SS1/23 model card, a traceability matrix, and a 2LoD re-approval workflow before any model reaches production.
- PRA SS1/23 model card per release
- Consumer Duty foreseeable-harm mapping
- Per-decision audit log + traceability matrix
- 2LoD re-approval gating every promotion
Why BritonOne Technology
Why regulated firms choose BritonOne Technology
Not features: four reasons a chief data officer or head of customer can defend in the room.
Consumer Duty-fluent by design
Consumer Duty & SS1/23 evidence ships as engineering output, not a late scramble.
Explainable, never a black box
Every decision carries a reason code, replayable on demand.
Live in fourteen weeks
A phased path from data audit to action-integrated production.
Adopted, not shelved
Scores arrive as actions in the workflow: 94% adoption, not a dead dashboard.
How we build
From data audit to a model teams use.
Five stages, each with a bounded output you can hold us to: working models and the model-risk evidence delivered in step, never a big-bang launch.
- Week 1–3
Discovery & evidence design
We audit the customer-data estate, design the feature store, and get the SS1/23 model-card template signed off by second-line risk, before a model is trained.
- Week 4–6
Feature store & customer-360
A versioned, point-in-time-correct feature store built from product, transaction, support, and behaviour signals, the source every model and audit reads from.
- Week 7–10
Model & action engine
First model trained on a hold-out set, per-decision attribution wired in, and every score mapped to an intervention the customer team can act on.
- Week 11–14
Integrate, clear & pilot
Integration into the CRM, 2LoD approval, FCA notification, then a live pilot cohort, proven in the agent's real workflow, not a demo.
- After go-live
Scale & extend
Phased rollout and further models (churn, next-best-action, lifetime value, credit-risk) under continuous drift monitoring and quarterly retrain governance.
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
22%
Retail
Demand forecasting for a grocery retailer
Cut waste 22% across 900 stores with SKU-level probabilistic forecasts.
PythonPyTorchDatabricksDelta Lake
Country · FR
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
3x
Foodtech
Self-service analytics for a foodtech platform
Self-service models tripled analyst throughput at a food-delivery platform.
LookerdbtBigQuery
Country · FR
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
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
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
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
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
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 customer-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.
The team finally uses it
“We had shipped two churn models in three years. Neither was used by the retention team. The BritonOne Technology build packaged the score with a recommended retention offer and a reason code the agent could speak to the customer. Adoption hit 94% in three months. Retention lift on the at-risk cohort: 11 percentage points.”
Head of CustomerTier-2 UK retail bank
Common questions about customer intelligence
What chief data officers and heads of customer ask before commissioning a rebuild.
