AI that ships. Data that governs itself.
From governed data foundations to production-grade GenAI under audit-ready controls. We build AI workloads that survive the regulator, the quarterly review, and the 3am page.
Capabilities under one accountable team.
Governed data foundations
Lakehouse architecture with native lineage, semantic layer, and policy-as-code. Lineage at the column, not the table.
GenAI for the regulated enterprise
Retrieval-augmented platforms with content firewalls, evaluation harnesses, and red-team programmes baked in.
AI underwriting & decisioning
Feature stores, model risk management, and regulator-facing explainability — the way central banks accept it.
AI agents at scale
Multi-agent orchestration on RapidHub — observability, guardrails, and human-in-the-loop as defaults, not afterthoughts.
Outcomes you can hold us to — by horizon.
Foundations
Outcome tree, baseline metrics, and a working pilot in production by day 90 — defensible with finance, signed off by risk.
Scale
Squad expansion across the next 2–3 value pools. Live-parallel cutovers. Capability uplift inside the client team.
Run & optimise
Managed run with named SLOs, quarterly value reviews, and a continuous-improvement budget reserved for innovation, not toil.
Five steps. One accountable team.
Discover
Use-case ranking by ROI × feasibility × risk. Kill the 60% that don’t earn their keep.
Foundation
Data fabric, governance, evaluation harness — before the first model.
Pilot
One use-case, end-to-end, with named business owner and KPI commitment.
Productise
MLOps, observability, model risk management. Audit-ready by default.
Scale
Reuse patterns, accelerate velocity, FinOps the GPU bill.
Tier-1 sovereign bank deploys AI underwriting in 11 months — USD 38M annualised.
More programmes we have shipped.
AI underwriting
9 days → 14 minGCC sovereign bank deploys AI underwriting in 11 months
Read case studyEHR data fabric
7d → 6 minLeading national provider unifies 9 EHR systems into one data fabric
Read case studyPredictive grid AI
−32% outagesLeading distribution utility deploys predictive grid AI — outages down 32%
Read case studyThree commercial models. One outcome standard.
We avoid open-ended retainers. Every model names its outcome and its measurement window in the contract.
Fixed-price diagnostic
2–4 week engagement. Outcome tree, baseline metrics, prioritised value pools, and a board-ready 18-month roadmap. Stop-go decision in week 4.
Outcome-linked pilot
8–12 week engagement to ship one value pool, end-to-end, with a measurable KPI commitment. Joint squads with the client team. Live-parallel before cutover.
Programme + managed run
Multi-quarter scale-out with managed services on top. Quarterly value reviews. SLO-tied annual incentive. Capability transfer by design.
Frequently asked questions
Do you build on Azure / OpenAI / Anthropic / Bedrock? +
All of the above, plus on-prem. We are model- and cloud-agnostic; we choose for your data residency, latency, and risk envelope.
How do you manage model risk? +
We follow SR 11-7 and equivalent local frameworks. Every production model has a model risk record, an owner, an evaluation harness, and a kill switch.
Can the model run on-prem for sovereignty? +
Yes. We have shipped sovereign LLM platforms in three jurisdictions. Reference architectures available under NDA.
What about data leakage? +
Content firewalls, prompt-injection defence, output filtering, and tenant-isolated retrieval. Audited by independent red teams quarterly.
How do you handle hallucinations? +
Retrieval-augmented architectures with citation enforcement, evaluation harnesses on representative ground-truth, and confidence-calibrated abstention.
Can you embed AI in our existing stack? +
Yes — we connect to ServiceNow, Salesforce, SAP, Workday, and most modern ESBs. We avoid lift-and-shift; we add value at the workflow layer.
Book a ai & data platforms briefing.
A senior partner will respond within one business day with a tailored agenda.