Models that survive the regulator, the quarterly review, and the 3am page.
Feature stores, evaluation harnesses, model risk records, deployment pipelines, drift monitoring — the operational backbone that turns ML projects into ML platforms.
Capabilities under one accountable team.
Feature store & lineage
Centralised feature registry with point-in-time correctness, lineage to source-of-record, and approved-feature governance.
Training & evaluation
Reproducible training pipelines, hyper-parameter sweeps, evaluation against versioned ground-truth datasets, fairness and bias audits.
Deployment & serving
Canary releases, shadow mode, A/B testing, model gateway with policy routing, and automatic rollback on performance regression.
Monitoring & MRM
Drift detection, performance monitoring, model risk records aligned with SR 11-7 / equivalents, and regulator-facing explainability dashboards.
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.
Maturity assessment
Score the current ML stack against a 12-dimension maturity model. Quick wins identified.
Foundation
Feature store, registry, evaluation harness, deployment pipeline — opinionated but extensible.
First model
Migrate one model to the platform end-to-end with full MRM evidence pack.
Scale
Onboard the next 5 models, cap the inference bill with FinOps, retire shadow ops.
Optimise
Quarterly drift reviews, fairness re-audits, GPU utilisation reviews.
Tier-1 GCC bank moves from 4 ML projects in shadow mode to 18 models in audited production in 12 months.
More programmes we have shipped.
AI underwriting
9 days → 14 minGCC sovereign bank deploys AI underwriting in 11 months
Read case studyLoan origination
14d → 3hNational bank ships modern Loan Origination System on Mendix in 11 months
Read case studyOpen banking
240+ TPPsLeading African bank ships open-banking rails across 7 markets
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
Build or buy? +
We build on top of MLflow, SageMaker, Vertex, Databricks, Azure ML, or your existing stack. We don’t replace; we govern and operationalise.
How is this different from generic DevOps? +
ML has data lineage, model lineage, drift, fairness, and regulator-facing explainability that generic DevOps doesn’t address. We bring the patterns that satisfy all of these.
Can you support open-source / self-hosted LLMs? +
Yes — we operate self-hosted Llama, Mistral, and Falcon at scale, with the same MLOps discipline as proprietary providers.
How do you handle drift? +
Drift on inputs (covariate), outputs (label), and performance — monitored continuously with thresholds that trigger evaluation, retraining, or kill-switch.
Model risk management? +
Aligned with SR 11-7, BCBS 239, EU AI Act, and equivalent local frameworks. Every model has a model card, evaluation evidence, and an owner.
Pricing? +
Per-model annual fee for managed estates, or T&M with capped fees for build-only engagements. FinOps reports for inference cost included.
Book a mlops briefing.
A senior partner will respond within one business day with a tailored agenda.