Analytics That Moves the Metric - Not Just the Dashboard.
Refactor monoliths, retire mainframes, and migrate to cloud-native architectures using AI-led code analysis, test-first generation, and SLSA-attested production cutover.
Why Most Analytics Doesn’t Move Metrics
It dies in PowerPoint. Models get built, dashboards get demoed, and nothing gets embedded in the workflow that controls the metric. Our analytics practice is built backwards from “what would the operator do differently if the model existed?”
Use-Case Catalogue
Customer
1. Customer-360 across product, marketing, service, and finance domains. 2. Churn and retention modelling with intervention design. 3. Propensity-to-buy, next-best-action, lifetime-value.
1. Demand sensing and statistical / ML forecasting (retail, manufacturing, supply chain). 2. Predictive maintenance and failure-mode classification. 3. Capacity, workforce, and inventory optimisation.
Risk & Finance
1. Fraud detection (rules + ML hybrid) with explainability. 2. AML alert triage and case prioritisation. 3. Working-capital, DSO, DPO, and cash-flow analytics.
Baselines benchmarked against incumbent vendor quotes for equivalent scope, with independent advisory validation on engagements over $5M. Total programme TCO includes build, dual-running, training, change management, and 24 month run rate operations full methodology available on request.
ML Ops - The Boring Stuff That Keeps Models Alive
- Feature store. Reusable features across models – no copy-paste.
- Drift detection. Statistical and concept drift monitored per feature and per output.
- Automated retraining. Triggered by drift thresholds, with human-approval gates.
- Eval & explainability. SHAP / feature importance reports per prediction, every release.
- Shadow & A/B. New models compared to incumbent on real traffic before cutover.
Will Claude write broken code at scale?” is the right question. Our answer is a four-layer correctness model not human review alone.
- Spec-driven inputs. Refactor prompts are built from your architecture docs, ADRs, and style guide not from open ended language.
- Test-first generation. Behavioral tests are generated against the legacy system first, run green, then become the contract the new code must satisfy.
- Deterministic verification gates. Static analysis, type-checks, dependency scans, and the regression suite gate every merge same gate as human-written code.
- Eval-driven CI. A code quality eval suite runs in CI; regressions in correctness, security, or hallucination rate block merge.
Human review is the final layer not the only one. The result: AI-suggested code carries the same provenance, attestation, and quality bar as code written by hand.
Every modernization project lives or dies at the cutover. We engineer for safety from day one.
Modelling
Python (scikit-learn, XGBoost, PyTorch), Databricks ML, Azure ML, Vertex AI, SageMaker
Serving
Databricks Model Serving, Kubernetes + KServe, native cloud endpoints
Tracking
MLflow, Weights & Biases
Feature store
Databricks Feature Store, Feast
Manufacturing
Predictive maintenance, yield optimisation, quality vision
BFSI
Fraud, AML, credit, customer-360, attrition
Healthcare
Population-health, no-show prediction, claims propensity
Retail
Demand, assortment, markdown, customer-360, store-ops
Supply Chain
Demand sensing, network optimisation, supplier risk
FAQ
Frequently Asked Questions
How do we know the model is actually working in production?
Every model is paired with an eval harness against a golden dataset, plus a live KPI uplift dashboard. If the metric does not move, the model does not stay.
What if our data is messy?
Most enterprise data is. We build a data readiness assessment in week two and fix what we need to in parallel with the model build. We do not push the analytics project behind a multi-quarter data quality programme.
Do you do generative AI as part of analytics?
Yes, when it fits. Many analytics use-cases now combine ML (for prediction) with Gen AI (for explanation and operator UX). See our Gen AI Services hub for deeper detail.
Ready to Build Analytics That Earns Its Keep?
Book a 60-minute analytics workshop. We will shape one priority use-case end-to-end and walk away with a 10-week delivery plan and a measurable KPI commitment.