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Data, Analytics & AI
AI/ML Solutions
Custom models from training to production.
End-to-end ML systems — feature platforms, training pipelines, MLOps, and monitored deployments. Engineered for the long arc, not the next demo.
The case
A working model
is the easy part.
Most enterprises can train a model. Few can keep one alive. The system around the model — the feature platform, the registry, the canary, the drift detectors, the rollback — is what separates a notebook from a production capability.
We engineer the system. Models are first-class artefacts on a hardened spine: versioned, observable, governed, and rollback-safe. The model is the smallest part of the work.
What we build
The full ML stack —
not just the notebook.
Feature platform
A governed feature store with online/offline parity, lineage, and reuse across teams — the unsung hero of every working ML org.
Training & experimentation
Reproducible training pipelines with experiment tracking, hyperparameter search, and eval suites that gate every promotion.
MLOps & deployment
Model registry, canary deploys, A/B-ready serving, drift detection, and rollback — engineered into the platform, not bolted on.
Computer vision
Custom vision models for quality, OCR, defect, identity, and inspection — including edge deployments where latency demands it.
NLP & document AI
Classification, extraction, search, and document understanding — pre-trained foundations with enterprise-specific fine-tuning.
Edge & embedded ML
On-device, on-edge, and embedded inference for industrial, retail, and field deployments where the cloud isn't available.
How we engage
Five phases
from experiment to operations.
Most engagements move from a 4–8 week experimentation phase into a multi-month productionization, then into a long-tail operate model with observability and continuous improvement.
Frame
Weeks 1–2
Decision-first scoping. We define the workflow, the KPI, and the eval before any modeling starts.
Experiment
Weeks 3–8
Rapid iteration on candidate approaches with disciplined eval — and an honest go/no-go at the end.
Productionize
Weeks 9–18
Feature platform, training pipeline, registry, serving, monitoring, runbooks. The unsexy work that makes the model durable.
Cutover
Weeks 18–22
Canary, A/B, gradual rollout — with rollback paths and operator training.
Operate
Ongoing
Drift surveillance, retraining cadence, eval-as-code, and continuous improvement on the same backlog as the rest of the product.
Stacks we work with
Frameworks change.
Production discipline doesn't.
Most engagements span 3–4 of the categories below. The ones that show up in every engagement are feature, registry, and monitoring — the components most enterprises buy last and wish they'd bought first.
Frameworks
What we train models in. Choice driven by team familiarity, the deployment target, and the maturity of the model class — not a religious preference.
Foundations
Pre-trained foundations as starting points. We work multi-vendor by default and lean toward open weights when sovereignty, latency, or unit economics demand it.
Feature & registry
The most underrated parts of an ML platform. Feature reuse across teams and disciplined model versioning are what separate a notebook from a capability.
Pipelines
How experiments become reproducible runs. Choice tracks the cloud the team is already on more often than not — we resist re-platforming for tooling fashion alone.
Serving
Where the model meets the request. Picked against latency budget, batch vs. realtime, and how strict the rollback path needs to be.
Monitoring
Drift, performance, and behaviour in production. The component most enterprises buy last and need first — we engineer it in alongside training.
Outcomes we engineer for
What a hardened
ML platform pays back.
5–10×
Iteration velocity
Increase in model iteration cycles per quarter once the platform is in place.
>99.4%
Production precision
Typical defect-vision precision in production after fine-tuning and canary calibration.
Zero
Manual rollbacks
When canary, registry, and drift detection are wired together — rollbacks become automatic.
Days
From experiment to A/B
Median time from model passing eval to live A/B in front of users on a hardened spine.
Where this applies
Wherever a model
needs to live in production.
- Manufacturing
- Healthcare Providers
- Healthcare Payers
- Pharma & Life Sciences
- Banking & Capital Markets
- Insurance & Reinsurance
- Retail & Luxury
- Logistics & Mobility
- Energy & Utilities
- Telecom & Media
- Public Sector
- Higher Education
- B2B SaaS
- Defense & Aerospace
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Strategy & Consulting
Capability audits, target-state architectures, and a sequenced investment thesis tied to operating KPIs.
Data Lakehouse & Warehouse
Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.
Advanced AI & Analytics
Forecasting, segmentation, and anomaly detection wired into operational workflows.
Start the conversation
From a notebook
to a model that lasts a decade.
We'll start with the decision the model supports — and engineer everything else around it.