Models that hold upunder real load,not just in the notebook.

The hard part of machine learning was never the model. It is everything around it: the labels nobody owns, the drift nobody notices, the eval set that quietly stopped representing production, and the fallback path for the day the model is wrong. We build that scaffolding first, because it is what decides whether the thing survives contact with real users.

The work itself.

1. 01

   Predictive analytics

   Demand forecasting, churn and pricing models, tied to a decision someone actually makes rather than to a leaderboard metric.

2. 02

   Natural language processing

   Document understanding, classification, extraction and conversational systems over your own corpus.

3. 03

   Computer vision

   Classification, detection, optical character recognition and visual inspection, including the edge deployment cases.

4. 04

   Recommendation systems

   Personalisation that survives cold start and does not collapse into recommending the same ten things.

5. 05

   Evaluation and guardrails

   Task-specific eval sets, regression suites, human review loops and explicit fallbacks for low-confidence output.

6. 06

   MLOps

   Training pipelines, model registries, drift detection and automated retraining with a rollback that has been tested.

Four phases, and whatyou hold at the end of each.

1. 01

   Framing

   We start from the decision, not the dataset. What action changes, who takes it, and what it is worth when the model is right.

   You getSuccess metric, baseline, honest feasibility read

2. 02

   Baseline

   The simplest thing that could work, shipped behind a flag. Often a heuristic. It sets the bar the model has to beat.

   You getDeployed baseline, evaluation harness

3. 03

   Modelling

   Iteration against a held-out set that reflects production, with error analysis driving what we try next.

   You getCandidate model, error analysis, eval report

4. 04

   Production

   Serving, monitoring, drift alerts and a retraining path, plus the human review loop for cases the model should not decide alone.

   You getLive system, monitoring, retraining pipeline

What we build on.

• Claude / GPT / Gemini
• PyTorch / TensorFlow
• scikit-learn / XGBoost
• LangChain / LlamaIndex
• MLflow / Kubeflow
• SMAWS SageMaker
• Azure ML / Vertex AI
• Hugging Face
• Weights & Biases
• Ray

Shipped, and still running.

• MedlineSCOUT — Agentic AI for Accounts PayableAutonomously resolves shipping discrepancies across SAP, AS400, MPower and more — closing a 6,000-ticket backlog while absorbing 283 new tickets a day.Read case study
• Clear CompanyAI Automation & Customer 360°Provisioning Automation + Customer Cortex — customers live in days instead of weeks with real-time health, churn and upsell intelligence.Read case study

What people ask first.