Custom ML Models
Forecasting, churn prediction, recommendation, fraud detection, classification, computer vision — built with the right algorithm for the job (XGBoost, LightGBM, deep learning, time series).
AI & Machine Learning
AI & ML built for production business value — not demos
Custom ML models, MLOps, LLM applications, and RAG systems shipped to production with monitoring, retraining, and evaluation harnesses. Every model ties to a measurable business KPI agreed at kickoff.
The 80% problem
Industry surveys consistently show that 70–80% of enterprise ML projects never make it to production — and of those that do, most are quietly retired within 18 months. The reason isn't model accuracy; it's everything around the model. No data pipeline. No retraining loop. No monitoring. No clear path from prediction to business action.
The same applies to LLMs. A demo that wows in a sandbox often hallucinates in production, drifts as documents update, or costs more than the human work it was meant to replace.
Production-first, not lab-first
We design every ML and AI engagement backwards from production. Before training a model, we define how its predictions will reach a decision-maker, how it will be retrained, how drift will be detected, and what business KPI it will move.
For LLM systems, that means RAG with evaluation harnesses, ground-truth datasets, and accuracy thresholds — not vibes. For traditional ML, it means MLOps tooling, feature stores where they earn their cost, and rollback plans that actually work.
What We Deliver
What we deliver
From a focused use-case discovery to multi-model production platforms — scoped to where AI actually unlocks value.
MLOps & Deployment
Model packaging, CI/CD, feature stores, drift monitoring, automated retraining, and rollback plans on MLflow, Vertex AI, SageMaker, or Databricks Model Serving.
LLM Applications & RAG
Retrieval-augmented generation, agentic workflows, document Q&A, and conversational interfaces on OpenAI, Anthropic Claude, Google Gemini, or open-source models with vector databases.
Evaluation & Monitoring
Offline eval suites, ground-truth datasets, A/B test harnesses, and production monitoring for both traditional ML and LLM-based systems. We don't ship without thresholds.
AI Use-Case Discovery
A focused 3–4 week sprint to identify, prioritise, and de-risk AI use cases. Output: a costed, ROI-ranked AI roadmap your leadership team can actually defend.
Generative AI Platforms
Internal AI tooling — copilots, content generators, knowledge bases — with proper guardrails, cost controls, and audit logging for enterprise deployment.
How We Work
A delivery process built for measurable outcomes
From scoping to handover, every engagement follows the same disciplined cadence — designed to remove ambiguity and ship results.
Use-Case Validation
We pressure-test the proposed use case for data sufficiency, business KPI clarity, and a credible path to deployment. Bad fits get killed early — saving you a six-figure write-off.
Baseline & Prototype
Quick baseline (often a heuristic or simple model) against a held-out evaluation set. Establishes the bar the production model must clear.
Production Build
Model + data pipelines + serving + monitoring + retraining loop, shipped together. Demos are weekly; we never deliver a model without the surrounding infrastructure.
Operate & Improve
30, 60, and 90-day post-launch reviews against the business KPI. Models that aren't earning their keep get re-trained, re-scoped, or retired.
Tools & Stack
Production-grade stack, vendor-neutral choices
We pick the right tool for the job — and document why — so you're never locked into a stack that doesn't fit your team.
Python (scikit-learn, PyTorch, TensorFlow)
XGBoost / LightGBM
Hugging Face Transformers
PySpark
MLflow
Vertex AI
AWS SageMaker
Databricks ML
OpenAI API
Anthropic Claude API
Google Gemini API
LangChain / LlamaIndex
Pinecone / Weaviate / pgvector
Feast (feature store)
Evidently / WhyLabs
Outcomes
Outcomes you can expect
Indicative ranges based on typical client engagements. Every project ties to a measurable KPI agreed at kickoff.
10–25%
typical lift on the targeted business KPI (churn, revenue, conversion)
50–80%
reduction in manual review time on document-heavy workflows
< 12 wk
typical time from kickoff to first model in production
100%
of models shipped with monitoring, retraining, and rollback plans
FAQ
Frequently asked questions
When should we use an LLM vs. a traditional ML model?
LLMs win for unstructured text and language tasks — summarisation, classification with few labels, conversational interfaces, and reasoning over documents. Traditional ML (gradient boosting, regression, classification) wins for structured tabular problems like forecasting, churn, fraud, and pricing — they are cheaper, faster, more accurate, and far more explainable than LLMs for those use cases.
How do you build LLM applications that are accurate and don't hallucinate?
Through retrieval-augmented generation (RAG), strict context grounding, evaluation harnesses, and humans-in-the-loop for high-stakes decisions. Every LLM system we ship has an offline eval suite with thresholds — and we never deploy without it.
Do we need a data scientist on our team to maintain what you build?
Not always. We deliver MLOps tooling — automated retraining, drift detection, and monitoring — so most production ML systems can be operated by an engineering team without a dedicated data scientist. For complex, evolving model portfolios we recommend at least one ML engineer in-house.
How do you make sure the model produces business value, not just accuracy?
Every engagement starts with a business KPI — not an accuracy metric. We design the model, deployment, and decision interface around moving that KPI, then measure post-launch. If a more accurate model doesn't move the business outcome, we don't ship it.
What about data privacy and AI governance?
We follow data minimisation, role-based access, and PII redaction by default. For regulated industries we deliver model cards, audit trails, and bias evaluation. We support on-prem and VPC-isolated LLM deployments where data residency requires it.
Related Services
Explore complementary capabilities
From AI ambition to AI in production
Book a 30-minute consultation. Tell us the use case you're considering — we'll come back with a frank assessment of feasibility, data requirements, and the shortest path to a production result.
Book a Consultation