Capability · AI & Data
AI that survives contact with production.
Most AI projects die between the demo and the deployment. We build the parts that make the difference — evaluation, retrieval quality, cost control, guardrails and the data foundation underneath.
Why this matters
The demo is the easy part.
A prototype that impresses in a meeting and a system that holds up under real users are different engineering problems. The second one needs evaluation you trust, retrieval that stays accurate as the corpus grows, latency and cost budgets that survive scale, and a way to tell whether a change made things better or worse. That is the work we do.
What we do
Four areas of work.
Combined to fit the problem — you are never sold a fixed bundle.
Generative AI systems
Retrieval-augmented generation, agentic workflows and document intelligence built around an evaluation harness, not around a prompt.
- RAG architecture, chunking and retrieval tuning
- Agent design with tool use and human checkpoints
- Offline and online evaluation pipelines
- Guardrails, grounding and hallucination containment
Machine learning platforms
The infrastructure that lets a model go from notebook to production and back again without a rewrite each time.
- Feature stores and training pipelines
- Model registry, versioning and rollback
- Drift detection and scheduled retraining
- Inference serving with cost and latency budgets
Data engineering
Pipelines and warehouses designed to be debugged at 3am — observable, idempotent and cheap to re-run.
- Batch and streaming ingestion
- Dimensional modelling and warehouse design
- Data quality contracts and lineage
- Cost and performance optimisation
Decision intelligence
Analytics that change a decision rather than filling a dashboard nobody opens.
- Metric layers and single-definition KPIs
- Forecasting and scenario modelling
- Executive and operational reporting
- Experimentation and causal analysis
What you get
How the work is different.
- Every system ships with a measurable quality baseline
- Eval-first
- Token, compute and storage budgets designed in, not discovered
- Cost-aware
- No lock-in to a single model vendor or cloud
- Portable
- Traces and metrics from day one, not after the first incident
- Observable
Tooling
What we work with.
We pick tools to fit your constraints and your team's ability to maintain them — not to fit our preferences.
Models & serving
- Claude
- OpenAI
- Llama
- vLLM
- Bedrock
- Vertex AI
Data
- Postgres
- BigQuery
- Snowflake
- dbt
- Airflow
- Kafka
- Spark
Vector & search
- pgvector
- Pinecone
- Qdrant
- OpenSearch
Platform
- Python
- TypeScript
- Docker
- Kubernetes
- Terraform
Ways to start
Pick the smallest useful first step.
Each of these stands alone. None of them requires committing to the next.
- 01
AI readiness review
Two to three weeks. Where AI would actually pay off in your operation, what your data can support today, and what it would cost to run.
- 02
Evaluated pilot
Six to eight weeks. One use case built end to end with an evaluation harness, so the go/no-go is a number rather than an opinion.
- 03
Platform build
A dedicated squad standing up the data and model infrastructure your teams build on afterwards.
Other capabilities
Next step
Bring us the hard version.
The clearest way to judge us is to describe the problem you have not been able to solve internally. We will tell you honestly whether we are the right team.