01 · AI & Intelligent Systems
AI that survives contact with production.
We build AI systems that run inside real workflows: retrieval-augmented assistants over enterprise data, agents that call your systems safely, vision models on imagery and predictive models with the evaluation and monitoring needed to trust them.
- Generative AI
- Machine learning
- Computer vision
- NLP
- AI agents
- AI-assisted decision systems
- MLOps
What we build
AI & Intelligent Systems, in practice.
LLM & RAG applications
Assistants and search over documents, databases and APIs, with retrieval tuned to your data, grounded answers with citations and access control that mirrors your source systems.
Agentic workflows
Multi-step agents that plan, call tools and hand off to people. We design the tool contracts, guardrails and audit trail first, so automation stays observable and reversible.
Text-to-SQL & analytics copilots
Natural-language access to warehouses and metric layers, constrained by a semantic model so generated queries respect business definitions instead of guessing them.
Computer vision & GeoAI
Detection, segmentation and classification on photos, drone and satellite imagery, from land-parcel segmentation to vegetation and water detection.
Predictive & decision models
Classification, forecasting and scoring models embedded in operational systems, with features and labels traced back to governed data.
Evaluation & MLOps
Offline evaluation sets, regression tests for prompts and models, deployment pipelines, drift and cost monitoring. Models are versioned artefacts, not notebooks.
Architecture
The production AI lifecycle
Data foundations
Quality, coverage, governance
Retrieval & context
Indexes, semantic & spatial joins
Models & agents
LLMs, ML, vision, tool use
Evaluation
Test sets, acceptance bars
Deployment
APIs, CI/CD, access control
Monitoring
Drift, cost, feedback
How we work
Engineering principles.
- Python
- PyTorch
- TensorFlow
- LangChain
- LangGraph
- LLM APIs
- FastAPI
- MLflow-style tracking
Data before models
Most AI failures are data failures. We check coverage, freshness and quality before choosing a model.
Evaluate like software
Every AI feature ships with a test set and an acceptance bar agreed with the business and is re-run on every change.
Design for the human in the loop
Confidence, provenance and override paths are part of the interface, not an afterthought.
Start a conversation
Bring us the hard problem.
Tell us what you are trying to build and the constraints you are working within. We will come back with how we would approach it.