AI strategy & consulting
Where AI changes an outcome, where it does not, and what it will cost to run.
AI & Intelligent Systems
AI that does a specific job.
We build applied AI systems — retrieval, agents, forecasting and vision — that sit inside real workflows and are evaluated against your data, not a public leaderboard.
The problem
Most AI projects stall between demo and production. The prototype answers well in a controlled session, then meets messy data, ambiguous questions, cost ceilings and compliance review. Nothing is technically broken, but nobody can say whether it is right, and so it never gets trusted with a real decision.
How we approach it
We start from the decision the system is meant to support, then work backwards to the data, the evaluation set and the acceptable failure mode. Retrieval is built to cite. Agents get budgets, tool permissions and human checkpoints. Every release is measured against a fixed evaluation suite so a change to a prompt, model or index is a measurable event rather than a guess.
Capabilities
What sits inside this practice.
Generative AI & LLM systems
Model selection, prompt architecture, fine-tuning and structured output.
RAG & knowledge retrieval
Chunking, embeddings, reranking, freshness and citation-backed answers.
AI agents & automation
Tool use, orchestration, permissions, budgets and human-in-the-loop control.
Machine learning & forecasting
Feature pipelines, training, drift monitoring and prediction serving.
Computer vision
Detection, classification and inspection pipelines for real operating conditions.
Technology
Tools we reach for.
Selected per project against your constraints — never a fixed stack applied by default.
PythonPyTorchLangGraphVector databasesModel gatewaysFeature storesEvaluation harnessesStreaming pipelinesGPU orchestrationObservability
Use cases
Where it applies.
Knowledge assistants
Answering from internal documentation with sources attached and access rules respected.
Document processing
Extracting structured data from contracts, claims and forms at volume.
Forecasting
Demand, risk and capacity models wired into planning systems.
Quality inspection
Vision models running against production lines and field imagery.
Process
How delivery runs.
01 DiscoverGoals, constraints, current systems
02 ArchitectDesign and written trade-offs
03 BuildIterative delivery and testing
04 ScaleLaunch, measure, evolve
Case studies
Selected work.
Structure is content-ready. Real projects appear here once approved for publication.
[PROJECT TITLE]
[CHALLENGE] · [SOLUTION] · [OUTCOME]
[PROJECT TITLE]
[CHALLENGE] · [SOLUTION] · [OUTCOME]
FAQ
Questions we get asked.
Usually not. Most value comes from retrieval quality, evaluation and integration. We recommend training only when a measurable gap remains after those are solved.
With an evaluation set built from your data and reviewed by your domain experts, run automatically on every change.
Yes. We design for the deployment constraint you have — cloud, hybrid or fully self-hosted.
Model routing, caching, context budgets and measurement per request, defined during architecture rather than after launch.
Ready to scope it properly?
Bring the problem, the constraints and the deadline. We will come back with an architecture and an honest view of effort.