04
AI & machine learning
AI is useful when it is boring enough to run every day.
Problem
Most AI work dies in a prototype because nobody designed the operating system around it.
A prompt is not a product. Production AI needs data boundaries, evaluation, fallbacks, cost control, and a human path when the model is wrong. We build those parts on purpose.
- Chat overlays on data the model should not see
- No evaluation, so nobody knows if it got worse
- Agents that can act without a permission model
- Cost and latency discovered after launch
Capabilities
What this work actually includes.
01
Applied generative AI
Assistants, drafting, extraction, and Q&A grounded in your corpus — with citations and refusal.
02
Retrieval systems
Chunking, embeddings, access control, and evaluation for RAG that operators can trust.
03
Agents with limits
Tool use, approvals, and audit logs. Autonomy is a setting, not a personality.
04
Classical ML
Ranking, classification, forecasting, and anomaly detection when a generative model is the wrong instrument.
05
MLOps & evaluation
Pipelines, golden sets, regression tests, and traces so quality has a number.
06
Automation
Document processing, routing, and back-office work with a human in the loop where it matters.
Technology
Tools we reach for here.
- Python
- PyTorch
- scikit-learn
- OpenAI
- Anthropic
- LangChain
- LlamaIndex
- PostgreSQL
- pgvector
- AWS
- Azure
Approach
How we deliver it.
01
Name the job
What decision or draft does this replace? If we cannot say, we do not start.
02
Bound the data
What the model may see, what it may write, and who can override it.
03
Evaluate before theatre
A small, honest test set beats a cinematic demo.
04
Operate it
Logs, costs, fallbacks, and a weekly review of where it fails.
Use cases
Where this tends to land.
Internal knowledge assistants
Answers from policies, tickets, and docs — with sources, not vibes.
Document intelligence
Extract, classify, and route the paperwork that currently waits on people.
Product intelligence
Recommendations, search, and copilot features inside a customer product.
Operations automation
Repetitive decisions with a model, a rule, or both — and a trail.
Related industries
Related insights
FAQ
Useful questions.
When the problem needs it. Many products are better served by retrieval, fine-tuning, or classical ML. We choose the cheapest honest method.
Start a project
Start a ai & ml project.
A short note is enough. We will come back with questions, not a pitch deck.