AI practice
AI development
Most AI projects stall between the demo and the deadline. We build the unglamorous parts that decide whether an AI feature survives contact with real users: retrieval that returns the right document, evaluation that catches regressions before customers do, guardrails, cost control and observability.
What you get
Outcomes, not activity
A working system in production, not a proof of concept on a laptop
Evaluation suites that make model and prompt changes measurable
Predictable inference cost, with caching and model routing in place
Traceable answers your compliance team can actually audit
Scope
What the work actually involves
The pieces we bring to a typical engagement. Not every project needs all of them.
01
Retrieval-augmented generation
Document pipelines, chunking strategy, hybrid search and re-ranking tuned against your own corpus, with citations users can follow back to the source.
02
Agentic workflows
Tool-using agents that read your systems, take scoped actions and hand off to a human at the right moment. Built with explicit permissions and full execution traces.
03
Evaluation and observability
Golden datasets, automated graders and regression gates wired into CI, plus dashboards for latency, spend and failure modes per release.
04
Integration with existing software
The hard part is rarely the model. We connect AI to the ERP, CRM, data warehouse and legacy services you already run, without a rewrite.
Toolkit
What we tend to work with
Chosen per project rather than per preference. If your team already runs something else and runs it well, we work in your stack.
- Python
- TypeScript
- Claude
- OpenAI
- LangGraph
- pgvector
- Postgres
- Kubernetes
- AWS
- Azure
Questions
AI development in practice
Can you work with our data without sending it to a third party?
Yes. We deploy into your own cloud account or on-premises, and can run open-weight models where regulation or data residency requires it. Where hosted models are acceptable, we use enterprise endpoints with no-training guarantees.
How long before we see something real?
A scoped pilot typically runs four to six weeks and ends with a system used by real people on real data, plus an evaluation suite that tells you whether it is good enough to expand.
Related
Often combined with
Talk to someone who has shipped this
A short call with the engineer who would run the work, not a salesperson with a calendar link. We will tell you if it is not a fit.
Typical reply within one business day · Initial consultation is free