What this covers.
- Retrieval-augmented assistants over your data
- Document parsing and structured extraction
- Customer support triage and drafting
- Internal workflow automation
- Semantic search and recommendations
- LLM evaluation and guardrails
- Cost, latency and token optimisation
- Human-in-the-loop review interfaces
What you actually receive.
Not just a repository handover — the surrounding pieces that decide whether it survives its first year.
Working integration
Wired into the product your team already uses, not a standalone toy that never gets adopted.
Evaluation harness
A real test set with measured accuracy, so you can change the prompt or model later and know whether it got better.
Guardrails
Input validation, output constraints, escalation paths and audit logging — the parts that decide whether it survives production.
Cost model
Per-request and monthly cost projections at your real volume, with the levers to bring them down documented.
Typical stack
Chosen per project, but this is where we start and what we know deeply.
- Claude API
- OpenAI
- LangChain
- pgvector
- Pinecone
- Python
- TypeScript
Typical engagement
$2k – $15k
A scoped pilot on one workflow starts low; multi-workflow platform integration with evaluation sits higher.
- Timeline
- 4 – 16 weeks
- Pricing model
- Fixed scope, fixed price
AI Integration & Automation, specifically.
No. We use enterprise API tiers where inputs are not retained for training, and for sensitive workloads we can architect around self-hosted or regionally-pinned models instead.