// Service
AI & Implementation
Practical AI built into your product and workflows: LLM features, RAG and automation, implemented where it measurably helps and skipped where it doesn't.
llm features · rag · integration
// 01 · Scope
What's included
- LLM features in your product: chat, search, summarization
- RAG pipelines over your own documents and data
- AI workflow automation for back-office processes
- Evals, guardrails and cost controls so output stays reliable
- Integration into your existing stack, not a bolt-on demo
// 02 · Flow
How the engagement runs
01
Find the real use case
We start from the business problem and check whether AI actually beats the simpler option.
02
Prototype against your data
A working proof of concept on your real documents and workflows, evaluated honestly.
03
Implement and integrate
Guardrails, evals and cost controls around the model, wired into your product and infrastructure.
04
Measure and iterate
We track output quality and usage, then tune prompts, retrieval and models as the field moves.
// 03 · Tools
What we reach for
ClaudeGPTLangChainRAGPyTorchOpenAI
Chosen per project, proven in production · see it in past work
Ready to put AI to work?
We'll find where AI genuinely helps, then implement it against your real data.