AI Integrations.
Embed AI capability into the product, stack or internal tooling you already run — without rewriting it. Tests, docs and a runbook left behind.
What “AI Integrations” means at Devmint.
AI Integrations at Devmint means embedding AI capability into the product, stack or internal tooling you already run — without rewriting it. We work inside your codebase, your auth model, your data warehouse and your existing UX, and we add the LLM layer, the vector store, the eval harness and the safety controls in a way that respects the system that's already paying your bills.
Most integration projects we inherit were bolted on by a vendor who didn't understand the host application. Devmint engagements start with a one-week technical audit, a clear written plan of where AI belongs and — critically — where it doesn't, and a delivery model where our engineers operate as a senior squad inside your team. You keep the code, the keys, the data and the IP. We leave behind tests, docs and a runbook.
Deliverables.
- Technical audit + written integration plan
- LLM, vector and eval layer inside your stack
- Tests, type safety, observability
- Cost + safety guardrails
- Handover docs your team can read
The shape.
Typical integration engagements run 3–8 weeks. Week one is a technical audit and written plan; the rest is implementation inside your codebase with shadow deploys and an explicit go-live week.
How we price.
Devmint engagements are scoped as fixed proposals against measurable outcomes — not hours. After a 30-minute discovery call, we send a written proposal with timeline, deliverables, eval targets and a single fixed fee. No procurement maze, no T&M creep. Smaller pilots and larger outcome-based contracts are scoped the same way — tell us what you're shipping and we'll come back with a number.
What we reach for.
We meet your stack. Common combos: Next.js + Vercel, Django + Postgres, Rails + Sidekiq, FastAPI + Cloudflare. AI layer typically OpenAI/Anthropic + pgvector/Pinecone + Langfuse.