◆ SERVICE

Retrieval-augmented generation (RAG) development

Production RAG — chunking strategy, embedding choice, hybrid retrieval, reranking, evals. The 90% that gets ignored when teams stop at "vector DB hooked up".

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◆ WHY MIR

Why teams pick us

Senior-only team

Every engineer ships production code. No staffing-pyramid markup, no junior-tax. 9+ years building outsourced product teams for venture-funded clients.

Live products, not slides

JobCannon (B2C SaaS, live), Make It Real Academy, MIR Foundation. We use what we ship and we ship what we sell.

Founders who exited

Peter Kolomiets — Marketers (Ukraine 2017–2021), previous outsourcing exit (2023), now building Make It Real. We engineer like operators because we are operators.

◆ STACK

What we build with

pgvector / Qdrant / Pinecone OpenAI / Voyage / Cohere embeddings BM25 + dense hybrid Cohere / Voyage rerankers Ragas / TruLens evals

◆ PROCESS

How we ship

Build a 100-question gold-set before touching embeddings.
Baseline with naive retrieval, measure recall@k and faithfulness.
Iterate: chunking → embedding → hybrid → rerank → metadata filters.
Ship with eval CI — every prompt/embedding change is benchmarked.

◆ REFERENCES

Recent work

JobCannon — production AI/SaaS career platform: 1500+ skills, 2500+ careers, multi-locale, payment-grade Stripe integration. Built end-to-end with the same RAG systems stack we'd put on your project.

See live →

◆ START A PROJECT

Tell us what you're building

We respond within 24 hours. NDA available on request.