Service

RAG Systems

Retrieval-Augmented Generation over your private knowledge bases — accurate, sourced, and current.

Enterprise-grade RAG pipelines with ingestion, chunking strategies, hybrid retrieval (BM25 + vector), reranking, and answer synthesis. We solve hallucination and stale-data problems with citation-first design.
Our Process

Discovery → Design → Build → Test → Deploy / Support

A disciplined cadence that keeps stakeholders aligned and shipping predictable.

  1. Step 1
    Discovery

    Corpus audit, retrieval requirements, SLAs.

  2. Step 2
    Design

    Index strategy, chunking, reranker, eval set.

  3. Step 3
    Build

    Pipelines, retrieval, UI, citations.

  4. Step 4
    Test

    Recall, precision, factuality, latency.

  5. Step 5
    Deploy / Support

    Re-indexing cadence, monitoring, feedback loops.

Key Capabilities

Tooling we ship with

Battle-tested frameworks, models, and platforms — chosen for outcomes, not fashion.

Pinecone
Weaviate
pgvector
Elasticsearch
OpenAI Embeddings
Cohere Rerank
LlamaIndex
LangChain
Outcomes

What you'll get out of an engagement

Predictable delivery, measurable outcomes, and a system your team can own.

Production-grade architecture from day one

Senior engineering leadership embedded in your team

Evaluation harnesses and observability baked in

Knowledge transfer, runbooks, enablement

FAQ

Common questions

Ready to ship something users love?

Tell us what you’re building. We’ll bring a senior team to the kickoff call.