RAG systems grounded in your documents
Retrieval-augmented generation (RAG) is how you get an LLM to answer from your private, current data instead of guessing. We build production RAG pipelines — ingestion, chunking, embeddings, hybrid retrieval, re-ranking, and evaluation — so answers are accurate, cited, and stay fresh as your content changes. Deploy in your own cloud or VPC so proprietary data never trains a public model.
Who it’s for: Teams that need AI answers grounded in their own documents, with accuracy and data control.
A focused single-source RAG assistant starts around $15k; multi-source, access-controlled systems scale from there. Scoped after a free discovery call.
Everything we deliver
A complete, senior-led capability — engaged end-to-end or à la carte.
Ingestion & chunking pipeline
Robust loaders for docs, PDFs, wikis, tickets, and databases, with chunking tuned to your content structure.
Embeddings & vector store
Production vector storage (pgvector, Pinecone, and similar) sized and indexed for your scale and latency needs.
Hybrid retrieval + re-ranking
Semantic plus keyword retrieval with a re-ranking pass, so exact error strings and fuzzy questions both return the right passage.
Evaluation & guardrails
An eval set and retrieval metrics to catch regressions, plus grounding and 'I don't know' behavior to prevent hallucinations.
Freshness & ops
Scheduled re-indexing wired to your content source so the system never drifts from the published truth.
What you walk away with
- Accurate, cited answers grounded in your own documents
- Hybrid retrieval + re-ranking so both exact terms and concepts work
- Private / VPC deployment — your data stays yours
- Evaluation harness so quality is measured, not assumed
A clear path from idea to launch
The same proven process across every engagement — transparent, collaborative, and senior-led.
- 1
Discover
We map goals, users, and constraints — and sign an NDA before we dig in.
- 2
Design
Flows, UX, and a premium UI system, validated with you before a line of code.
- 3
Build
Senior engineers ship in tight iterations with QA and reviews at every stage.
- 4
Ship
We launch, instrument, and harden — fast, SEO-ready, and accessible.
- 5
Scale
Ongoing support, new features, and optimization as you grow.
RAG Development questions, answered
RAG vs. fine-tuning — which do I need?
RAG grounds answers in your current documents without retraining, so it's cheaper, easier to keep fresh, and far less prone to hallucination — the right default for a knowledge assistant. Fine-tuning is for fixing tone, format, or specialized behavior. Most business use cases start with RAG.
How much does a custom RAG chatbot cost?
A focused single-source RAG assistant typically starts around $15k; multi-source systems with access control and evaluation run higher. We scope precisely after understanding your content and accuracy requirements.
How do you reduce hallucinations in a RAG system?
Better retrieval (hybrid + re-ranking), grounding every answer in retrieved passages with citations, confidence thresholds that trigger a human hand-off, and an evaluation harness that measures faithfulness — not vibes.
Can it run on our own infrastructure?
Yes. We deploy in your cloud or VPC (or on-prem) with data isolation, so proprietary content never leaves your environment or trains a public model. We align to SOC 2, GDPR, and HIPAA where relevant.
Ready to start your RAG Development project?
Tell us about your project and we'll send a scoped, transparent estimate.