Production RAG in your infra.You own it, we build it.
Every corpus retrieves differently. We build your pipeline around yours — parsing your document types, tuning search on your real queries, and proving quality before we hand over the keys.
- Cloud agnostic
- Infrastructure agnostic
- Production-first
- Custom connectors
Built deliberately. Proven before handover.
Retrieval quality measured, not assumed. We prove it on your queries before handover.
Discovery
We start with the business problem, then map the reality around it.
- Understand the business problem
- Learn existing workflows
- Review current infrastructure
Architecture
We design a retrieval system for your data — not a template.
- Design a custom RAG pipeline
- Identify required connectors
- Plan the retrieval strategy
- Define evaluation metrics
Build
We develop, integrate, and deploy inside your environment.
- Develop custom connectors
- Build the retrieval pipeline
- Integrate with existing systems
- Deploy securely
Validate
We test against real usage and refine until your team signs off.
- Test against real usage
- Refine against feedback
- Repeat until it's production-ready
- Sign off with your team
Sign-off isn't the end of the engagement.
Steps 03 and 04 loop until the system is genuinely ready for production — not just working once. After go-live we stay on for monitoring, fixes, and continuous improvement as your data and needs evolve.
The infrastructure underneath production retrieval.
You don't need to arrive knowing which pieces you need — we help you decide what's necessary for your use case, then build it.
Custom connectors
Ingestion from wherever your knowledge actually lives — wikis, ticketing systems, databases, file shares, proprietary APIs.
Parser engine
Structure extracted from your real documents — tables, scanned PDFs, manuals, code, CAD — not just clean text.
Index infrastructure
Vector, keyword, and graph indexes deployed inside your own cloud, sized and tuned to your corpus.
Custom retrieval methods
Hybrid search, reranking, multi-hop and agentic retrieval, tuned against your real queries — not a benchmark.
MCP & tool integration
Retrieval exposed as tools your existing agents, copilots, and applications can call directly.
Why not just use a managed RAG service?
Sometimes you should — for a prototype or a small, clean corpus, a managed service is the right call. In production, a pipeline you can't tune or own is usually where retrieval starts to fail.
See the full comparisonManaged RAG
One pipeline for the average corpus, on the vendor's terms.
Custom RAG
Your parsing, retrieval, and evaluation — tuned to your data, owned by you.
Our biggest success so far30% more customers served every month. $4M in added annual sales.
The problem
Technicians service commercial kitchen equipment from many manufacturers. Finding the right fix across thousands of pages of documentation was slow, and mistakes were costly.
Our solution
We indexed the full documentation library, hot-side and cold-side, into a retrieval system technicians can simply ask.
- Discovery workshop
- Data collection
- Architecture design
- Connector planning
- Data ingestion
- Retrieval pipeline
- AI application
- Deployment
- Testing
- User rollout
+30%
More customers served, every month
$4M
Added to annual sales
↓
Costs from human error in the field
You're hiring engineers to architect a system — not buying a chatbot.
Production-first engineering
We build for uptime, evaluation, and maintainability — not a demo that impresses once.
Custom architecture per client
Every pipeline is designed for one organization's data, workflows, and constraints.
Cloud & infrastructure agnostic
AWS, Azure, GCP, on-prem, or hybrid. We build where you already operate.
Works with existing systems
We integrate into your enterprise stack rather than replacing it.
Custom connectors, built as needed
Proprietary sources and internal systems get first-class connectors.
Fast deployment
A focused scope and a proven method put production in reach in about two weeks.
Modern RAG best practices
Hybrid retrieval, grounded citations, and evaluation baked in from day one.
Built to be maintained
Clean architecture and documentation so your team owns it long after launch.
Let's architect your system.
Tell us about your data, your stack, and the problem you're solving. We'll come to the first call with an informed point of view — not a sales pitch.