Most RAG systems fail in production.
Not because of the model — because of the architecture.
We build retrieval-augmented generation pipelines for US companies in regulated industries. Hybrid retrieval. RBAC at the retrieval layer. Continuous evaluation. Zero silent failures.
You built an internal AI assistant. It worked beautifully on your test documents. Then you pointed it at production data — mixed formats, scanned PDFs, three years of inconsistent metadata — and it started returning garbage.
Or worse: it kept returning answers. They were just wrong. And nobody knew.
In fintech, that's a compliance risk. In legal, it's liability. In healthcare, it's patient safety. The stakes of a silent AI failure are not the same across industries — and we build systems calibrated to yours.
Where systems break
Test data ≠ production data
Real documents have mixed formats, OCR artifacts, inconsistent metadata. Systems tuned on clean test data collapse on the real thing.
No evaluation = no visibility
Without RAGAS pipelines and regression tests, quality degrades silently. You find out from user complaints, not dashboards.
RBAC bolted on after deployment
Access control at the API layer isn't enough. Sensitive documents surface to wrong users on semantically similar queries.
Finds the right document even with imprecise queries, domain jargon, and mixed-format inputs. Pure vector search fails on exact match. You need both.
When the system doesn't have high certainty, it says so and routes gracefully. No hallucinated answers delivered with false confidence.
Per-user metadata filtering before vector search runs. Sensitive documents don't surface to unauthorized users — built into retrieval, not the API.
We measure retrieval precision, answer faithfulness, and context relevance continuously. You get a dashboard, not surprises at the next sprint review.
When your product updates or your data changes, the system updates. Stale knowledge is the silent killer of AI adoption.
Every answer links to its source document. Auditable. Compliant. Defensible in a regulatory review — or in front of your compliance team.
A US financial analytics platform needed to query market data daily with fully auditable answers under compliance requirements. The previous approach produced hallucinated answers on complex financial documents.
Results
A 60-person US SaaS engineering team had three years of Confluence docs, runbooks, and architecture decisions that nobody could find. We built a RAG system over their full corpus — queryable via web and a Slack slash command.
Results
Retrieval & Embedding
Evaluation
Orchestration
APIs & Data
How long does it take to build a production RAG system?
A typical engagement is 6–10 weeks from scoping to production deployment. This includes document ingestion pipeline, hybrid retrieval setup, RAGAS evaluation infrastructure, and a written runbook. Timeline depends on data complexity, the number of document sources, and whether we're integrating with existing authentication systems.
What does a RAG engagement cost?
Most RAG projects fall between $15,000–$50,000 depending on scope. We offer a 2-week AI Readiness Audit at $3,500 as a lower-risk entry point — it maps your data, identifies retrieval failure modes, and produces a concrete architecture recommendation before we commit to a full build.
Do you have experience with compliance-sensitive industries?
Yes. Our production systems run in US fintech under regulatory oversight — citation tracing on every response, RBAC at the retrieval layer, full audit trails. We understand that in regulated environments, an ungrounded AI response is not a product bug, it's a liability event. We architect for that from day one.
Can you work with our existing vector database / infrastructure?
Yes. We work with Pinecone, FAISS, ChromaDB, Weaviate, and pgvector. We can build around your existing AWS setup, your preferred embedding model, and your current auth system. The goal is a system your team can own — not one that creates new vendor lock-in.
Where is your team located?
We work with US clients across all time zones. All communication is in English, async-first with regular synchronous check-ins. Every client works directly with the senior engineer running the project — there's no account manager layer, no handoffs to junior staff.
What happens after the project is delivered?
You get the full codebase, documentation, an ops runbook, and a handoff session. The system is built to be maintained by your team without us. We also offer an optional 90-day post-delivery support retainer for teams who want ongoing evaluation monitoring or want us available during the first production quarter.
Start with the AI Readiness Audit — $3,500
A 2-week audit of your data, infrastructure, and AI readiness. Full written roadmap with realistic effort and cost estimates — no retainer required. Take the deliverable to any team.
Let's build yours right the first time. We take a small number of projects at a time — every client works directly with the senior engineer running the system.
Book a Free Scoping Call