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
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