Service RAG Systems

Retrieval-augmented generation, built to be right in production.

Most RAG demos work on the three questions someone rehearsed and fall apart on the fourth. Mine are built to hold up on the questions you didn't rehearse — hybrid retrieval that actually finds the right passage, reranking that puts it first, and grounded, source-cited answers on your own data, without that data leaking anywhere it shouldn't.

Source-cited Every answer, every time
Hybrid search Keyword + vector, reranked
1 KPI Attached before we build

Most "RAG" I get called in to fix is a vector database bolted to a chatbot, and it fails in the same predictable way: the right chunk exists in the index, but it never makes it into the prompt. That's not a model problem. That's a retrieval problem, and no amount of prompt tuning fixes it.

I build the retrieval layer first, because it's the part that decides whether the rest of the system is even worth running. Hybrid search catches what pure embeddings miss — exact terms, IDs, part numbers, names. Reranking puts the actually-relevant passage at the top instead of the third-closest cosine match. And every answer ships with the citation that produced it, so you can check the model's work instead of trusting it.

What a production RAG system actually needs

Three things separate a system your team relies on from a demo that impresses once and gets quietly abandoned.

  • Hybrid retrieval — keyword and vector search combined, tuned to your content, so exact terms and semantic meaning both surface the right passage.
  • Reranking — a second pass that reorders candidates by actual relevance to the query, not just embedding distance, before anything reaches the model.
  • Grounding and citations — answers constrained to what the retrieved sources actually say, with a citation on every claim, so wrong answers are visible instead of confidently invisible.

Your data, without the leaks

The other half of the job is the boundary around the data. Chunking that respects document structure, access controls enforced at retrieval time — not just at the UI — and no client's documents ever surfacing in another client's answers. I design the index and the permission model together, not as an afterthought once the demo works.

A RAG system that can't cite its source is a guessing machine with better vocabulary. I build ones that show their work on every answer.

The four layers of a production RAG system.

Built in from the first sprint — not bolted on after the demo hallucinates.
01 / Retrieval
Hybrid, not just vector
Keyword and semantic search combined and tuned to your content, so exact terms, IDs, and meaning-based queries all find the right passage.
02 / Reranking
Relevance, ordered correctly
A second-pass reranker reorders retrieved candidates by actual relevance to the query before anything reaches the model's context window.
03 / Grounding
Answers tied to sources
Responses constrained to what the retrieved documents actually say, with a citation on every claim so you can verify instead of trust.
04 / Evals
Guardrails and a scoreboard
Retrieval precision, citation accuracy, and answer quality measured against a held-out test set, tracked release over release.
Hybrid searchRerankingGroundingCitationsEvalsOn-prem option

Twelve weeks, docs to grounded answers.

Fixed scope, fixed price, weekly demos during the build.
01Identify

Find the question worth answering

We map the corpus and the real questions your team asks it, and agree on the KPI — answer accuracy, time-to-answer, deflected tickets — before a line of code is written.

02Architect

Design retrieval, reranking, and grounding

Chunking strategy, hybrid index, reranker, and citation format are designed together against your real documents — fixed scope and price, no surprises.

03Build

Ship into production, not a slide deck

We build in sprints with weekly demos on your real corpus, deploy into your environment, and harden retrieval until it holds up under real questions.

04Measure

Prove it against the KPI

We run evals against a held-out test set, track the system against the number we set, and keep tuning retrieval until the value crosses the cost.

Related work.

RAG rarely ships alone. These usually come with it.
Service / 05
Knowledge systems
Your documents, wikis, tickets, and contracts turned into one queryable, source-cited system that finally answers back — with the receipts.
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Service / 01
Enterprise AI agents
Agentic workflows designed around your actual processes, not a demo — built, deployed, and tuned until they hold up in production.
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Service / 03
AI infrastructure
Local model deployment and hardware builds for organizations that need their data to stay inside their walls. Nothing leaves the perimeter.
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Service / 04
Compliance & governance
SOC 2 controls, HIPAA compliance, and deployments built for regulated industries — from day one, not bolted on later.
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Service / 06
Decision intelligence
Analytics, dashboards, and predictive models that drive real decisions — built by someone who spent 15 years attaching dollars to outcomes.
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Service / 07
Enterprise workflows
Multi-system orchestration across Salesforce, ServiceNow, SAP, and your custom APIs — finally talking, with an audit trail.
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Let's build retrieval that's actually right.

If you have a corpus your team keeps asking questions of, that's where we start. I'll tell you what accurate retrieval is worth before we build it.

Let's talk

Markets served.

Remote-first across the United States and internationally — including these markets.

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