RAG systems built, grounded, and evaluated so your agents answer from your own data.
Gaper builds the full retrieval pipeline that grounds your AI agents in your real content: ingestion, chunking, embeddings, retrieval, and citations. We deploy it into your cloud on your data and auth, prove accuracy with evals, then hand over the code and runbook so you own it.
$ gaper deploy agent --to production ✓ plan ……………… 4 steps ✓ retrieve …… 1,240 docs grounded ✓ tool ………… salesforce.update_record ✓ eval ………… 12/12 checks passed ● live · p95 1.2s · 0 errors
RAG engineering at Gaper is the work of building a retrieval-augmented generation pipeline that grounds an AI agent in your own documents and data. We design ingestion, chunking, embeddings, retrieval, and citation, then evaluate accuracy and deploy the system into your cloud for you to own.
OpenAI
Claude
GeminiModel-agnosticMost teams bolt a vector search onto an LLM and watch it hallucinate, cite nothing, and drift as the corpus grows. Without grounding, retrieval evals, and freshness controls, the answers cannot be trusted in production.
- Does it touch real systems?
- Can the outcome be measured?
- Where does human approval stay?
- Who owns it after launch?
Book a free assessment. We will identify one high-leverage workflow, make the build-vs-buy call, and scope the smallest production release.
From strategy to production, owned by your team.
- 01
Map the workflow
We start from the documents, SOPs, portals, inboxes, and spreadsheets your team already uses, then turn the repeatable path into an agent workflow map.
- 02
Build the supervised agent
We build on OpenAI, Claude, Gemini, or the right model for the job, with evals, guardrails, citations, and human approval gates where risk matters.
- 03
Connect the stack
The agent gets the data layer, APIs, MCP tools, auth, and write-backs it needs to finish work inside your systems, not beside them.
- 04
Sandbox, verify, go live
We launch in a sandbox, verify every run, then move into supervised production with traces, rollback, and an owner.
Agents wired into the systems you already run.
Ingestion and chunking
We pull from your docs, wikis, tickets, databases, and PDFs, then chunk and normalize them so retrieval stays accurate as content changes. Parsing, dedup, and metadata are built in, not bolted on.
Embeddings and vector store
We choose and tune the embedding model and index for your corpus and budget, on OpenAI, Claude, or Gemini, in your own vector store. Model-agnostic, so you are not locked to one provider.
Grounded retrieval and reranking
Hybrid search, reranking, and query rewriting bring back the right passages, not the loudest ones. Every answer is forced to ground in retrieved sources so the agent stops inventing facts.
Citations and answer quality
Responses link back to the source passage so users and auditors can verify them. We tune for faithfulness and relevance, not just plausible-sounding text.
Retrieval evals and guardrails
We build an eval set on your real questions and measure recall, faithfulness, and citation accuracy before launch and on every change. Regressions surface in CI, not in front of customers.
Freshness and re-indexing
Pipelines re-index on a schedule or on change so the agent answers from current content. Stale documents expire, and you see exactly what the agent can and cannot retrieve.
How Gaper builds and delivers your RAG system
We follow one arc: Design, Build, Deploy. We map your sources and the questions the agent must answer, build the retrieval pipeline against your real corpus, and deploy it into your environment on your data and auth. You get the code, the eval suite, and a runbook, and your team owns all of it.
- Design the source map, chunking strategy, and eval set from your real questions
- Build ingestion, embeddings, retrieval, reranking, and citation end to end
- Deploy into your cloud and hand over code, evals, and runbook
- 01Scopeworkflow mappeddone
- 02Buildagent + toolsdone
- 03Evaluatesuite greendone
- 04Shiplive in prodlive
p95 latency 1.2s
eval pass 12/12
rollback ready
Grounded and citable, measured before launch
A RAG system is only useful if you can trust it. We hold the pipeline to retrieval evals on faithfulness, recall, and citation accuracy, run them on every change, and wire guardrails so the agent declines when it lacks grounding rather than guessing.
- Eval set built on your actual queries, scored on faithfulness and recall
- Citations on every answer so people can verify the source
- Guardrails that refuse ungrounded answers instead of hallucinating
- 01Eval suiteknown + edge casespass
- 02Policy checkguardrails enforcedpass
- 03Human fallbacklow-confidence routedhold
- 04Releaseshipped to prodlive
p95 latency 1.2s
eval pass 12/12
rollback ready
You own the system and run it in production
This is a build-and-deliver engagement, not a seat we fill. The RAG pipeline runs in your cloud, on your auth, against your data, and the code and evals live in your repos. We can be live in as little as 24 hours, and your team keeps shipping after we hand over.
- Runs in your own cloud on your data and auth, no lock-in
- Code, evals, and runbook handed over for your team to own
- Live in as little as 24 hours, supervised into production
Access your auth
Data your environment
Ops monitor or handoff
Questions buyers ask us.
Does Gaper staff out or place RAG engineers?+
Who owns the RAG pipeline after you build it?+
How do you keep the agent from hallucinating?+
Which models and vector stores do you use?+
Ready to deploy your first agent?
Book a free 30-minute assessment. We'll map the highest-leverage workflow and scope the smallest thing worth shipping, live in as little as 24 hours.