Gaper builds RAG systems that ground your agents in your own data, with citations.
Gaper designs and deploys retrieval-augmented generation pipelines that pull answers straight from your documents, databases, and knowledge bases. Every response is grounded in real sources and cites them, so your agents stay accurate and your team can verify the output.
$ 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
A RAG system is a retrieval layer that grounds a language model in your own content. It fetches the most relevant passages from your documents and data at query time, feeds them to the model as context, and returns answers that cite their sources, so outputs stay accurate, current, and verifiable.
OpenAI
Claude
GeminiModel-agnosticMost agent projects stall on accuracy: the model answers confidently from training data instead of your actual content, and nobody can trace where a claim came from. Without grounded retrieval and evals, a demo that looks great quietly invents policies once real users arrive.
- 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.
Retrieval pipelines built on your content
Gaper ingests your documents, wikis, tickets, and databases, then builds the chunking, embedding, and indexing pipeline that turns them into a fast, queryable knowledge layer your agents can reason over.
Grounded, cited answers
Every response is traced to the passages it came from. The agent quotes and links its sources, and says it does not know rather than guessing when retrieval comes up empty.
Hybrid search and reranking
We combine semantic and keyword retrieval with rerankers so the model sees the right context, not just the closest vector. Better recall and precision means fewer wrong answers.
Evals before users
We build retrieval and answer-quality evals that score groundedness, citation accuracy, and relevance, and gate every release on them so quality is measured, not assumed.
Model-agnostic deployment
The same retrieval layer runs on OpenAI, Claude, or Gemini. We pick per workload and keep you free to switch models without rebuilding the pipeline.
Runs in your cloud on your data
Indexes and embeddings live in your environment under your auth and access controls. Your knowledge base stays yours, with no data leaving your perimeter unless you decide it should.
Grounded answers, with sources you can check
An agent that invents a policy is worse than no agent. We design retrieval so every claim is anchored to your real content and shown with its citation, and so the agent escalates instead of guessing when it has nothing solid to stand on.
- Cited, source-grounded responses
- Honest "I don't know" paths when retrieval is thin
- Traceable from answer back to passage
- 01Eval suiteknown + edge casespass
- 02Policy checkguardrails enforcedpass
- 03Human fallbacklow-confidence routedhold
- 04Releaseshipped to prodlive
p95 latency 1.2s
eval pass 12/12
rollback ready
Evaluated before it ever reaches a user
Retrieval quality is the difference between a demo and a production system. We build evals that score groundedness, citation accuracy, and relevance, then gate releases on them so accuracy is a number you can watch over time, not a hope.
- Groundedness and citation scoring
- Relevance and recall tracked per release
- Quality gates that block regressions
Deployed in your stack, owned by your team
We deploy the retrieval layer where your data already lives, on your cloud and your auth, then hand over the code, the evals, and a runbook. You can re-index, tune, and extend it without us.
- Runs in your environment on your data
- Code, evals, and runbook handed over
- Extend and re-index without us
Access your auth
Data your environment
Ops monitor or handoff
Questions buyers ask us.
What does Gaper actually build for a RAG project?+
How do you keep answers accurate and prevent hallucination?+
Where does the system run and who owns the data?+
Which model and database do you use, and how fast can it go live?+
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.