Saabsa · Austin HQ, US delivery
RAG development that answers from your documents
Retrieval-augmented generation is how a model answers from your corpus instead of its memory. Saabsa builds that path for production: chunking, permissions, evaluation, and the application around it. A demo chatbot over a public folder is not the engagement.
RAG breaks in production for boring reasons
The demo used a clean export. Production has overlapping policies, stale files, scanned PDFs, and users who must not see each other’s documents. Enterprise RAG fails when retrieval ignores access control, when nobody owns an evaluation set, or when “I don’t know” is not an allowed answer.
Saabsa’s RAG implementation work is that layer plus the product around it: an internal knowledge assistant, a document workflow, or a clinic or legal pattern we have already shipped.
Corpus and permissions
What is in the index, what is excluded, and how a user’s identity filters retrieval.
Retrieval design
Chunking, metadata, hybrid search where it earns its place, and citations the user can open.
RAG evaluation
Golden questions, unanswerable questions, and a regression check when the index or prompt changes.
The application
Not a naked chat box. A workflow with an owner, a fallback, and a cost cap.
How this connects to the rest of Saabsa
Lease Exit is the document-intelligence playbook: extract, rank, and produce an artifact a person can use. Patientree is the healthcare playbook when the corpus is clinical operations rather than a generic wiki. DataXPipe is the pattern when the failure is pipeline trust, not retrieval.
If the assistant must also take actions, pair this page with AI agent development. If you are still deciding whether the corpus is good enough to index, that decision belongs in the sprint, not in a six-month index project.
Questions buyers ask
Before you book the sprint
Is RAG the same as enterprise search?
Search returns documents. RAG drafts an answer from retrieved passages and should show where it came from. We implement both the retrieval and the answer path, and we say when search alone is enough.
Can you keep data in our tenant?
Yes. Private corpus, your cloud, and a written boundary on what is sent to a model API. That constraint is settled in the sprint.
Do you fine-tune instead?
Rarely as the first move. Retrieval, evaluation, and permissions fix more production failures than a fine-tune on a small corpus.
What do you need from us to start?
A document set with an owner, a handful of real questions, and clarity on who must not see what.
Related
The rest of the implementation map
AI implementation
Part of the same prototype-to-production path.
Prototype to production
Part of the same prototype-to-production path.
Enterprise deployment
Part of the same prototype-to-production path.
Production readiness
Part of the same prototype-to-production path.
AI agents
Part of the same prototype-to-production path.
Enterprise generative AI
Part of the same prototype-to-production path.
Evaluation
Part of the same prototype-to-production path.
Security and governance
Part of the same prototype-to-production path.
Healthcare AI
Part of the same prototype-to-production path.
AI Production Sprint
Part of the same prototype-to-production path.
Bring the prototype, not a wishlist.
The AI Production Sprint is two weeks and fixed fee. You leave with a go/no-go, architecture and security notes, and a next-step quote you can decline.