Permissioned Enterprise AI Document & Knowledge Assistant
Customer support engineers and compliance teams spent hours manually searching through thousands of PDF technical manuals, product warranties, and policy documents.
60%
Faster document search & resolution
100%
Answers with page-level citations
0
Client documents used for model training
50-question
Golden benchmark gating every release
01 • The Challenge
What Was Actually Broken
Pre-Build Baseline
- Support engineers opened three or four PDF manuals to answer a single warranty or compliance ticket
- Compliance answers rebuilt from scratch each time, with no citation trail for auditors
- New support hires took weeks to become productive at finding the right document section
- Outdated document versions circulating in shared drives alongside current ones
A logistics SaaS company's knowledge lived in thousands of PDFs — installation manuals, warranty terms, policy documents, compliance addenda — accumulated over a decade of product iterations. Support engineers answered complex tickets by manually searching this corpus, often opening several documents to reconstruct a single answer, while compliance teams rebuilt sensitive answers from scratch each time because nothing pointed back to the authoritative page.
Three constraints ruled out a naive chatbot. Answers in this domain must be verifiable — every claim needed to land on an exact page of an exact document. Permissions had to mirror the existing role model: support engineers, compliance staff and partners see different document sets, and retrieval must respect that, not just hide citations in the UI. And client documents could never be used to train public models — a contractual and regulatory red line, not a preference.
02 • What We Shipped
The System, Piece by Piece
Engineered a private RAG assistant that indexes internal documents with strict role-based access controls, verifiable page citations, and zero training on public models.
Private retrieval-augmented generation
Documents are parsed, chunked with page offsets and embedded into pgvector; answers are composed only from retrieved passages — the model never answers from general knowledge when a source exists.
Role-based access at retrieval time
RBAC is enforced inside the retrieval step itself: a partner's query never retrieves passages from restricted document classes, even before any citation is rendered.
Page-level citations on every answer
Each claim carries a link to the exact page of the source PDF, so support and compliance verify in one click and auditors get a trail instead of a transcript.
Accuracy benchmark harness
A golden set of real questions with verified answers scores every model, prompt and index change before release — regressions block deployment rather than reaching users.
Automated ingestion
A watched-folder script parses new and revised PDFs, chunks and embeds them, and retires superseded versions — so the index tracks the document repository instead of drifting behind it.
Architecture & Decisions
Stack
Python and FastAPI for the retrieval and answer services, LangChain for document parsing and chunking pipelines, pgvector inside the client's existing PostgreSQL, OpenAI API with zero-data-retention controls for embeddings and generation.
Data controls
API calls run with training on client data disabled; documents and embeddings stay inside the client's environment; no document content is logged to third-party observability.
Evaluation in CI
The benchmark harness runs in the client's repository pipeline: retrieval and answer-quality thresholds must hold before any index, prompt or model change merges.
Repository & rollout
Built in the client's GitHub from day one with pull-request review and Friday demos; rollout staged from a compliance pilot group to full support engineering.
03 • Delivery
How the Work Ran
Fixed scope, weekly working software in your repository, and a handover your team can run without us.
See the full delivery processDiscovery sampled real resolved tickets to learn how experts actually find answers — the synonyms, the document families, the dead ends — and turned fifty of those questions into the golden benchmark set that gates every subsequent release.
Delivery ran as fixed-scope milestones with a working demo every Friday inside the client's repository: parsing and indexing first, then retrieval quality against the benchmark, then RBAC enforcement, then the answer interface. Accuracy thresholds had to hold on the benchmark before any group of users saw the assistant.
Handover included the accuracy benchmark report, a latency and answer-quality telemetry dashboard, the automated ingestion script for the client's PDF repositories, and recorded walkthroughs for the two people who would own it. A pilot with the compliance team ran for two weeks before support engineering got access.
04 • Outcome
Measured Results
Technical document search and resolution time fell by 60% — complex compliance questions that took an hour of document archaeology now take minutes.
Every answer arrives with page-level citations attached, giving compliance an auditable trail instead of a chat transcript.
The benchmark harness lets the team upgrade models, prompts and indexes with measured confidence — they can prove quality held rather than hope it did.
Onboarding a support engineer onto document search moved from weeks of osmosis to using the tool with the citations teaching them the corpus.
Shipped Outcome
60% reduction in technical document search and resolution time
"Support reps now verify complex compliance questions in seconds with exact source citations attached to every answer."
— VP of Engineering, Logistics SaaS
Handover & Runbook
Accuracy benchmark report, latency telemetry dashboard, and automated ingestion script for updating internal PDF repositories.
Related capability:AI Development
Representative write-up of delivery work of this type — client-identifying details are protected under mutual NDA, and figures describe measured outcomes from engagements of this pattern rather than a named client's public benchmark. References within your sector can be arranged during serious evaluation.
Questions
About This Engagement
Is this a real client project?
A representative write-up of delivery work of this type — client identity, document specifics and figures that could single them out are protected under mutual NDA. Named references within your sector can be arranged during serious evaluation.
Are our documents used to train the AI model?
No. API calls run with training on client data disabled, embeddings and document chunks stay inside your environment, and document content is excluded from third-party logging. This is enforced by configuration we hand over, not by promise.
How do you control hallucinations in compliance answers?
Three mechanisms: answers compose only from retrieved passages, every claim carries a page-level citation for one-click verification, and a 50-question golden benchmark scores each release so quality regressions block deployment instead of reaching users.
How are permissions enforced?
At retrieval time, not just in the interface — a user's role filters which passages can enter the candidate set before generation runs. A partner query cannot retrieve restricted document classes, and the behaviour is covered by tests in the repository.
What does running this cost?
The dominant costs are embedding and generation API calls, which scale with document volume and query volume — both visible in the telemetry dashboard from day one. Discovery produces a workload estimate before you commit, and the fixed scope covers the build; running costs are yours to watch, not ours to hide.
Want the full technical walkthrough? Request a case study walkthrough — arranged under mutual NDA with client consent.
Content last reviewed October 2026.
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