Knowledge Intelligence (RAG)

knowledge assistant — internal

Does part 8812-C meet the rev 4 spec? Does 8812-C meet rev 4?

grounded answer3 sources · 0.4s

Yes — under the current revision. Rev 4 tightens shaft tolerance to ±0.05 mm¹, and the vendor catalog lists part 8812-C at ±0.03 mm², inside the limit. Note that Rev 4 superseded Rev 3 on 18 Mar 2026³.

Sources 1spec-rev4.pdf · p.12 2vendor-catalog.xlsx · row 214 3change-notice-0318.pdf

Illustrative loop. Production assistants are configured to your corpus, your vocabulary, and your access rules.

Capabilities

From archive to answer engine

Configured to your corpus, your vocabulary, and your access rules — not a generic chatbot with your logo on it.

Document indexing

PDFs, drawings, spreadsheets, and email archives — parsed, chunked, embedded, and kept current in a private search index. Scanned paper included, via OCR.

Semantic search

Search by meaning, not keyword roulette. "Early-termination penalties" finds the clause even when the contract never uses those exact words.

Internal assistants

Managers ask in plain language and get the contract, proposal, spec, or project record in seconds — instead of a folder dig or a reply-all.

Regulatory & policy search

Compliance requirements, safety manuals, and procedures become queryable — so "where does it say that?" gets the exact paragraph in the exact revision.

Answers with citations

The assistant composes its answer from retrieved passages and attaches file-and-page references to every claim. No source, no sentence.

Permission-aware access

Access rules travel with every query. People retrieve only from documents they are already allowed to open — the index never becomes a side door.

The corpus

What it indexes

Point it at the archives you already keep. We connect sources during onboarding; continuous ingestion keeps the index current after that.

ContractsTerms, parties, renewal dates

SpecificationsRevisions, tolerances, requirements

Vendor catalogsParts, pricing, lead times

Policy & procedure manualsHow it's done, in writing

Regulatory documentationThe rules you answer to

Project recordsDecisions, budgets, closeouts

Meeting notesWho decided what, and when

Historical proposalsWhat you quoted, what won

PDF — native PDF — scanned, OCR DOCX XLSX PPTX EML / MSG PST archives CSV HTML Markdown TXT TIFF / PNG drawings

Same files need field-level extraction or three-way matching? That's Document Intelligence — the two systems deploy well together.

Grounded by design

How it stays honest

Retrieval-augmented generation is a design discipline, not a magic setting. Four rules keep the assistant grounded.

Retrieval first

The model answers from passages retrieved out of your indexed corpus — not from its general training data. If it's not in your documents, it's not in the answer.

Citations, mandatory

Every answer names the file, page, or row it came from. A claim you can't click through to a source is treated as a bug, not a style choice.

“I don't know” is allowed

When retrieval comes back thin, the assistant says so and shows the nearest match instead of improvising. A visible gap beats a confident guess.

Human spot-review

During operation we sample real queries and grade answers against their cited passages, tuning retrieval wherever evidence ran thin. Autonomy is earned, not assumed.

When the corpus can't answer, neither will the assistant.

Thin evidence produces a decline, a pointer to the nearest match, and a route to a person who can actually answer. That behavior — and the data controls behind it — follows the same commitments we publish for every AI system we operate.

Read our Responsible AI commitments

assistant — low-evidence query

you        Which vendors are approved for
           cold-chain packaging?

assistant  I can't support an answer from the
           indexed corpus — no passage clears
           the evidence threshold.

           nearest match   vendor-catalog.xlsx
                           (ambient packaging only)
           action          routed to procurement
           fabricated      nothing — by design

knowledge api — sample

# permission-aware query — answers return their sources
POST /v1/knowledge/query
{
  "index": "yourco-corpus-prod",
  "actor": "ops-lead@yourco.com",
  "query": "notice period — msa 2025-014",
  "require_citations": true
}
→ 200 · answer + 2 citations · scope: docs the actor may read

Deployment

A private index, on infrastructure we operate

Your corpus is chunked, embedded, and stored in a private index on our own cloud — not scattered across third-party trial accounts. Enterprise API integrations put answers inside the tools where questions actually come up, and access rules travel with every query.

Private indexembeddings and documents stay on our US-based cloud — Secure AI Hosting
Agent-readyautonomous agents can use the same governed index as a tool — Autonomous Agents
Run in productionmonitored, validated, and supported 24/7 — AI Ops & Security

Questions

The skeptic's section

What stops it from making things up?

Design, mostly. We build on commercially available foundation models from established providers, including OpenAI — but constrained: the assistant synthesizes only from passages retrieved out of your corpus, every answer must cite its sources, and thin evidence produces a decline instead of a guess. During operation we spot-review real queries against their cited passages. No vendor can honestly promise zero errors; this design makes errors rare, visible, and easy to check.

Where does our data live, and who can see it?

In a private index on our US-based infrastructure. We practice data minimization — the index holds what retrieval needs and nothing more — and your access rules carry into query time, so people only retrieve from documents they are already permitted to open. Customer data is never used to train public AI models without your explicit authorization. Full detail on our Responsible AI page.

How current is the index?

Continuously current. Connected sources sync on the cadence you choose — near-real-time for active repositories, nightly for slow-moving archives. New and edited documents are re-indexed automatically, and deletions propagate: a document removed at the source stops being retrievable and citable.

What file formats does it handle?

PDF — native and scanned, via OCR — plus Word, Excel, PowerPoint, email archives (EML, MSG, PST), CSV, HTML, plain text, and Markdown. Drawings and images are indexed with OCR and vision models where the content supports it. An unusual format isn't a dealbreaker: bring samples to the consultation and we'll test them during scoping.

Knowledge intelligence

Your company already wrote the answers.

We make them findable — with sources attached. Bring the three questions your team keeps re-answering from memory; we'll scope the corpus, the connectors, and the rollout in one consultation.

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