Answers from the knowledge your company already owns
An AI knowledge system lets your team ask questions in plain language and get answers grounded in your own material — equipment manuals, SOPs, past tickets, engineering notes, contracts — with citations back to the source. It removes the bottleneck where the answer exists somewhere in twenty years of files, but the only search that works is asking the one person who remembers.
Our approach is retrieval-first, not fine-tuning: documents are indexed and the relevant passages are fed to the model at question time, so answers cite sources, content updates take effect immediately, and nothing about your data gets baked into a model. Access control is enforced at retrieval, so people only get answers from documents they are allowed to read.
AI & Intelligent AutomationHow the work gets done
The same way every time: scope, build, hand over.
Quality is decided in the retrieval layer. That means chunking documents in ways that respect their structure, hybrid search combining keyword and semantic matching, reranking, and handling the ugly realities — tables, revision histories, near-duplicate documents, drawings. Ingestion runs as a pipeline syncing from your sources on a schedule, so the index tracks the truth instead of a snapshot.
Before launch we build a golden set of real questions with known correct answers and score the system against it; after launch, user feedback and unanswered-question logs drive tuning. The operational measures are simple: are answers cited, are they correct on the golden set, and are people actually asking — usage is the honest metric.
Scoping starts with the questions, not the documents. We collect the questions people actually ask, from ticket logs, chat history, and a few hours with the people who field them, and use those to decide which sources matter and in what order. A system that indexes everything on day one usually answers poorly, because stale drafts and duplicates outvote the current version. The trade-offs we weigh are breadth against precision, freshness against ingestion cost, and how much structure to impose on sources that have none. We prefer a narrow, well-curated corpus that grows as the golden set proves each new source earns its place.
The common failure after launch is trust erosion: a few confidently wrong answers early on, and people stop asking. We design against that by refusing to answer when retrieval finds nothing solid, by showing the source passage next to every claim, and by making it easy to flag a bad answer. Flags feed a review queue that your document owners work, since most bad answers trace back to a bad or outdated document rather than the model. At handover your team gets the ingestion pipelines, the golden set with its scoring script, and a runbook for adding a source, so the system keeps up with the company.
Scope it in writing
What we agree before work starts
- Ingestion pipelines syncing from your document sources with change detection
- Hybrid retrieval index tuned on your actual content
Build with checkpoints
Working results, not slide decks
- Question-answering interface with citations to source passages
- Permissions-aware retrieval mapped to your access controls
Hand over something you own
Documentation, source, and training
- Golden question set with accuracy scoring, run before and after launch
- Usage and unanswered-question reporting
Sound familiar?
Where ai knowledge systems earns its keep.
A maintenance tech troubleshooting a machine at 2 a.m. with the answer buried in a 900-page manual
A new hire who needs six months of shoulder-tapping compressed into a search box
A support team answering the same 50 questions from a knowledge base nobody can search
An engineering team re-solving problems already documented in old project folders
Ask about AI Knowledge Systems
Describe the problem. Get a straight answer.
One line is enough. An engineer replies within a business day.
Related work
AI operating software for training operations
Components:
- Schedulers (training ops): The people running the training operation.
- Operating software (AI assistance): The operating software, with AI assistance built in.
- Cloud services (distributed): Distributed cloud services behind the application.
- Training game (learning): The training game the software connects to.
Connections:
- Schedulers to Operating software
- Operating software to Cloud services
- Operating software to Training game
A global automotive manufacturer's training operation · Automotive
AI operating software for training operations
AI operating software for the manufacturer's training schedulers — removing manual scheduling labor and logistics tracking, saving hundreds of hours per year — plus a training video game built to help trainers perform better. Used across the manufacturer's training organization.
Read the case study →Common questions
Asked before every ai knowledge systems project.
How do you prevent the system from making things up?
Grounding and honesty about limits. The model answers only from retrieved passages and cites them, answers without supporting sources are flagged or refused, and the golden question set measures hallucination directly. Citations matter most — they let the reader verify in one click.
Does our data stay private?
Your documents live in your index, in your environment or cloud tenancy, and retrieval respects existing permissions — an answer never quotes a document the asker cannot open. Model API calls run under commercial terms that exclude training on your data, and stricter setups can use models you host.
How does it stay current as documents change?
Ingestion is a pipeline, not a one-time load. It watches your sources — SharePoint, drives, wikis, ticket systems — and re-indexes what changed, so a revised SOP is answerable the same day. Stale-document reporting also surfaces cleanup your document owners have been meaning to do.
Can it answer from drawings, tables, and spreadsheets, or only text?
Tables and spreadsheets work well when ingestion preserves their structure, which is a large part of what we tune; a torque value in a table is only useful if the row and column labels come with it. Drawings and diagrams are harder. Title blocks, notes, and callouts are extractable, but interpreting geometry from an image is not something we would promise. Where it matters, we index the drawing's metadata and link to the file.
How much cleanup do our documents need before this works?
Less than most teams fear, and the system tells you where cleanup would pay off. Ingestion handles messy formats, duplicates, and revision chains as a matter of course. What hurts is contradictory content, such as two SOPs that disagree with no indication which is current. The golden set surfaces those conflicts early, and the stale-document reporting gives your document owners a prioritized list rather than a mandate to clean everything first.
Where this sits
AI Knowledge Systems, inside a ai & intelligent automation system.
The lit component is the part of the system this service delivers; the rest is what it has to work with.
Hover or focus a component to see what it is and what it talks to. Arrow keys move between them.
Inbound documents and messages are ingested, an agent reasons with company knowledge and acts through the systems of record, and a person reviews the cases that need judgment.
Components:
- Inbound (email, PDFs, forms): The unstructured work arriving every day.
- Ingestion (extract, classify): Turns documents into structured fields with confidence scores.
- Agent (reasons, uses tools): A model with tools: it looks things up, decides, and acts — within limits you set.
- Knowledge (your docs): Company procedures and history, retrieved on demand.
- Systems of record (ERP, CRM): Where the work actually lands.
- Reviewer (exceptions): The person who sees what the agent was unsure about.
Connections:
- Inbound to Ingestion over email
- Ingestion to Agent over events
- Agent to Knowledge over REST, both directions
- Agent to Systems of record over REST
- Agent to Reviewer over handoff
Strategy. Software. Systems.
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Tell us what your operation is doing manually, what isn't connected, or what you're trying to build. We'll tell you plainly whether and how we can help.