In short
Your documentation, tickets, contracts and wikis already contain most of the answers your team asks for. A knowledge system makes them answerable: retrieval-augmented generation over your own material, with citations, so people get the right paragraph instead of a confident guess.
Deliverables
What you get.
Everything listed here is a thing you can hold: a document, a system, a working piece of software. Not hours, not effort.
- 01Ingestion pipelines for your real formats: docs, PDFs, tickets, wikis, email archives
- 02Chunking and embedding strategy tuned to your material, not a default config
- 03Retrieval quality evaluation, measured against questions your team actually asks
- 04An answer layer that cites its sources and refuses when the evidence is thin
- 05Permission-aware retrieval, so people only get answers from documents they may see
- 06The interface your team uses daily: search, chat or embedded inside your tools
Capabilities
What we can take on.
The concrete jobs this service covers. Yours is not listed? Ask anyway: the honest answer is often a sibling service.
- A company knowledge base an AI can answer from
- Document search that finds the thing you meant, not the word you typed
- Onboarding wikis new hires actually read
- Support libraries that deflect repeat questions
- Content models that keep knowledge structured as it grows
Process
How we work.
Four stages, one line. The order never changes; the depth does.
- 01Collect real questionsWe start from the questions your team already asks, which become the evaluation set.
- 02Ingest and measureRetrieval quality is scored before any interface is built.
- 03Build the thinnest useful interfaceSearch box, chat or in-tool panel, whichever your team will actually open.
- 04Tune against missesEvery wrong answer in testing becomes a fix in chunking, retrieval or the refusal logic.
The working model
The harness, drawn.
Four stages, narrated, with captions. Sound is off until you ask for it.
Why Web Fortuners
Three reasons, checkable.
- 01
We measure retrieval quality instead of demoing it
You see the score against your own questions before launch.
- 02
Citations are non-negotiable here
A system that cannot show its source does not ship.
- 03
Design and engineering sit in one studio
The system is built to be both accurate and something people choose to use.
FAQ
Questions, answered.
The things people ask before they write to us. Yours is not here? Ask it directly.
It is the normal starting point. Ingestion includes cleanup, deduplication and structure detection, and the evaluation phase tells you honestly whether your material can support reliable answers yet.
Every answer is built from retrieved passages and carries citations back to the source document. If retrieval finds nothing solid, the system says it does not know instead of guessing.
Inside infrastructure you control. We design for your compliance constraints first, and nothing is sent to a model provider without a documented, reviewed data path.
Fixed scope, quoted in writing. The main cost driver is ingestion complexity, which we assess from a sample of your real documents before quoting.
Proof
The pillar at work.
No case study credits this exact service yet. These credit its siblings in the AI pillar: same studio, same standards.
AI pillar
Agents that do, not chat.
Models put inside the workflow, with the harness, the evaluations and the human checkpoints that make them safe to run in production.
- 01AI AgentsAutonomous agents that execute real work, not chatbots
- 02Agent HarnessesOrchestration, tooling, evals and guardrails for agents at scale
- 03API Integration & ImplementationLLM APIs, MCP servers and system integration done right
- 05AI Strategy & EvaluationAdoption roadmaps, evals and safety for teams going AI-first
- 06Workflow AutomationEnd-to-end business processes automated with AI in the loop
- 07Conversational AgentsChat and voice agents that answer, qualify and book, around the clock