AI knowledge assistants that answer from your documents, with sources
Ask a question in plain English and get an answer drawn from your own policies, contracts and case files. Every answer links to its source, and every user sees only what they're allowed to.
- Built by the team behind our claims document AI
- Accuracy tested before launch
- Runs in your cloud: AWS, Azure or Google Cloud
Your answers exist. Your team just can't find them fast enough.
- Staff spend hours searching shared drives, policy manuals and old emails.
- Public chatbots make things up and can't be trusted with confidential files.
- Previous AI pilots looked good in a demo, then gave wrong answers on real questions.
- The people who know how things work are retiring or leaving, and the knowledge goes with them.
Built for the questions your teams ask every day
Claims
What does our guideline say about reopening a closed claim?
Example replyAnswered with sources: Claims guidelines, section 4.2
HR and payroll
How do we handle a retroactive pay change for a union employee?
Example replyAnswered with sources: Payroll procedures manual, page 18
Legal and compliance
Which of our contracts have an auto-renewal clause?
Example replyAnswered with sources: 14 contracts, listed with clause references
IT and engineering
How does the nightly batch job in the old claims system decide what to import?
Example replyAnswered with sources: Batch job source code and the 2019 runbook
Planning a modernization? A knowledge assistant over your legacy system's code and documents captures how it really works before you rebuild it. Legacy application modernization
How we build an assistant you can trust
A reference architecture technical teams can check, step by step.
Prepare your documents
PDFs, Word files, emails and scans are converted, cleaned and split into passages. Personal data is masked where needed.
Index them two ways
Keyword search finds exact terms and policy numbers; semantic search finds meaning. Where facts connect across documents, a knowledge graph links them.
Retrieve the best passages
Results from both searches are merged and re-ranked, and filtered by what each user is allowed to see.
Answer with citations
Plain-language answers, every claim linked to its source, and “I don't know” when the documents don't support an answer.
Test and monitor
Accuracy scored on your team's real questions before launch; accuracy, response time and cost per question tracked after.
Built so wrong answers can't hide
Every answer is cited
People can check it in one click.
It refuses rather than guesses
When the documents don't cover the question, it says so.
Permissions are enforced
At the search layer, so no one sees a document they couldn't open anyway.
Your data stays in your cloud
With personal data masking and encryption as standard.
Accuracy is measured before launch
Not discovered by users.
Built by engineers who've done this in production
- Team experience
Our AI engineers have built retrieval systems for legal teams handling tens of thousands of documents a month, with answers in under a second.
- Team experience
We test AI features against real questions and score accuracy before every release, a practice our team has run across dozens of production AI applications.
- Integriti result
Our claims document AI links every extracted value to its source page, inside a live claims platform.
Your cloud, your model. Chosen for accuracy, cost and where your data must stay. No lock-in to one provider.
- AWS
- Azure
- Google Cloud
- OpenAI
- Anthropic
- Mistral
- Open-source models
See it answer from your own documents
A 2 to 3 week pilot: we load a sample of your documents, build a working assistant, and measure how many of your team's real questions it answers correctly, and at what cost per question. Fixed scope and fixed fee, agreed before we start and credited toward the full build.
AI knowledge assistant FAQ
What is RAG?
Retrieval-augmented generation. Instead of answering from general training, the AI first finds the most relevant passages in your documents, then writes its answer from them, with citations.
How is this different from ChatGPT?
It answers only from your documents, shows its sources, respects your permissions and runs in your own cloud account.
Will it make things up?
It's designed not to. Answers must be supported by retrieved passages, and it says so when they aren't. We measure this before launch.
Which systems can it read from?
Common document stores, shared drives, databases and business applications through their APIs. We confirm your sources during the pilot.
How long does it take?
A pilot takes 2 to 3 weeks. A production rollout depends on document volume, sources and security reviews.
What does it cost to run?
We track cost per question from day one and route simple questions to lower-cost models, so running costs stay predictable.
Bring the questions your team asks most
In a 30-minute call with our Head of AI and our CEO or CTO, we'll look at your documents and the questions your team keeps asking, and show you what an assistant would answer.
- A live assistant answering sample questions, with citations
- How permissions and personal data masking would work for you
- What a pilot on your documents would measure