01 / EVIDENCE CHAINS
Every claim has a trail.
Connect conclusions to exact passages and source locations. Keep the original evidence close enough to inspect, challenge and revisit.
Exact quotations · Source identity · Claim dependencies
THE TRUST LAYER FOR AI
Trust,
built into AI.
Give your AI answers that people can inspect, reasoning they can verify, and evidence they can follow.
Evidence chains and logic verification for AI companies and developer teams.
Your models. Your product. A stronger foundation of trust.
Documents
Data
Tool outputs
01Source evidence
| Period | Q4 2024 | Q4 2025 |
|---|---|---|
| Revenue · USD | 1,000,000 | 1,250,000 |
02Recomputed from the source values
(1,250,000 − 1,000,000) ÷ 1,000,000= 25%Same period · Same currency
Calculation checked
Two source values · One reproducible derivation
Evidence → Logic → ProofCitProof
Built for the builders
- AI assistants
- Autonomous agents
- AI analytics
- Knowledge platforms
A FOUNDATION FOR RELIABLE AI
Trust has a structure.
Connect what your AI says to the evidence and logic that support it.
A path from an answer to its sources, from a conclusion to its premises, and from a question to the evidence still needed.
02 / LOGIC VERIFICATION
Reasoning you can replay.
Make the path from facts to conclusions explicit. Derive results through rules, then check the proof independently of the process that produced it.
ASP-based derivation · Proof traces · Independent checks
03 / TARGETED DISCOVERY
Know what to look for next.
Work backwards from a question to its missing premises. Direct the next read towards evidence that could change the answer, then revisit the reasoning.
Goal-driven search · Missing premises · Re-evaluation
04 / REUSABLE KNOWLEDGE
Keep the knowledge. Keep the proof.
Bring evidence, confirmed facts and reviewed rules into a reusable knowledge layer. Preserve their dependencies so conclusions can be checked as information changes.
Evidence-backed facts · Versioned rules · Traceable reuse
FOLLOW THE REASONING
A missing fact becomes a next step.
An AI agent wants to approve a refund. Follow the evidence to see what supports the decision—and what can change it.
Try the example. Add the missing evidence, then introduce an exception.
The conditions behind the decision
Can this refund proceed?
Refund policy01
Refunds are allowed within 30 days when the item is unopened and is not marked final sale.
Order record02
Purchased 12 days ago. Package unopened.
Final-sale statusNot yet read
Read the full example records+
The illustration uses a complete three-condition policy. Changing the final-sale field recomputes the displayed result in your browser.
Within 30 days
Unopened item
Not a final-sale item
Decision traceNeeds evidence
Hold for evidence
Read the final-sale field on the order record before approving this refund.
One premise is still open.
FOR AI COMPANIES
Your AI product.
With trust built in.
Make verification part of the experience you deliver. We work with your team to connect evidence and reasoning checks to the workflows that matter.
YOUR PRODUCT
AI answers
Agent decisions
Analytical findings
THE TRUST LAYER
CitProof
- Evidence chains
- Reasoning checks
- Evidence discovery
BACK TO YOUR PRODUCT
Inspectable sources
Checkable derivations
Unresolved questions
A shared verification approach across models, retrieval systems and agent workflows.
AI assistants & RAG
Let users follow an answer all the way to the passage that supports it.
Agents & automation
Expose the rules, conditions and exceptions behind a proposed action.
AI data analysis
Connect findings to input values, calculations and the assumptions behind them.
RESEARCH FOR THE LONG TERM
Models will change.
The need for trust won’t.
CitProof is an AI trust research and technology company. We study how AI can justify its answers, test its reasoning and discover the evidence it still needs.
Our purpose is enduring: make trust a capability that AI products can build on. We turn that research into evidence and verification services for the teams creating them.
Evidence before confidence
An answer should carry a path to what supports it.
Discovery with verification
New ideas become useful knowledge when their evidence and reasoning can be examined.
Knowledge that compounds
Retain the facts, rules and proof that make the next answer stronger.
WORK WITH CITPROOF
Start with one important workflow.
Bring us the part of your AI product where trust matters most. Build from a concrete question to an integration your team can evaluate.
A technical collaboration shaped around your product, data and users.
Trust integration
Connect source evidence, reasoning checks and reviewable results to your AI workflow.
Verification evaluation
Build representative cases and examine unsupported claims, reasoning failures and the effort needed to review an answer.
Research collaboration
Explore evidence discovery, verifiable reasoning and reusable knowledge with us.
- 1
Bring a workflow
Choose the question, evidence and outcome that matter.
- 2
Build and evaluate
Connect a focused example and test it against your cases.
- 3
Integrate and improve
Shape the verification experience around your product.
A few practical questions.
Who is CitProof for?+
AI companies and developer teams building assistants, agents, analytical tools and knowledge products. We help them add evidence and reasoning verification to the AI experiences they deliver.
How do evidence chains go beyond citations?+
A citation points to a source. An evidence chain connects a specific claim to its exact supporting content, the facts used in a derivation and the rules that lead to a conclusion. This makes the path inspectable and the relevant checks repeatable.
What does the logic layer do?+
It makes conditions, dependencies and exceptions explicit. Our approach combines ASP-based derivation with independent proof checking, and uses goal-driven evidence discovery to identify which missing premises matter next.
How do we begin?+
Start with a technical conversation about one workflow. Bring representative inputs, outputs and examples of where trust breaks down. Together we define the checks, evaluation cases and integration scope.
BUILD WITH US
Make trust part
of what you build.
From the first source to the final answer, give your AI product a foundation that people can examine.
Explore the trust layer