Know where it came from.
Connect proposed values and claims to their underlying customer statements, records, and reviewable evidence.
Evidence infrastructure for AI systemsConfidence
Confidence
needs evidence.
AI can produce convincing answers. Filomorph helps engineering teams inspect the claims behind them—trace sources, evaluate constraints, and make uncertainty visible before it becomes an action.
Built for engineering teams moving from AI prototypes to accountable workflows.
FILOMORPH / EVIDENCE INSPECTOR
Customer source · Turn 03
“Actually, change the quantity to 36 short tons.”
01 · CLAIM?Proposed
02 · SOURCE↗Traced
03 · CHECK
Supported
Supported✓
Quantity independently reconstructed
Current value: 36 short tons
Supersedes: 24 short tons · Source: customer turn 03
Field-level evidence · Synthetic illustrationSUPPORTED ✓
Designed aroundSource traceabilityIndependent checksExplicit uncertaintyHuman oversight
THE FILOMORPH MARK
See the approach From assertion to evidence.
Two inputs converge on one independently checked conclusion. Our mark reflects the principle behind Filomorph: a claim earns its check—not the other way around.
THE PROBLEM
Fluent isn't the same as correct.
Most AI systems are optimized to produce an answer. In business-critical workflows, the harder question is whether that answer is justified by the information and rules available.
Separate claims from checks.
Evaluate operational constraints independently of the AI that proposed an action or answer.
Make uncertainty visible.
Show unresolved and contradictory evidence rather than turning an unsupported inference into a green checkmark.
THE APPROACH
From generated output to inspectable evidence.
Filomorph explores an assurance layer between AI-generated proposals and the systems that depend on them.
01
Generate a candidate
An AI model proposes a structured interpretation, claim, or draft.
02
Check independently
Separate components examine available source evidence, permitted values, and applicable rules.
03
Surface the boundary
Reviewers see what's supported, what's unresolved, and which claims require a decision.
Assurance previewIllustrative synthetic scenario
Customer request
“Prepare 24 short tons of River Gravel for delivery to East Yard.”
AI-proposed field
Requested date: October 20
Unresolved: no date requested
The date may be operationally valid, but the customer's words do not establish it.
Interactive illustration based on the synthetic Order Assurance workbench. Not a claim of production accuracy or universal language understanding.
WHERE IT FITS
Built for the moments when an AI answer matters.
We're exploring pilot opportunities with B2B software teams that need greater visibility into AI-assisted decisions and workflows.
AI workflow automation ↗
Inspect structured actions before a person approves them or a downstream system relies on them.
Operational copilots ↗
Trace proposed business values to conversation history and trusted operational constraints.
Claim verification ↗
Distinguish formally checkable rule implications from claims that still require evidence or human judgment.
A DIFFERENT KIND OF ASSURANCE
Not every green check means the same thing.
Filomorph distinguishes source reconstruction, operational validation, and formal rule verification. Each establishes something different. None should be presented as proof of more than it actually checks.
That separation is central to our approach: transparent evidence, inspectable limitations, and defensible boundaries.
Assurance layers / Research prototype
Operational constraintsCHECKED
Conversation-grounded fieldsINSPECTABLE
Restricted formal rule claimsPROVABLE IN SCOPE
General human intentNOT FORMALLY PROVEN
EARLY PILOT CONVERSATIONSMake AI decisions
Make AI decisions
more accountable.
We're speaking with engineering leaders about where independent verification could make AI-powered software safer to operate and easier to trust.
Direct line to the founder · No sales funnel