01
Evidence in
Inputs
- Codebases and Git APIs
- Tickets and change records
- Specifications and design documents
- Engineering discussions
Intent infrastructure for software systems
Software records what changed. UTNIR helps establish intent.
Connect codebases, tickets, specifications, and engineering discussions. UTNIR works in the background to produce evidence-backed intent headlines for human review and certification.
What goes in. What comes out. What it unlocks.
From fragmented engineering resources to establishing epistemic truth of your system.
See the whole exchange at a glance: the evidence UTNIR receives, the knowledge it produces, and how that governed knowledge keeps working after the analysis is complete.
UTNIR Intent engine
[No data leaves your organization] 02
Knowledge out
Outputs
- Intent headlines for review
- Evidence and provenance
- Human certification
- System of record and APIs
03
Operationalize the record
How do you use outputs?
Map, guardrails & context APIs for AI agents
Give agents governed systemic intent, uncertainty, provenance, and context in a structured way instead of asking them to reconstruct understanding from raw repositories.
Team continuity and onboarding
Let engineers and new hires understand verified systemic intent without depending on perfectly maintained dynamic specifications—reducing ambiguity as systems and teams evolve.
Reverse the intent-ambiguity tax
Operationalize Polanyi’s paradox: move from “we can know more than we can tell” toward an evidence-backed UTNIR operating principle—Tell more, if you know more.
Governance and executive visibility
Give leads, managers, governors, and executives a durable view of how low-level intent propagates through systems without requiring them to reconstruct meaning from implementation details.
UTNIR stands between fragmented evidence and reusable organizational knowledge: UTNIR reasons over the evidence, the evidence stays visible, humans certify, and the resulting record remains available to people and APIs.
The product
A reviewable answer, not another AI summary.
UTNIR, as an autonomous background engine, reasons about intent to produce intent headlines for human review, while preserving supporting evidence, uncertainty, and human decisions as a governed record of the system’s epistemic truth.
Pending finding
System change summary
Executive summary
A change in system is detected. UTNIR presents the proposed intent together with the evidence and uncertainty for human review.
What changed
Describes the change that is detected in the system.
What this means
UTNIR presents the proposed intent for human review.
Impact and uncertainty
Available evidence and uncertainty remain visible during review.
Related context
Relevant evidence and organizational context remain attached to the finding for review.
Human review
Accept, correct, or reject the proposed intent before it becomes part of the governed record.
Useful for
Useful when intent becomes expensive to recover.
AI-generated code creates cognitive tax
Verify that intent was correctly implemented without reconstructing it manually.
Knowledge disappears over time
Preserve intent when context fades, people leave, or systems change hands.
AI agents need context and guardrails
Give agents access to the intent, evidence, and constraints behind a system.
Complex systems accumulate intent ambiguity
Keep the reason behind a system recoverable as complexity grows.
Leadership needs to know the intent graph today
See how intent, decisions, and system changes connect across the organization.
Request access
Bring the system where Intent is hardest to recover.
Tell us what you want to understand. We will use your submission only to respond about UTNIR and early-access fit.
Prefer email? utnirhq@gmail.com