Evidence & reporting 

Claims Are Cheap. Here Is What You Actually Receive.

Two specimens below: the evidence pack an AI assurance engagement produces, and the output of the free diagnostic. Both redacted, both real in structure.

Specimen 01

Evidence Pack.

Issued after every assurance cycle and re-issued on any model, prompt or data change. It states what was tested, under which conditions, how often behavior held, and the part most vendors omit: what was not covered. It is written to be handed to a risk committee without translation, and it never tells you whether to ship. That decision yours, and the document says so.

EVIDENCE PACK · REDACTEDREL 4.2 · CYCLE 07
Scope & bounds

Claims-triage agent. Must classify within taxonomy, escalate on ambiguity, and never quote policy limits not present in the retrieved document.

Run summary
Condition Runs Within bounds
Standard cases 1,000 99.1%
Ambiguous cases 400 96.4%
Adversarial phrasing 400 91.8%
Long context (>40k) 200 88.5%
Named gaps
G-01 · Escalation missed under multi-turn pressure 23 / 2,000
G-02 · Limit quoted from prior turn, not document 9 / 2,000
G-03 · Non-English claimant names untested no coverage
Change since cycle 06
Adversarial band +2.3 pts
Long context band −1.1 pts
Evidence under tested conditions. Known gaps stated. Modenix does not certify, does not assert compliance, and does not make the release decision.
Specimen 02

Diagnostic Output.

What the seven-question diagnostic returns. The score and profile appear on screen, free, before we ask who you are.

GAP DIAGNOSTIC · SPECIMENINDEX 71 / 100
Profile

Verification debt: shipping materially more change than can be independently vouched for.

By dimension
Verification capacity shrank
AI code volume 40–70%
Regression cycle overnight
Escaped defects sometimes
AI feature grading in production
Recommended sequence
1· Readiness assessment 3 weeks
2· Rebuild regression, two highest-change services 60 days
3· Assurance baseline on the agent in production parallel
Scored on our assessment framework, built from 1,000+ engagements since 2002.
Specimen 03

Your Dashboard, Including the Bad Rows.


Our engineers' output lands in the view you already use, beside your own business-unit averages: issues closed, story points, PRs merged, rework rate, and escaped.

If someone on our side sits below your bar, you you see it before we tell you, and you see either the improvement trend or the replacement. It is an uncomfortable way to sell and the only claim here that cannot be invented.

Engineer signal, last 30 daysvs. your BU avg
Issues closed1.18×avg
Story points completed1.12×avg
PRs merged1.09×avg
AI-assisted volume1.34×avg
Rework rate (lower better)0.81×avg
Escaped defects1.04×avg
Illustrative. Live reporting wires to your Jira, GitHub, and adoption telemetry.
From the reviews

What Long Engagements Sound Like.

Summarized from independently verified reviews and attributed by role and sector. We do not publish client names. Here is how to verify anyway.

Engineers stayed on the same projects for years, long enough for real working relationships to form.

Senior Software Engineering Manager · Enterprise Software

Ramping up and ramping down repeatedly over several years, without friction in either direction.

QA Manager · Higher Education

Consistently high-quality output, and quick to scale when requirements moved.

Quality Lead · Life Sciences Software

Found real issues within four weeks of starting, met every agreed date, and kept proposing solutions rather than waiting for direction.

Founder · Security Software