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AI Audits and Checkers That Drop Any Finding Without a Verbatim Quote

An AI audit whose findings only survive if a verbatim quote backs them — the `no quote, no finding` rule, built and verified in production.

For
SaaS teams and agencies
Checked

Sounds familiar?

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The situation AI agents & assistants
The tool told you three things were wrong, and when you opened the file, none of them were there.

Tick the lines that describe your case.

Describe my task

How I solve it

What I build

I build checkers and audits where a finding only survives if it can be shown word-for-word in the source. A deterministic scanner collects the raw evidence first; a model writes findings on top of that evidence; a verifier drops any finding without a verbatim quote; and a second, deterministic pass removes off-topic noise the model pulled in. The result is a tool that says less, but every line it keeps, you can point to.

This is the core of my own AI tracking audit. It was calibrated on real inputs, and the drop rule is what makes it usable rather than noisy.

The process

How it works

6 steps. Scope and a fixed price are agreed before the first one.

  1. Collect the evidence deterministically first: a bounded dossier of the exact text around each signal, with a hard size cap.

  2. Constrain the model to that dossier: a versioned prompt, a primary and a fallback model, retries and a daily spend cap.

  3. Require a verbatim quote: every finding must carry the exact source text it came from.

  4. Drop the rest: a verifier removes findings without a quote, and a deterministic backstop removes off-topic noise.

  5. Score honestly: a plain A–F score, and an explicit "needs connection" where the outside evidence needed to judge simply is not available.

  6. Report: findings with their quotes, plus what stays unknown and why.

Handover

What you get

  • A checker whose findings all carry a verbatim quote; anything else is dropped before you see it.
  • A deterministic evidence dossier built before the model runs, so the model is reading fixed text, not the whole world.
  • A versioned prompt calibrated on real inputs, a fallback model, retries and a daily spend cap.
  • An honest "needs connection" state instead of a guessed verdict.
  • A score and a report, not a wall of unfalsifiable claims.

Built with

  • Deterministic scanner
  • an evidence dossier builder
  • a versioned model prompt with a primary and a fallback model
  • a verbatim-quote verifier with a deterministic backstop
  • a scored report
  • automated tests at each gate
  • On my own build this runs on TypeScript
  • Fastify
  • Prisma/PostgreSQL
  • ClickHouse
  • a Next.js backend and vitest

Proof

Done before, with dates and numbers

Own product Built and run by me, in production.

Before → after

Before, one pipeline metric read 0% on 58 of 58 stores because it was built as a subtraction, and the analyst could return findings with no quote behind them. After, the metric was rewritten and every finding must carry a verbatim quote, with the prompt calibrated on 26 live public stores. That build ran from 2026-08 to 2026-09.

  • Fixel Pixel AI Tracking Audit

    in production

    the no quote, no finding rule: a deterministic evidence dossier, an LLM analyst and a verifier that drops any finding without a verbatim quote, calibrated on 26 public stores.

  • Fixel Pixel 2.0

    in production

    the tracking pipeline the audit reads from: server and browser delivery with a receipt per event, reconciled against orders.

Client words

No client words here. My quotes are from tracking and automation work, not from AI audits, and I will not stretch one to cover this page.

What you can look at

A redrawn pipeline — deterministic scanner → evidence dossier → AI analyst → verbatim-quote verifier, with the deterministic backstop beside it → report — with the drop step shown. Built on a synthetic store; no client data appears in it.

This is my own product, not a client engagement. Figures are counts and pipeline behaviour, not client results. I am the founder of Fixel Pixel.

Price and timeline

What it costs and how it runs

A checker like this is a Custom Engineering Project, scoped and quoted in writing after we agree the evidence rule. The scope is small on purpose: the interesting work is the rule and its backstop, not the model. If a single deterministic check already answers your question, I will say so and build that instead.

QuoteFixed before work starts

Price
Custom Engineering Project from USD 1,500, scoped and quoted in writing after we agree the checker's evidence rule. Discussing the task is free.
Timeline
Custom Engineering Project: scoped and quoted in writing. The evidence rule and the deterministic backstop are agreed before any model is wired in.
First step
Describe the task. I reply within one working day, free, and tell you which option fits — or that you can fix it yourself.
Describe a task like this

A note from Daniilbefore you decide

When you don’t need this

If findings are few and checkable by hand, or a human already reviews every line, the verifier is overhead. It pays off when the source is large — hundreds of pages, events or feed rows — and a wrong finding costs you trust. If you would rather the model move faster by skipping the quote step, this is not that; the drop step is the product.

— Daniil

Questions

What people ask about this

Does the AI decide anything on its own?

No. It writes findings over a fixed evidence dossier, and a verifier drops whatever is not backed by a verbatim quote. The rule is the system, not a prompt instruction to be clever.

What happens when no quote exists?

The finding is dropped, not softened. If the evidence to judge is missing, the report says "needs connection" instead of guessing.

Will a strict rule miss real issues?

Sometimes, yes. The rule trades recall for trust on purpose. For checks that need full recall, a deterministic rule is added beside the model rather than loosening the quote gate.

How do you keep cost and drift under control?

A versioned prompt, a primary and a fallback model, retries, a daily cap, and a deterministic filter for off-topic noise. Cost per run is a scoping detail, not a claim.

Can it run on our own data and keep it in-house?

That is a scoping question. The pattern works with a hosted model or a self-hosted one; where your data is allowed to go gets decided with you before anything is built.

More like this

All situations
Daniil Maximkin

Hi, I’m Daniil.

I work with you from defining the problem to implementation and handover. You talk to the person who does the work. I work in English and Russian.

My story My work

Have a task like this?

Describe your task

The first answer is free, within one working day. Or write directly: next@taskfordaniel.com