Product / Logres Intelligence

Logres Intelligence.
Measure the answer. Improve the evidence.

The AI visibility intelligence and remediation system. See how AI assistants name, cite, and describe your organization, understand why competitors appear instead, and fix the source evidence shaping the answer.

Get your baselineSee the methodology

Not rankings. Recommendation intelligence.

Baseline resultIllustrative

Buyer question

Who is best qualified to help with this decision?

Evidence captured

Named organizations, cited sources, and answer language.

Dated

Visibility

Authority

Accuracy

Influence

Measured across the assistants buyers use

  • ChatGPT
  • Claude
  • Gemini
  • Perplexity
  • Copilot
Three outcomes

The recommendation layer, made inspectable.

  1. /01

    Find where you disappear

    Know when your organization is named, omitted, cited, or described incorrectly across the questions buyers actually ask.

  2. /02

    Understand why competitors win

    Trace the sources, claims, pages, and third-party evidence AI systems use when forming a recommendation.

  3. /03

    Fix and re-test the evidence

    Turn findings into grounded website and authority improvements, verify implementation, and measure movement over time.

01 Measure

See your market as AI presents it.

Logres runs the questions real buyers ask across leading AI assistants, recording who gets named, which sources get cited, how each organization is described, and whether the answer supports consideration.

Illustrative visibility × authority view

AuthorityVisibilityYouAB

  1. Test real buyer questions

    Build prompt groups around services, locations, problems, comparison questions, and high-intent decisions—not generic keywords.

  2. Compare the competitive answer

    See the organizations AI systems recommend instead of you and how consistently each competitor appears.

  3. Score the complete position

    Measure four separate dimensions: Visibility, Authority, Accuracy, and Influence.

02 Diagnose

Know why the answer looks the way it does.

A score tells you that a gap exists. Logres connects that gap to the evidence behind it: cited domains, competitor pages, missing subject coverage, weak structure, outdated facts, and inconsistent descriptions.

Illustrative citation evidence

Third-party publicationcited
Competitor service pagecited
Owned website sourceowned
Evidence is linked to the answer before a change is proposed.

  1. Trace citations and supporting evidence

    See which sources AI assistants cite, which competitors benefit, and where your own site fails to support the answer.

  2. Inspect AI readability

    Map findings directly to pages and content nodes so recommendations identify exactly what is missing and where it belongs.

  3. Separate facts from assumptions

    Every proposed change carries provenance. Unsupported claims, invented statistics, and unconfirmed business facts remain blocked from publication.

03 Improve

Fix what AI can read, verify, and trust.

The AI-Readability Diff Studio turns diagnosis into an implementation-ready change set without allowing generic AI copy or unsupported claims into the site.

Illustrative Diff Studio

Provenance checked
Before

Service information lacks an answer-ready explanation.

Proposed

Add verified service context with its supporting source.

Node-mapped findingService section · verified target
SchemaHTML sectionllms.txtImplementation guide

  1. Create grounded website changes

    Generate page structure, answer-ready sections, schema, entity descriptions, service coverage, FAQs, and machine-readable files from verified facts.

  2. Review every proposed edit

    Accept, reject, or resolve changes individually. Every recommendation shows its source, reason, target location, and expected impact.

  3. Export an implementation bundle

    Produce developer-ready schema, HTML sections, llms.txt, content snippets, NAP checks, and plain-English implementation guidance.

04 Re-measure

Separate what was fixed from what has changed.

Website improvements can be verified immediately. AI recommendations change on a different clock.

Two clocks, one evidence trail

Site implementation
Snapshot
Verified change
AI answer movement
Baseline
Defined re-test

  1. Verify site implementation

    Re-snapshot the live site and confirm which technical or content findings have actually been resolved.

  2. Re-run the market baseline

    Measure the same prompt groups on a defined cadence to see whether visibility, citations, descriptions, and competitive position move.

  3. Maintain an evidence timeline

    Keep site changes and AI-answer changes on separate timelines so progress is reported honestly rather than implied prematurely.

Method boundary

Four dimensions. One evidence standard.

Visibility, Authority, Accuracy, and Influence describe separate aspects of the position AI systems create. Logres keeps the measurement distinct from the remediation work that follows it, with dated source evidence behind the findings.

Visibility — are you named?

Authority — is credible evidence cited?

Accuracy — is the description right?

Influence — does the answer support consideration?

Read the methodology
Start with a baseline

Make the recommendation answer visible before you try to improve it.

Scope a dated, evidence-based view of how AI assistants currently present your organization and the competitors they name instead.

Get your baseline