About Kojable

Built to make AI representation understandable and improvable.

Kojable began with a practical problem: AI systems were shaping how buyers understood companies, but teams could not easily see when those answers were inaccurate, incomplete, or misaligned with the evidence.

Dublin, Ireland Founded 2025 AI answer alignment platform

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AI representation snapshot

ChatGPT Positioning gap identified

Claude Category framing differs

Gemini Competitor evidence stronger

Perplexity Supporting source gap

Representation clarity Needs attention

Improvement path Actions identified

The origin

“AI was answering questions about companies. The evidence behind those answers was difficult to see.”

The problem that led to Kojable.

AI became part of the buyer journey before companies had a way to diagnose the answers.

Buyers now use ChatGPT, Claude, Gemini, and Perplexity to research categories, compare providers, validate claims, and decide which companies deserve further consideration.

Those answers can reflect public pages, articles, reviews, citations, third-party summaries, outdated positioning, competitor framing, and missing proof. The result is not always factually wrong, but it can be incomplete or misleading in commercially important ways.

Kojable was built to make that process more manageable: monitor how AI currently represents the company, diagnose what may be shaping the answer, explain what to change and how to change it, and retest comparable questions to verify what improved.

Mission

Make AI representation understandable, actionable, and verifiable.

Kojable is not simply a visibility dashboard or a generic content service. It combines recurring monitoring, evidence-backed diagnosis, practical implementation guidance, and retesting so teams can move from an unexplained answer to a clear course of action.

Monitor

Track how major AI systems describe, compare, cite, and recommend your company across relevant buyer questions.

Diagnose

Identify recurring claims, source patterns, competitor framing, outdated information, and missing proof associated with the answer.

Improve

Prioritise what should change, explain why it matters, and provide clear guidance on where and how to make the change.

Verify

Retest comparable questions to measure what changed, what held, and what needs further attention.

How we think

The answer is more useful when you can see what may be shaping it.

AI answers can appear definitive to the buyer reading them. Kojable treats those answers as a measurable operating surface: something teams can monitor, diagnose, improve, and retest with evidence rather than assumptions.

What Kojable examines

How AI describes the company’s category, audience, capabilities, and differentiation.
Which competitors are recommended and how the comparison is framed.
Which sources, recurring claims, outdated information, and evidence gaps are associated with the answer.
Whether comparable answers change after the recommended actions are carried out.

Beliefs

The principles Kojable is built on.

Evidence before assumptions

A score can show that a gap exists. Useful action requires understanding the answer patterns, sources, and information gaps associated with it.

Guidance should be actionable

Every recommendation should explain what needs attention, why it matters, where the change should happen, and how to carry it out.

Improvement should be verified

Representation work is not complete when an asset is published. Comparable answers should be retested to see what changed and what needs further refinement.