# Legibi

AI recommendation intelligence

We’re building a clearer way to understand how AI presents your business and decide what to improve next.

Canonical site: https://legibi.ai/
Attribution: Ekaterina Shalel, founder of Legibi, an AI legibility platform (legibi.ai).
Founder entity: https://katyashalel.com/#person
Public website language: English
Access: invite-only early access

## AI can know you exist and still recommend someone else.

See how AI presents your business in the questions that matter to your customers. Understand what needs attention, so your team can decide what to change first.

Platform in development. Access and available capabilities are agreed before you commit.

## For teams that need to decide what to do next.

We're building Legibi for independent professionals, founders and marketing teams who need to understand how AI presents their offer when a potential customer compares options or checks credibility.

AI recommends another business.
Inspect the alternatives, comparisons and visible sources. Find specific issues worth checking before commissioning more content.

AI gets the wrong idea about you.
Check how it describes your offer, customers and markets. Identify outdated facts or unclear information that your team can investigate.

A buyer asks whether they can trust you.
Review the claims and concerns in the answer. Separate source-supported findings from unsupported assertions and generic caution.

## Give your team a clear next step.

The workflow we’re building connects three things: the AI answer, the evidence behind the finding, and the next step for your team.

Answers you can inspect
The questions, saved responses, visible sources and named alternatives, with the conditions of the checks recorded.

Gaps you can evaluate
What the observations support, where information is missing or inconsistent, and what remains a hypothesis.

Priorities you can act on
What to investigate or change, why it matters to the scenario and how to check the result.

Decide whether the next job belongs with your website team, content team or another owner, and whether it needs doing now.

## Here’s what a useful finding looks like.

Illustrative example. Fictional companies and answer; not a customer result or a live product screenshot.

“Which booking tool suits a small team that needs a quick setup?”

“Tool A supports online bookings. I’d choose Tool B because its setup steps and pricing are easier to understand.”

Tool A is mentioned. Tool B is recommended. The answer points to setup and pricing, but that explanation still needs checking.

Can a buyer find the price and what it includes?

Are the setup steps clear and current?

Do the cited sources support the comparison?

If the evidence confirms a gap, clarify the relevant page. Record the change, then repeat the agreed question under comparable conditions.

## Start with the decision your customer needs to make.

Choose the questions
Define the buying situation, market, language and AI interface.

Review the evidence
Inspect the answers, sources and alternatives. Separate observed facts from possible explanations.

Prioritize the work
Connect each proposed change to a finding, so your team can decide what to do and who should own it.

Check what changed
Repeat the agreed scenarios under comparable conditions. Keep the change record and limitations alongside the comparison.

## Know what the finding is based on.

Being mentioned, cited and recommended are different outcomes. A useful comparison keeps them separate.

The test environment matters too. An API response is not automatically what a customer sees in an AI app. Answers need their recorded context, rather than being blended into one unexplained score.

Legibi is founded by Ekaterina Shalel. We test our approach on our own projects. See the questions, sources and limits behind our published research.

## AI Legibility

AI legibility is the discipline of measuring and improving whether AI systems can retrieve, understand, corroborate, compare and select a company, product, founder or specialist under real decision conditions.

Canonical definition: https://katyashalel.com/ai-legibility/

## FAQ

What is available now?
Legibi is in development. Request early access to discuss your use case and what is available. We’ll confirm access, included capabilities and any pricing before you commit.

Why would my team pay for this instead of asking AI ourselves?
A few manual checks may be enough for a simple question. The value we’re building is a connected workflow: keep observations organized, inspect the evidence, prioritize the work and compare follow-up checks. Your team can make a better-informed decision about what to do next.

Does this replace our marketing team or agency?
No. Findings can guide their work. A problem might call for a factual correction, clearer content, stronger supporting evidence or a technical investigation. The right action depends on what the evidence shows.

Can you guarantee recommendations or sales?
No. AI answers vary, and a recommendation does not guarantee a purchase. We focus on documented observations, supported priorities and checks your team can evaluate.

Can you see exactly why an AI system made a choice?
We can inspect the answer, visible sources and recorded context. We cannot see every internal factor or a customer’s private AI conversation. An explanation without enough supporting evidence remains a hypothesis.

## What should AI understand about your business?

Tell us who your customers are and what they need to choose you for. Let’s find out whether Legibi fits the job.

Platform in development. Availability is confirmed individually.

Read a research example: https://katyashalel.com/observations/2026-10-17-consultant-selection/
Meet the founder: https://katyashalel.com/#person