Peec AI is an analytics platform that runs the prompts marketing teams enter across AI engines and tracks visibility, position, sentiment and sources over time; Recro is an insight agency that replaces those prompts with the brand’s buyers built as personas, generates their decision questions and turns the result into action. In Peec, measurement quality depends on the quality of the prompts the team writes. In Recro, producing the question is the work itself. For a team that already knows the right questions, Peec is an efficient measurement layer; for a B2B brand that doesn’t know which questions matter, the question Recro answers comes first.
Recro wrote this comparison. Information about Peec AI comes from the company’s own announcement and funding coverage; sources are listed at the end. Details are current as of October 2026.
What does Peec AI do?
Berlin-based Peec AI launched its product in February 2025 and raised a $21 million Series A led by Singular in November 2025. According to the company, more than 1,300 brands and agencies adopted the platform within ten months. That fast adoption reflects a simple, clear product.
Peec’s logic is straightforward: the team enters the prompts it wants to track, the platform runs them in ChatGPT, Gemini, Claude, Perplexity and other engines and stores the answers over time. Reporting rests on three measures: visibility (how often the brand appears), position (its ranking) and sentiment (how it’s described). The platform also shows which sources drive impact, and data can be exported via API or into BI dashboards.
The real variable: who writes the prompt?
Every prompt-based tool carries a hidden assumption: the tracked question is the question the buyer asks. In practice, prompts are often written by the marketing team, and the team describes the category in its own language.
We saw the cost of this in Recro’s simulations. A coffee supply brand was mentioned in 8 out of 8 runs for the questions of a tourism and restaurant group’s F&B director persona. The same brand wasn’t mentioned at all in the questions of hotel, café and restaurant chain procurement directors. Had the team written its prompts in the F&B director’s language, the dashboard would have shown “excellent visibility”. The bids the sales team was losing were behind the other door.
In short, the prompt set determines the measurement. If it isn’t clear which person asks, at which stage, under which conditions, the number on the dashboard is an average whose owner is unknown. We unpacked this in Persona GAP Analysis and What is a decision question?
Persona, occasion and buying intent
Recro produces questions from three layers. First, the persona: each role in the buying group is built separately with a dedicated persona builder module, grounded in an established buyer-insight model. Second, the occasion: the same person asks one question during a planned supplier change and another during an urgent need after an audit. Third, buying intent: the goal isn’t to show up in high-volume questions but to be everywhere a buyer close to purchase looks.
These layers surface the questions teams struggle to write on their own. A finance lead’s “what are the cost items beyond the fixed fee for this service?” is one example. In Recro’s reports, the researched brand appeared in only 12% of answers to price and total-cost questions like this. Details are in What does AI say when buyers ask about price?
Comparison table
| Dimension | Peec AI | Recro Insight Model |
|---|---|---|
| Measurement input | Prompts entered by the team | Questions generated from persona, occasion and decision stage |
| Core metrics | Visibility, position, sentiment, sources | Appearance by persona, framing, source and GAP type |
| Interpretation and action | Platform insights; execution sits with the team | Prioritised action list; follow-through with a brand representative |
| Data use | API and BI integration, exportable reports | Specific, local report; a dashboard that tracks progress after GAPs are found |
| Operating model | Self-serve analytics platform, also used by agencies | Agency service; infrastructure optimised regularly for each brand |
| Best fit | Teams that know their question set and want data in their own systems | B2B brands that want to learn which questions decide the deal |
Which one fits which situation?
For a team that knows its buyers well, has already mapped its decision questions and wants to combine AI visibility with other marketing data in its own BI environment, Peec is a clean, fast and scalable choice.
For a brand without that map, the order reverses. First you need to find out who asks what, on which occasion, and why the brand gets eliminated. Recro does this as an agency: the brand gets a representative, findings are reviewed together in meetings, and progress is tracked in a dashboard as GAPs close. The question map Recro produces can later feed any monitoring tool as well.
How does Recro make this gap visible?
The Recro Insight Model doesn’t leave the weakest link of measurement, the question, to the team’s guesswork. Questions are derived from the brand’s real buyers.
One simulation · 4 key pillars · 90+ steps
What sets Recro apart is four pillars built from scratch for each brand. Together they form a single simulation of 90+ steps, with each pillar producing the input for the next.
- Persona builder: Decision-maker profiles that represent the brand’s real buyers, specific to its sector and sales structure.
- Question builder: The brand-specific decision questions these people ask AI along their buying journey.
- Report builder: A report that reads the answers against the brand’s goals and competitors, together with the sources behind them.
- Action recommendation builder: A prioritized list of actions that closes the sector-specific signal gaps.
- The persona builder builds each role in the buying group separately, so each door is measured on its own rather than as one “target audience” average.
- The question builder turns persona attributes and occasions into decision questions that don’t name the brand.
- The report and action recommendation builders tie each finding to a GAP type and a prioritised action.
Outputs aren’t templates; they are specific, local results for the brand’s sector, market and buying group. The infrastructure is optimised regularly for each brand, and the brand’s representative tracks progress with you.
We asked the same question about other tools: Recro compared with Semrush AI Visibility Toolkit, Profound, Otterly.AI and Ahrefs Brand Radar.
Executive summary
- Peec AI is a fast-growing analytics platform that tracks the prompts teams enter across many AI engines.
- Prompt-based measurement is only as good as the prompt set; questions written in the wrong person’s language can look misleadingly good.
- Recro generates questions from persona, occasion and buying intent, turns findings into action and follows through with a representative.
Frequently asked questions
Which metrics does Peec AI report?
According to the company: visibility, position and sentiment, plus which sources drive impact. Data can be exported through API and BI integrations.
Can’t you enter persona-based prompts in Peec?
You can; a role and conditions can be written into the prompt. The hard part is knowing which persona asks which question on which occasion. In Recro, that is the work itself.
Can Recro’s question map be used in another tool?
The decision questions Recro produces are a strategic asset for the brand; it can use them in its content plan and its own measurement tools.
Why is Recro an agency service rather than software?
Because the value shows up in interpreting the measurement and turning it into action. Each brand gets a representative, findings are reviewed in meetings and progress is tracked in a dashboard.
Which suits a small B2B brand better?
If you know your decision questions and have time to read a dashboard, Peec. If you don’t know why you drop off the shortlist, Recro first.
Are the prompts you track the questions your buyers actually ask? Recro builds the questions from personas and occasions specific to your brand.
Request a demo insight report →
Sources
- Peec AI · We raised $21M Series A to help brands win in AI search (2025)
- Tech.eu · Peec AI raises $21M Series A (2025)
This article was prepared with AI assistance and published under the review of Mehmet Semih İpek.
