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Comparison
The closest match in positioning: enterprise-only, demo-led, and concerned with how AI represents a brand. Both build their prompts partly from panel data. The split is in what you can check: Meikai publishes the size of its panel and versions every change to the set; Bluefish runs one fixed set and does not describe the panel behind it.
All comparisonsBluefish is the competitor closest to us. Both read how AI answers frame a brand, down to the cited page, and both check what AI says against the brand's own governed facts. The difference starts before the first answer, in where the questions come from and how much of that you can check.
Bluefish runs a static prompt set, built from Google signals, brand data and panel data, and held fixed so that model drift does not move the numbers. It does not describe that panel. Meikai builds the set from a panel of 1.5 billion real question-and-answer messages from 2 million people, with demographics, and versions every change, so the set can follow demand with each earlier version kept on record. From there the optimisation runs on a schedule, each change is measured before and after, and advertising sits in the same platform, at a published price. Bluefish publishes no pricing, named engine list or allowances.
For a Fortune 500 procurement process that expects a bespoke path either way, that may not decide anything. For everyone else it decides the shortlist.
Read off each vendor's own public pages on the date shown. Cells we could not source are marked Not published.
| Criterion | Meikai | Bluefish AI |
|---|---|---|
| Entry price | EUR 90/mo1 | Not published1 |
| What you pay for | Per brand, per market, per LLM, per prompt1 | Not published1 |
| Maximum prompts | Unlimited1 | Not published1 |
| Supported LLMs | 14+ monitored4 | 10+ daily, list not published1 |
| Analysis window | No limit1 | Not published1 |
| Multi-brand and multi-country | Multi-country and multi-brand view1 | Not published1 |
| Prompt modelling | Prompt Studio: generate, bulk import, review, versioned publishing5 | Not published1 |
| Real user prompt data | 1.5B+ panel messages with demographics, 2M users reported5 | Fixed set built partly from panel data, panel not described4 |
| Self-serve signup | No, demo-led1 | No, request a demo1 |
| Product and SKU tracking | SKU ranking, shelf share, price win rate, accuracy7 | Agentic Commerce and Product Performance1 |
| Citation and source tracking | Per-citation portrayal, centrality, format and source influence4 | Citation analysis, impact score, influence rank2 |
| Brand perception | Attribute radar, claims checked against governed facts4 | AI Monitoring: favorability and risk2 |
| Site and crawler analysis | SEO audit, logs from 7 CDNs, every bot IP-verified, AI referrals to purchase10 | Not published1 |
| Advertising in AI answers | ChatGPT ad planner (GEA), campaigns pushed to OpenAI, public rate card11 | Not published1 |
| Optimisation agents | Scheduled multi-agent runs, critic-reviewed3 | Agentic Campaigns: ranked tactics your teams ship3 |
| Optimisation impact | Before and after tracking on every change6 | Fixed baselines that separate marketing impact from model drift4 |
| Data access | 150+ MCP tools, sync API, 1M-row exports to GCS9 | MCP, 15+ provider integrations, terms not published1 |
| Integrations | GA4, Search Console, logs from 7 CDNs10 | 15+ provider integrations, not named1 |
| Stakeholder reporting | PDF and PowerPoint decks from every dashboard2 | Not published1 |
| Dedicated customer success manager | Included on Growth and Enterprise1 | Not published1 |
| Security and compliance | SSO and role-based access, SOC 2 Type 2 in progress8 | Not published1 |
Not published means the vendor does not state it on the public pages listed below. It does not mean the product cannot do it.
Both vendors draw on panel data. Bluefish runs a static prompt set, built from Google signals, brand data and panel data, broken out by the customer personas the brand defines and held fixed so that model drift does not move the numbers. The consistency is a virtue, but Bluefish does not describe the panel itself. Meikai starts from a panel aggregate of 1.5 billion real question-and-answer messages reported from 2 million users, with demographics, lets that evidence inform the generated questions, organises them by persona and topic, and publishes each change as a version, so you get the same consistency with the history attached. Ask any vendor to show where its prompts came from before you compare a visibility number.
Bluefish does not publish pricing. Its site has no pricing page and the call to action throughout is a demo request. We have no figure to quote and will not estimate one.
Yes. Brand Accuracy checks every claim an answer makes against a governed truth set you approve, reports a hallucination rate and a factual-inaccuracy rate per platform with confidence ranges, and puts each contradiction in an incident queue with the evidence frozen for your team to confirm, reject or defer. Bluefish publishes the same detection step. Ask both vendors to open the incident queue in the demo.
Bluefish states more than ten engines tracked daily, naming Alexa for Shopping among them, but does not publish the full list, so we cannot compare like for like. Meikai monitors fourteen and states which ones. Ask both vendors for the list in writing before deciding.
The fastest way to settle a shortlist is to run the same prompts through both platforms and compare what comes back. We will set that up with your prompts and your markets.