Jincove
Tracks how six AI answer engines describe your clients
About Jincove
Jincove exists because the question "where do we rank" has stopped having one answer. A growing share of buying research now happens inside AI assistants, which synthesise a response and cite a handful of sources rather than returning ten blue links. Whether your client is named in that synthesis, and which sources the model leaned on to say it, is a different measurement problem from search rank, and conventional rank trackers do not touch it.
This reads six major AI answer engines, ChatGPT and Gemini and Copilot among them, and records what each returns for prompts you define. Results come back with citations and sources so you can see not just whether a brand appeared but what the model read to form that view. Tracking is scheduled, so the output is a trend rather than a snapshot, and there is a REST API and MCP server so the data can feed something else rather than only a dashboard.
Define the prompts that matter for a client, the questions a real buyer would ask an assistant. Jincove runs each prompt against each selected engine on a schedule. One run equals one prompt on one engine, which is how billing is counted and makes cost easy to predict. Every result stores the answer, whether the brand was mentioned, and the sources cited. From there the data is available in the platform, through a REST call, or through the MCP server, so an agent can query it directly.
- •Six AI answer engines - ChatGPT, Gemini, Copilot and others queried through one interface.
- •Citations and sources recorded - Not just whether a brand appeared, but what the model cited to say it.
- •Scheduled tracking - Repeated runs turn single answers into a trend line.
- •REST API and MCP server - The data is queryable from your own code or by an agent, not locked in a dashboard.
- •Usage-based credits - One run is one prompt on one engine, so cost maps directly to what you measure.
- •Built for agencies - Multi-client structure rather than a single brand.
Aimed squarely at agencies who need to show a client something concrete about AI visibility, which is currently one of the hardest things in the discipline to evidence. The citation data is the part worth paying for: knowing a competitor is named is useful, knowing which three pages the model read to name them is actionable.
On cost, this is usage-based rather than a flat subscription. Plans sit at $249 and $749, with credits working out at roughly $20 per 1,000 falling to about $12 per 1,000 on larger plans, and one credit costing somewhere between $0.015 and $0.02 depending on tier. Since a run is one prompt on one engine, the arithmetic to watch is prompts multiplied by engines multiplied by frequency, which grows faster than people expect.
Pricing
$249 - $749/mo
- Studio$249/mo
- Agency$749/mo
- CustomContact sales
From the vendor pricing page, 2026-10-16· unchanged since September 2026
