Build a competitor matrix from a list of names
Give it a column of competitors and get pricing, positioning, target segment and differentiators back as structured columns.
Updated August 2026
What goes in, what comes out
Input
- Product
- Website
Output
- Starting price
- Pricing model
- Target segment
- Key differentiator
The prompt
Paste this into the description field, or write your own version of it.
Find the entry-level price and pricing model (per-seat, usage-based, flat), the primary target customer segment, and the single clearest differentiator this product claims over alternatives.
Why this works
Pricing pages, homepages and comparison posts are all public and highly structured. This is the kind of research that is tedious rather than hard, which makes it a good fit for per-row automation.
Getting the best results
Enterprise tiers are often quote-only, and the model returns N/A rather than guessing. The blank-cell counter tells you how much of a market prices publicly, which is a useful signal in itself.
What it costs
On Gemini 3.1 Pro, a typical row of this shape costs about <$0.01, so roughly $2.18 per thousand rows, paid directly to Google. OpenClay adds nothing.
The tool shows an exact figure after a five-row test, before you commit to the batch. See all models and pricing.
Step by step
- 1List the competitors. One row per product, with a website column if you have it.
- 2Name the comparison axes. Each axis becomes an output column, so be specific.
- 3Test and tune. If cells come back blank, make the column names more concrete and re-test.
- 4Export the matrix. Download and drop straight into your comparison deck or doc.
No account, no card, no platform fee.
Bring your own API key and pay the model provider directly.
Start enriching, freeOther use cases
- Enrich a company list with AI: Turn a column of company names into leadership, funding, headcount and positioning, researched from the live web one row at a time.
- Enrich a lead list without paying for Clay: Export raw prospects from Apollo, lemlist or LinkedIn, enrich them with live research, and pipe them back into your sequencer.
- Enrich a list of universities: Rankings, acceptance rates, tuition and programme details for hundreds of institutions, without copying from a dozen tabs.
- Track hiring signals across companies: Open roles, the functions being hired, and the careers page URL: a decent proxy for where a company is investing.
- Enrich any CSV with AI: The generic case. If you can describe the column you want in a sentence, and the answer exists on the public web, this works.