Most tools start with a list of contacts. Client Clarity starts with your company and your value proposition, builds the persona you sell to, then finds target accounts that are a good fit and researches each one. You get cited discovery briefs, a shortlist of ten matched companies and weekly buying signals, so you walk into the meeting prepared. Built for consulting firms, B2B agencies and sales teams. Free during beta.
ā¢2 reviews
What's great
What was great about it is, the pipeline is the right shape. It doesn't ask an LLM to "find me leads." It works in stages: Business ā Market ā Buyer persona ā Fit ā Account ā Signal. Each stage narrows the one before, so the shortlist comes from a defined persona and value proposition rather than a vague prompt. For a vertical like ours, that's what makes the output usable.
Citations make it auditable. Every claim in a discovery brief links back to a public source. That's how you deal with hallucination in practice: I can check a figure before I put it in front of a prospect. [e.g. "I spot-checked a few claims about one agency's branch expansion against Companies House and their website, and they held up."]
It sticks to public data. No scraped personal data or grey-market contact lists, which matters for GDPR. Our own customers are letting agents who deal with data compliance every day.
The signal layer adds timing. Weekly alerts on leadership changes, hiring and expansion help with when to reach out, not just who. Agencies changing software usually follow a trigger like that: a new branch, an acquisition, or a new finance hire.
What would make it great for technical teams:
An API or webhooks so briefs and signals can go straight into our CRM or internal tools without copy-paste
Structured export (JSON/CSV) with each claim's source URL and retrieval date kept
Freshness and confidence markers on each claim, so stale data is obvious
UK registry filters such as Companies House SIC codes (68310 estate agencies, 68320 property management), incorporation date and filing history
De-duplication against existing CRM accounts so we don't research customers we already have
Overall, this is grounded, verifiable research rather than generic AI summaries. With an API it would be a strong data source for any B2B team in a vertical market. Nice work, Daniel.
What needs improvement
CRM de-duplication. Leave out companies already in our pipeline or customer base so we don't research the same accounts again.
Refresh before a meeting. One-click "update this brief" that shows what changed since the last run.
Bigger, iterative shortlists. Let us reject or approve suggestions and have the next batch learn from that feedback.
None of these are deal-breakers. The core (value-prop-first targeting and cited briefs) is right. These changes would take it from a research tool to part of our sales stack.
vs Alternatives
We'd tried the usual stack: Apollo and LinkedIn Sales Navigator for lists, plus ChatGPT for research. None of them answered the question that matters to us, which is which letting agencies actually need our software right now, and why.
Apollo / ZoomInfo / Cognism are contact databases. They give you names and emails, but the research is still yours to do. In UK proptech, a list of 5,000 "estate agents" isn't useful when only a few dozen are a real fit.
Sales Navigator finds people but tells you nothing about the business: growth, compliance pressure, systems, triggers.
Clay is powerful, but you have to design and maintain the enrichment workflow yourself. That's a project, not a tool.
ChatGPT / generic AI writes fluent summaries with no sources, so I can't trust a figure enough to use it on a call.
Client Clarity was the only one that starts from our value proposition, filters accounts by fit, cites every claim, and uses public data only. The last point matters for GDPR when your customers are agencies that handle tenant data. It does the research and the targeting together, and I can check the output.
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Thanks Cham, really appreciate the feedback. Great to hear you'd already tried the usual stack (Apollo, Sales Nav, Clay, ChatGPT) and that starting from the value prop is what made the difference. That's exactly why we built it that way. You could think about the competitive stack as :
Apollo / ZoomInfo
ā Who can I contact?
Google / LinkedIn / company websites
ā What can I find out?
ClientClarity
ā Who should I care about, and why?
Being able to check a figure before it goes in front of a prospect is the whole point of the citations, so good to know that's what landed for you.
Your list is useful too. CRM de-duplication and a "what's changed since last run" refresh are the ones I'm looking at first. The API and structured export make sense for a technical team like yours.
Would you be up for a quick chat? I'd like to hear how you'd want the Companies House filters to work in practice.
Report
Hi all, I'm Dan, the founder of Client Clarity.
We built it because most prospecting tools start with a list of contacts, and we think that's the wrong place to start. Client Clarity starts with your own company and value proposition, builds the persona you sell to, then finds target accounts that are a good fit and researches each one.
Each brief covers the company, the people, their situation, relevant signals and where your proposition might fit, with the claims tied to cited sources.
It's not a lead generation tool in the usual sense. It doesn't scrape contact lists or send messages for you. It does the preparation you'd otherwise spend hours on before a client conversation.
The part I'm most interested in testing is whether AI can turn that research into something useful before you walk into the room.
You can build a free example report with no signup at https://clientclarity.co.uk, and it's free during beta.
It's launching today and I'd love to hear what you think. What do you check before a first client call that a tool like this would need to cover?