Find AI

Name: Find AI
Brand: Find AI

AI Native

Automate any workflow with plain english. Command 1000's of “ChatGPT's” to tackle your work.

Est 2022|5 Ref Customers

LinkedIn 388|Website|FTEs 11|HQ New York, New York, US

MarketsList Building & Enrichment Account Research

Published Oct 15, 2026

01Common complaints

- Scenarios where alternatives are better - Enterprise procurement and compliance requirements - If SOC 2 Type II, ISO 27001, detailed DPA terms, and published security documentation are required upfront, vendors with mature compliance artifacts (for example, enterprise-focused providers) may be more appropriate. - All-in-one outbound execution - If the primary need is prospecting, sequencing, dialer, and email engagement in a single suite, an integrated product such as Apollo may reduce integration work. - Strict data provenance and opt-out sensitivity - If the organization is highly sensitive to allegations of scraping or unclear data sourcing, vendors that publish clear data broker policies and opt-out processes may be preferred. - Need for large-scale intent and technographics - If workflows depend on integrated intent signals and technographic data at scale, Find AI may not be the optimal primary system of record. - Red flags in the buying process (what to watch) - Absence of a clear security and compliance page or missing certification attestations - Vague answers about data sourcing for profiles and companies - No clear profile removal or opt-out path - Credit systems that cannot be forecasted using typical usage patterns - Current limitations acknowledged by the vendor - No explicit limitations or roadmap items were documented in accessible vendor sources for this pass. - Roadmap signals - Chatsheet blog mentions API access and progress toward API availability for Find AI, implying developer embedding was planned. - Scale limitations - Not publicly documented; buyers should test: - Maximum practical match volume before relevance diminishes - Credit burn rate for broad queries - Security or compliance gaps - Security certifications were not publicly documented in accessible sources. - API limitations - An API is referenced in vendor blog posts, but specifics (authentication, rate limits, endpoints, data fields, terms of service constraints) were not captured in the sources accessed.

Deal disqualifiers (what makes a poor fit)

Requirement for SOC 2 or ISO 27001 evidence before any pilot
Operation within heavily regulated environments with strict data provenance requirements
Need for highly transparent, deterministic filtering and explainability for compliance

02Why they buy

- Competitive advantages supported by available evidence - This research pass found limited direct customer reviews about Find AI specifically; therefore, many unique selling points are primarily vendor-stated or inferred from integrations. - Likely differentiation (vendor-stated / ecosystem evidence) - Natural-language search over professional/profile-like datasets (Perplexity-style framing) - Spreadsheet-first workflow consistent with Chatsheet’s broader product positioning - Availability inside Clay as an integration and documented per-match billing in that context - Areas where competitors have clear advantages - ZoomInfo: more documented compliance artifacts, broader product suite, and enterprise admin controls (generally) - Apollo: tighter integration with outbound execution and common SDR workflows - Clay: broader orchestration and multiple enrichment sources for repeatable pipelines

03Field reports

- Review-platform coverage (Gartner Peer Insights, G2, TrustRadius) - Gartner Peer Insights: no product-specific Find AI listing or reviews were found in accessible crawling during this pass; Gartner’s site often requires interactive navigation. - TrustRadius: product-specific Find AI/Chatsheet reviews were not identified in the accessible results from this pass. - G2: no Find AI/Chatsheet listing was surfaced in accessible results during this pass. - Community/Social feedback (direct quotes) - Direct Find AI-focused quotes were limited; most community content found related to the broader category and included: - Comments about the limitations of linear AI chat archives and a desire for better retrieval and organization - Confusion and frustration with credit-based AI pricing models - Concerns about LinkedIn content quality and potential AI-generated outreach - Compliance-related negative sentiment (Find AI / related ecosystem) - Reddit user allegation: a user posted that the vendor is "essentially using LinkedIn to create its content by scraping the profiles/companies data then re-writing it using some GenAI tool." This is an allegation and was not verified with vendor documentation in this pass. - Another Reddit user requested: "Remove profile from the vendor's site" (seeking takedown/opt-out). - Workflow pain (context for why a tool like Find AI might appeal) - A Reddit user described research workflow issues: "For simple tasks, chat is perfect… I either lose the important intermediate steps…" — illustrating why users may prefer more structured find and retrieval workflows. - Why customers may choose this product over alternatives (inferred) - Based on vendor positioning and the Clay integration documentation, plausible decision drivers include: - A natural-language search experience instead of complex filter sets - Faster list-building within spreadsheet workflows - Usage models that align spend with tangible outputs (for example, per-match costing in Clay) - Why some customers might switch to competitors (inferred) - Based on common patterns in the category and the limited direct reviews: - Data accuracy and freshness concerns - Compliance and ethics concerns about data sourcing - Budget unpredictability under credit systems - Preference for single-vendor suites (such as Apollo or ZoomInfo) where database, sequencing, and intent signals are integrated - Deal-breakers and reasons customers did not purchase (likely) - Unclear security posture (no SOC 2 or ISO certifications publicly documented) - Unclear data provenance and opt-out processes - Lack of a public review footprint on major enterprise review platforms

Vendor case studies

No detailed, metric-heavy official case studies were located in the sources accessed for this pass.

What evidence exists (lightweight)

Chatsheet’s homepage provides example scenarios (for example, an example that cites counts of matches found for a role) that read like product demo copy.

Critical evaluation

Example outcomes shown on marketing pages are not independently verified.
Buyers should request:
- Accuracy benchmarks (precision/recall style) on target personas
- Data provenance explanation
- A reproducible pilot with clear success criteria (for example, proportion with valid contact information, deliverability rate, improvement in reply rate)

04Best fit

Company sizes

Likely fit:
- Small-to-mid sized recruiting firms and agencies
- Startups to midmarket GTM teams
A reasonable operating assumption for evaluation is a small-to-midsize user base across revenue operations, recruiting, and growth functions; this should be validated with the vendor.

Industries and verticals

Recruiting and talent
B2B SaaS outbound sales and lead generation
Venture capital and private markets research

Roles and personas

Decision makers:
- Head of Recruiting / Talent Operations
- VP Sales / Head of Growth
- RevOps / GTM Operations
- Agency owners
End users:
- Sourcers, recruiters, SDRs
- Research associates
- GTM operators building lists and enrichment pipelines

Pain points and challenges the product addresses

Finding targeted people and companies without mastering complex filters
Reducing time spent switching across tools and managing multiple credit systems
Accelerating list creation and handoff into downstream workflows

Qualification criteria (what makes a strong fit)

Organizations that already operate in spreadsheets and want sourcing to integrate as a spreadsheet step
Ability to define personas in clear natural language
Ability to measure outcomes (matches, deliverable emails, reply rate, qualified meetings)

05The pitch

What the product does (core value proposition in objective terms)

- Find AI (Chatsheet) is an AI-powered search product for locating people and companies using natural-language queries (Perplexity-style), oriented around LinkedIn-type professional data and go-to-market (GTM) workflows. - The product is presented as part of the broader Chatsheet product family (an "AI spreadsheet") that combines spreadsheet-like workflows with AI automation for search, enrichment, and outreach. - Vendor materials indicate a focus on reducing tool sprawl in lead sourcing and enrichment stacks (for example, multiple logins, credit systems, and separate enrichment pipelines).

Target market and market positioning

Primary ICP appears to be:
- Recruiting firms and talent teams sourcing candidates
- Lead generation agencies
- Venture / investment teams sourcing companies/people and building lists
Positioning theme: an "AI + spreadsheet workflow" aimed at teams seeking faster list-building, enrichment, and outbound loops without stitching multiple point tools.

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