Verified prospect list assembly and enrichment console

For

Sales teams and agencies building verified contact lists for prospecting

Solves

Prospect data is scattered across rented tools, so lists arrive incomplete, unverified and hard to trace to a source.

Delivers

Reviewed, source-linked contact list with verification state and enrichment history

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce list-building hours while keeping every contact traceable to its source.

1. Describe the ideal customer in plain language.
2. Accept CSV, LinkedIn URLs, domains or filter input.
3. Search a large contact repository.
4. Run contacts through an enrichment waterfall.
5. Verify email addresses before use.
6. Score and rank contacts by fit.
7. Sync approved lists to CRM systems.
8. Run per-lead research agents from plain-English prompts.
9. Scan public web sources for current signals.
10. Target prospects by job changes or funding news.
11. Find lookalikes of existing successful customers.
12. Attach source links to every field.
13. Process large volumes in batches.
14. Export to spreadsheet formats.
15. Capture leads while browsing via extension.
16. Expose an API for external workflows.
17. Draft personalized outreach from approved lists.
18. Schedule follow-up tasks and reminders.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned reviewed, source-linked contact list with source references and unresolved questions.

Everything these tools do, in one app

• Natural language prospecting Users describe their ideal customer in plain language to find matching leads.Found in Telescope, Jesse, Tables.so and 1 more
• Multi-source lead input Accepts various inputs like CSV, LinkedIn URLs, domains, or search filters to start prospecting.Found in Cleanlist AI
• Large contact database Provides access to a vast repository of profiles or emails for lead discovery.Found in Ciro, AI Email Finder by Ful.io, Telescope and 1 more
• Enrichment waterfall Runs contacts through multiple data providers to fill in missing details like emails and phone numbers.Found in Cleanlist AI, Tables.so
• Email verification Checks email addresses for accuracy to reduce bounces.Found in Cleanlist AI, AI Email Finder by Ful.io
• Lead scoring Ranks contacts based on fit to help prioritize outreach.Found in Ciro, Avina
• CRM sync Exports or syncs leads directly to CRM systems like HubSpot or Salesforce.Found in Cleanlist AI, Ciro, Telescope and 2 more
• AI research agents Runs automated research tasks per lead based on plain-English prompts.Found in Cleanlist AI
• Live web search Scans public web sources in real-time to find current prospects and signals.Found in Jesse, Avina
• Signal-based targeting Identifies prospects based on buying signals like job changes or funding news.Found in Jesse, Avina
• Lookalike search Finds prospects similar to existing successful customers using dynamic signals.Found in Telescope, Jesse
• Source-backed results Provides links to sources for each lead to verify information.Found in Tables.so
• Batch processing Handles large volumes of leads efficiently in batches.Found in LeadGenSheet.com, Tables.so
• Spreadsheet export Exports lead data directly into spreadsheet formats for easy management.Found in LeadGenSheet.com, Ciro, Telescope
• Chrome extension Browser extension for finding leads or emails while browsing.Found in Cleanlist AI, AI Email Finder by Ful.io
• API access Allows integration with external tools and custom workflows via API.Found in Cleanlist AI, AI Email Finder by Ful.io, Jesse
• Automated outreach Runs personalized email campaigns or ad activations based on lead lists.Found in Avina
• Task automation Automates routine tasks like scheduling and reminders for productivity.Found in Myko Assistant

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted input lists, public profile and company pages, provider records and buying signals

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted inputs

4. 3

   Provider records and buying signals

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed, source-linked contact list with verification state and enrichment history

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for deduplication, schema validation, verification checks and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Supplied lists must have a lawful basis for contact; final outreach and data-use decisions remain with the buyer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: List brief and sources, Searchable contact library, Review and export queue. Use a table-first library with saved searches, a detail panel showing source links, enrichment history and verification state, and a right-hand panel for filters, signals and comments. Let users compare duplicate records side by side. Display draft, needs review and approved states. Provide a client or teammate preview link with comments anchored to the relevant contact. Make the task-specific outcome a reviewed, source-linked contact list visible beside its evidence, review state and value baseline.

Accounts and administration

Workspace ownership, source permissions, contact retention limits, suppression lists, reviewer roles, export logs, API keys and usage caps. Add organization access boundaries, named reviewers, data retention controls and explicit approval for external actions such as outreach sends.

Integrations and data access

CRM systems such as HubSpot or Salesforce, spreadsheet tools, email sending platforms and browser extension surfaces. Start with file exchange and validate destination specifications before promising direct sync. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   4 days

   One buyer segment, one recurring use case; first modules: describe the ideal customer in plain language; accept CSV, LinkedIn URLs, domains or filter input. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will sales teams and agencies building verified contact lists for prospecting use it to solve "prospect data is scattered across rented tools, so lists arrive incomplete, unverified and hard to trace to a source"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Verified contacts per research hour and bounce rate on first send.
4. Measure, then decide. Track verified contacts per research hour and bounce rate on first send; accepted-contact rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One buyer workspace, one approved input format and one permitted data region; final outreach and data-use decisions remain with the buyer. Implement a bounded representative case set and the first two task modules: describe the ideal customer in plain language; accept CSV, LinkedIn URLs, domains or filter input. Support enrichment, verification and scoring with operator review. Include source links, corrections, basic organization access, approval states, spreadsheet export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified provider integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the reviewed, source-linked contact list. Retain the explicit scope boundary: One buyer workspace, one approved input format and one permitted data region; final outreach and data-use decisions remain with the buyer.

What the build depends on. List upload and preview, asynchronous enrichment jobs, editable version history, reviewer access and tested export formats. High-fidelity prospecting requires lawful basis for contact and current provider access. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One buyer workspace, one approved input format and one permitted data region; final outreach and data-use decisions remain with the buyer.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined list package. Offer a monthly seat or volume allowance after repeat demand. Quote custom API or outreach automation separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked contact list. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce list-building hours while keeping every contact traceable to its source. Demonstrate a concrete reviewed, source-linked contact list using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Sales teams and agencies building verified contact lists for prospecting professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked contact list from a small authorized input set, with a transparent calculation of verified contacts per research hour and bounce rate on first send and no promised savings.

The first 30 days

1. Week 1: interview five sales teams and agencies building verified contact lists for prospecting and inspect a recent example of prospect data scattered across rented tools, so lists arrive incomplete, unverified and hard to trace to a source.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure verified contacts per research hour and bounce rate on first send, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Verified contacts per research hour and bounce rate on first send. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Verified contacts per research hour and bounce rate on first send; accepted-contact rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, source-linked contact list. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved source rules, suppression records and reviewer corrections, together with reliable delivery for a narrow prospecting niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for sales teams and agencies building verified contact lists for prospecting. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Cleanlist AI, Ciro, AI Email Finder by Ful.io, SDRx, Telescope, Jesse, Tables.so, LeadGenSheet.com, Avina and Myko Assistant, plus manual spreadsheet research. Compare this product with the buyer's present method on verified contacts per research hour and bounce rate on first send. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Provider and enrichment lookups, verification calls, storage, reviewer hours, client revision rounds and permitted source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewed, source-linked contact list. Track cost per accepted contact, including correction work, unsuccessful cases and support.

Preserve lawful basis for contact, source attribution, suppression lists and data retention limits. Buyers approve outreach scope and data use. One buyer workspace, one approved input format and one permitted data region; final outreach and data-use decisions remain with the buyer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.