B2B prospect data sourcing and enrichment console

For

Sales operations and market research teams building and maintaining B2B prospect lists

Solves

Prospect data is scattered across several rented tools, so lists go stale, contacts are incomplete and enrichment steps are repeated by hand.

Delivers

Reviewed, export-ready prospect dataset

Built in

about 5 weeks of creation time, MVP in 6 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 tool sprawl and manual list work while keeping one owned, auditable prospect dataset.

1. Collect company information from permitted sources into prospect lists.
2. Extract emails, phone numbers and social contacts for outreach.
3. Enrich datasets with live, up-to-date information from multiple sources.
4. Apply buyer-defined filters to find niche leads.
5. Run AI agents to automate research and data collection tasks.
6. Provide a spreadsheet-like environment for data operations.
7. Expose an API for other tools and workflows.
8. Track market trends and competitor activities.
9. Export collected data for CRM or further processing.
10. Find lookalike companies from uploaded lists.
11. Identify best contacts from target account lists by job title.
12. Examine the technology stack of apps to find prospects or partners.
13. Surface real-time news and event signals with impact and sentiment scoring.
14. Add qualitative company fields such as competitive moat, business model and GTM motion.
15. Perform entity resolution at ingestion to reduce noise.
16. Feed live B2B data into large language models and AI agents.
17. Cover global company and professional datasets.
18. Handle rate limits to reduce interruptions when collecting multiple pages.
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, export-ready prospect dataset with source references and unresolved questions.

Everything these tools do, in one app

• Company data sourcing Collects company information from various sources to build prospect lists.Found in Extruct AI, Kuration.ai, Fork.ai and 5 more
• Contact information extraction Extracts emails, phone numbers, and social contacts for outreach.Found in Kuration.ai, Fork.ai, Vurge
• Real-time data enrichment Enhances datasets with live, up-to-date information from multiple sources.Found in Extruct AI, Databar.ai, akta.pro and 1 more
• Custom filtering Allows users to define precise search criteria to find niche leads.Found in Extruct AI, Fork.ai, Telescope 2.0
• AI-driven automation Uses AI agents to automate research and data collection tasks.Found in Extruct AI, Kuration.ai, Vurge and 2 more
• Spreadsheet interface Provides a familiar spreadsheet-like environment for data operations.Found in Vurge, Databar.ai
• API integration Enables integration with other tools and workflows via API.Found in Extruct AI, Kuration.ai, Databar.ai and 2 more
• Market monitoring Tracks market trends and competitor activities in real time.Found in Extruct AI, akta.pro
• Data export Exports collected data for further processing or CRM integration.Found in Fork.ai
• Similar company search Finds lookalike companies based on uploaded lists to expand target markets.Found in Telescope 2.0
• Account-based research Identifies best contacts from target account lists based on job titles.Found in Telescope 2.0
• Technology stack analysis Examines the technology stack of apps to identify prospects or partners.Found in Fork.ai
• News and signals Provides real-time news and event signals with impact and sentiment scoring.Found in akta.pro
• Qualitative company fields Includes fields like competitive moat, business model, and GTM motion.Found in akta.pro
• Entity resolution Performs entity mapping at ingestion to reduce noise in data feeds.Found in akta.pro
• LLM agent integration Integrates live B2B data into large language models and AI agents.Found in Explorium MCP
• Global data coverage Provides access to extensive global company and professional datasets.Found in Explorium MCP
• Rate limit handling Manages rate limits to reduce interruptions when scraping multiple pages.Found in Vurge

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted company sources, contact records, live enrichment feeds and buyer-defined filters

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted company sources

4. 3

   Contact records

5. 4

   Live enrichment feeds and buyer-defined filters

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, export-ready prospect dataset

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, rate-limit handling and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved source set and one export schema; final contact accuracy, permission checks and outreach decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source and filter setup, Searchable prospect library, Enrichment and review queue, Export and delivery. Use a filter bar over a sortable table for the library, a side panel for field definitions and source references, and a review queue for records with low confidence or missing permissions. Let users compare enriched and original values side by side. Display sourced, enriched, reviewed and exported states. Provide a shareable read-only view for approved stakeholders. Make the task-specific outcome reviewed, export-ready prospect dataset visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, field definitions, review states, usage allowances, export limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Buyer-owned CRM, permitted company sources and authorized enrichment providers. Cloud storage, spreadsheet import/export and outreach destinations. Start with file exchange and validate destination specifications before promising direct CRM 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

   6 days

   One buyer segment, one recurring use case; first modules: collect company information from permitted sources; extract emails, phone numbers and social contacts. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

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

4. 4

   Full product

   3 weeks

   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 operations and market research teams building and maintaining B2B prospect lists use it to solve "prospect data is scattered across several rented tools, so lists go stale, contacts are incomplete and enrichment steps are repeated by hand"?
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: Accepted prospect records per research hour and stale or bounced records after export.
4. Measure, then decide. Track accepted prospect records per research hour and stale or bounced records after export; accepted-output 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 approved source set and one export schema; final contact accuracy, permission checks and outreach decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: collect company information from permitted sources; extract emails, phone numbers and social contacts. Support the third module with operator review: enrich datasets with live, up-to-date information. Include source references, corrections, basic organization access, approval states, 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 source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the reviewed, export-ready prospect dataset. Retain the explicit scope boundary: One approved source set and one export schema; final contact accuracy, permission checks and outreach decisions remain human.

What the build depends on. Source upload and preview, asynchronous enrichment jobs, editable version history, reviewer access and tested export formats. High-fidelity coverage requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved source set and one export schema; final contact accuracy, permission checks and outreach decisions remain human.

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 prospect dataset. Offer a monthly production allowance after repeat demand. Quote complex multi-source or global coverage separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, export-ready prospect dataset. 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 tool sprawl and manual list work while keeping one owned, auditable prospect dataset. Demonstrate a concrete reviewed, export-ready prospect dataset using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Sales operations and market research professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, export-ready prospect dataset from a small authorized input set, with a transparent calculation of accepted prospect records per research hour and stale or bounced records after export and no promised savings.

The first 30 days

1. Week 1: interview five sales operations and market research teams building and maintaining B2B prospect lists and inspect a recent example of prospect data scattered across several rented tools, so lists go stale, contacts are incomplete and enrichment steps are repeated by hand.
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 accepted prospect records per research hour and stale or bounced records after export, 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: Accepted prospect records per research hour and stale or bounced records after export. 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

Accepted prospect records per research hour and stale or bounced records after export; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, export-ready prospect dataset. 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 mappings, field definitions and review examples, together with reliable delivery for a narrow sales and research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for sales operations and market research teams building and maintaining B2B prospect lists. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Extruct AI, Kuration.ai, Fork.ai, Vurge, Telescope 2.0, Databar.ai, akta.pro and Explorium MCP, plus manual spreadsheet research. Compare this product with the buyer's present method on accepted prospect records per research hour and stale or bounced records after export. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Source access, enrichment calls, storage, reviewer hours, client revision rounds and permitted data licenses. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the reviewed, export-ready prospect dataset. Track cost per accepted record, including correction work, unsuccessful cases and support.

Preserve source attribution, contact permissions, opt-out status and usage rights. Buyers approve outreach scope and export destinations. One approved source set and one export schema; final contact accuracy, permission checks and outreach decisions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.