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B2B data enrichment where you already work: a Google Sheet, an AI chat through MCP, or a REST API.

React Admin Dashboard Template with Java Backend

Website, pricing, platforms and company facts side by side.
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| Website | derrick-app.com | flatlogic.com |
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| Company | Startup from France · 1 - 9 employees · 2026 | — |
| Listed in | — |
In their own words, as submitted to SaaSHub.


Derrick is a B2B data enrichment service built for the tools sales, RevOps and growth teams already use. Point it at a column of company names, domains, or LinkedIn URLs and it fills in the rest: verified professional emails, phone numbers, company firmographics, technologies in use, hiring...
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What each product offers, as listed by its team.


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Lead Enrichment from Linkedin (3 Minutes Tutorial FR) - Derrick App
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As answered by people managing Derrick App and Sing App React Java.
Derrick App's answer
Derrick runs the same enrichment engine on three different surfaces, against one shared credit balance.
A Google Sheets sidebar for working a list: enrich hundreds or thousands of rows, see the result column by column, then filter and sort in place.
An MCP server for working inside an AI conversation: ask a question in plain language from Claude, ChatGPT or any MCP-compatible client and get real data back instead of a guess. Most enrichment tools are only now announcing MCP support. Derrick ships it.
A REST API for working without a human: a CRM, a backend, a scheduled job, or a no-code workflow through Zapier, Make and n8n.
Two other things are unusual. Finder features are billed per result found, so a lookup that returns nothing costs nothing. And Derrick keeps no database of its own: every request queries sources at the moment you ask, so you get the state of the world today rather than a snapshot from a data dump.
French company data is native, not an afterthought: SIRET, SIREN, directors and legal records come straight from the public registry.
Derrick App's answer
Derrick runs the same enrichment engine on three different surfaces, against one shared credit balance.
A Google Sheets sidebar for working a list: enrich hundreds or thousands of rows, see the result column by column, then filter and sort in place.
An MCP server for working inside an AI conversation: ask a question in plain language from Claude, ChatGPT or any MCP-compatible client and get real data back instead of a guess. Most enrichment tools are only now announcing MCP support. Derrick ships it.
A REST API for working without a human: a CRM, a backend, a scheduled job, or a no-code workflow through Zapier, Make and n8n.
Two other things are unusual. Finder features are billed per result found, so a lookup that returns nothing costs nothing. And Derrick keeps no database of its own: every request queries sources at the moment you ask, so you get the state of the world today rather than a snapshot from a data dump.
French company data is native, not an afterthought: SIRET, SIREN, directors and legal records come straight from the public registry.
Derrick App's answer
Small sales, growth and RevOps teams that build their own prospecting lists, plus the agencies and freelancers who do that work for clients.
Concretely: someone who owns pipeline, works from a spreadsheet or a CRM, and needs contact and company data enriched before an outreach sequence goes out. Usually a founder, an SDR, a growth marketer, a RevOps or ops person, or a lead-gen consultant. Team size is typically under 200 people.
A second and growing group is developers and technical operators, who reach Derrick through the REST API or the MCP server rather than through the spreadsheet, and wire enrichment into a product, a CRM or an automated workflow.
Geographically the base is strongest in France, where the native SIRET and SIREN coverage matters, and extends across Europe and North America for LinkedIn, email and phone enrichment.
Derrick App's answer
Derrick was founded in 2022 and came out of agency work, not out of a product plan.
Running lead generation for clients meant the same loop every week: build a list, export it, push it through one tool for emails, another for company data, re-import the result, then clean up what did not match. The spreadsheet was where the work actually lived, and every tool wanted to pull the work out of it.
So the first version was built the other way around: bring the enrichment to the spreadsheet instead of moving the list to the enrichment tool. That decision shaped everything after it. When AI assistants became a place where people genuinely do research, the same engine was exposed as an MCP server rather than rebuilt as another chat product. When teams needed it inside their own systems, the same engine was exposed as a REST API.
The other early decision was to store no data. Derrick queries sources at the moment of the request. It costs more per lookup than serving from a warehouse, but the record is current, and there is no stale database to maintain.
Derrick App's answer
Google Apps Script for the Google Sheets add-on, distributed through the Google Workspace Marketplace.
Node.js for the MCP server, published on npm as derrick-mcp and installable with npx, no build step. It loads its tool list dynamically from the API at startup, so new enrichment features appear in AI clients without a server update.
A JSON REST API over HTTPS with API-key authentication, which is the shared foundation all surfaces call.
A JavaScript Chrome extension, used to connect a LinkedIn session for the LinkedIn-based features.
Astro on Cloudflare Pages for the public website.
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