Data Legion

[Data Legion @datalegion]

Data Legion is now live on @MonidHQ 🚀

AI agents on Monid can now pull Data Legion’s person, company & contact data. They pay per call, with no signup or contract.

Huge thanks to the Monid team for making it happen.

Try it → https://t.co/ldODUZwDI2

#AIAgents #B2Bdata #AgenticAI

[Data Legion @datalegion]

@anjum_ai Fable

[Data Legion @datalegion]

Data Legion is now on @firecrawl Alexandria, a library of data providers for AI agents.

More demand for data now comes from agents. We want to be wherever they're looking.

Thanks to the Firecrawl team. Go check it out.

https://t.co/uZMYKNwDEW

#AIAgents #B2BData

[Data Legion @datalegion]

Most data APIs hide the price behind "Contact sales."

Ours is on the page. Flat per-record, $10 to start, no contract. One credit = one record.

What enrichment should cost:

https://t.co/Gz1ZBdD5GP

#DataEnrichment #FlatRatePricing #B2BData #EnrichmentAPI #PayPerRecord

[Data Legion @datalegion]

Most prospect research is the same workflow done by hand at every B2B company: open LinkedIn, scrape titles, pull funding from Crunchbase, drop notes in a doc, paste into the CRM. Thirty to ninety minutes per account. Stale by the next quarter.

The structured-data half of that workflow is exactly what an agent should do. We wrote up a Claude Code + datalegion-cli agent that produces a research dossier and per-contact email-sequence outlines from a target domain. Setup to first brief in about ten minutes, and every CLI invocation in the post is real and runnable.

https://t.co/b8KhJLODxv

#B2BSales #SalesAutomation #AIAgents #ProspectResearch #ClaudeCode

[Data Legion @datalegion]

Four ways to access the same person and company data. We built the CLI last, after watching AI agents reach for the terminal every time.

https://t.co/KxMGkiZn6U

#B2BData #AIAgents #DeveloperTools #MCPServer #APIDesign

[Data Legion @datalegion]

GTM engineer job postings grew 200%+ YoY. The role exists because someone has to build the pipes between data and revenue.

The enrichment API is the integration that matters most. Here's how to evaluate one:

https://t.co/ghcMxR9hGC

#GTMEngineering #DataEnrichment #RevOps #SalesAutomation #APIIntegration

[Data Legion @datalegion]

Clay users: you can enrich tables with Data Legion's Person API in about five minutes.

Step-by-step setup guide, endpoint, auth, column mapping, response paths:

https://t.co/7JXLZs4vsM

#DataEnrichment #GTMOps #SalesIntelligence #ContactData #RevOps

[Data Legion @datalegion]

GTM engineer job postings grew 200%+ YoY. The role exists because someone has to build the pipes between data and revenue.

The enrichment API is the integration that matters most. Here's how to evaluate one:

https://t.co/ghcMxR9hGC

#GTMEngineering #DataEnrichment #B2BSales #RevOps #SalesOps

[Data Legion @datalegion]

Five things to evaluate: coverage on your ICP, verification methodology, refresh cadence, compliance posture, and delivery format.

Framework here:

https://t.co/t6BOEyozXV

#B2BData #DataQuality #RevenueOperations #GoToMarket #SalesIntelligence

[Data Legion @datalegion]

You have an email address. You need the name, title, company, phone, and seniority.

Reverse email lookup does that in under a second. Here's how it actually works and how to integrate it:

https://t.co/RkxdZfN8bY

#DataEnrichment #B2BData #RevOps #SalesIntelligence #GTM

[Data Legion @datalegion]

Build a pipeline that enriches leads the moment they enter your system. Full tutorial with code:

https://t.co/bQv216Ehro

#DataEnrichment #MarketingOps #RevOps #SalesOps #LeadEnrichment

[Data Legion @datalegion]

Person data has a trust problem.

You buy employment records, skills data, education history, contact info. You feed it into models that drive workforce planning, talent analytics, market sizing. Decisions worth millions.

But you can't verify any of it programmatically. Not at the field level. There's no timestamp telling you when that job title was last confirmed. No source count showing how many independent signals back it up. You get a record and you trust it. That's the quality assurance model for most of the market.

On the sell side it's the same story. Enterprise buyers want to audit what they're paying for. The best most providers can offer is "we refresh our data regularly." That's a marketing claim, not a quality metric.

The fix is straightforward. Field-level freshness dates and independent source counts on every person record. Each field carries its own metadata. When was it last verified? How many sources confirmed it? You can query it, filter on it, put it in a contract and hold someone to it.

The person data market has run on trust and good vibes for too long. It's time for receipts.

#PersonData #DataQuality #WorkforcePlanning #DaaS

[Data Legion @datalegion]

Stop buying data like it’s a commodity. It’s infrastructure.

Most companies treat data purchases like ordering office supplies. Cheapest vendor, bulk buy, move on.

Then they wonder why outbound falls flat and sales is flagging lead quality.

You’re not buying a product. You’re building a foundation.

Bad data corrupts everything downstream. Segmentation, scoring models, rep productivity, reporting. It compounds every quarter you let it slide.

Nobody shops for the cheapest concrete when building a skyscraper. You spec it. You test it. You hold suppliers to standards. Same energy needed here.

Define your data architecture before you buy anything. What fields actually matter? What’s your decay tolerance? How does this feed into activation?

Audit what you already have. Most teams are sitting on 30-40% garbage records they keep paying to enrich. Clean house first.

Hold vendors to real SLAs. Match rates, freshness guarantees, fill rates by segment. Put it in the contract.

Your data stack is a system, not a shopping list. Intent, firmographics, technographics, contact data. They’re layers that need to work together.

#DataStrategy #B2BMarketing #RevOps #GTM

[Data Legion @datalegion]

I talk to companies that use data in their products every day. They all say the same thing: we need more contacts, more records, bigger lists, wider coverage. Makes sense on paper. More data, more people reached.

But the teams actually winning with data aren't hoarding the most records. They've got the cleanest ones.

Build a dataset optimised for recall and here's what you get. A massive list. Then your SDRs start dialling and half the numbers are dead. Emails bounce. Sender reputation tanks. Marketing reports inflated TAM numbers that sales can't convert. 500,000 contacts and somehow your pipeline got worse.

Flip that. Build for precision and your reps actually trust the data, so they use it. Emails land. You get real engagement metrics, not padded stats. Campaigns hit real buyers with real titles at real companies. Every dollar on outreach is working.

The unglamorous truth about B2B data is that a smaller, accurate dataset will outperform a massive, noisy one almost every time. Nobody's quota gets hit by a contact that bounced. Nobody closes a deal from a record that's two job changes out of date.

I'd rather give a customer 10,000 contacts they can build pipeline on than 100,000 that look good in a report but fall apart the second someone tries to use them.
Volume is easy to sell. Precision is hard to build. That's exactly why it's worth more.

#ContactData #B2BData