ChatGPT
Claude
Perplexity
Google AIA sample on their task
Send us [50 prompts / 200 frames / 20 documents] from your [task] and we'll label them with [our experts] this week, with our agreement scores. You keep the data either way.
How to get customers for a data labeling or AI training data company
Reach AI labs and ML teams when they hire, raise and ship, and be the vendor AI assistants name when buyers ask.
Trusted by teams at
How to get customers for a data labeling or AI training data company
Own one data type and one buyer, such as expert-written reasoning data for model labs or lidar annotation for autonomy teams. Find teams at the moment they need data: ML and research hiring, a new funding round, a model launch or a move into a new modality. Reach the research lead and the data lead with a paid sample on their task. And publish the pages AI assistants cite, because buyers ask ChatGPT and Perplexity for vendors before they ask a colleague.
- 01
Pick one data type and one buyer
Write down what you label best and for whom: preference data for model labs, medical image segmentation for health AI teams, or multilingual speech for voice companies. Vendors who do everything sound like a crowd marketplace. A buyer should read your homepage and know which of their problems you solve. Your call; tell Revnu and it works from it.
- 02
Get your quality and security story in writing
Publish how you measure quality, such as inter-annotator agreement, gold sets and review rates, and how you handle data: NDAs, access controls, where labelers sit and what you never do with customer data. Have a security pack ready to send. This step is yours; Revnu doesn't do it.
- 03
List the teams that need data now
Find companies hiring ML, research or evaluation roles, companies that just raised, and labs that shipped a model or moved into a new modality. Note the dated reason for each. Revnu builds the list and shows the evidence on each lead.
- 04
Reach the research lead and the data lead
Write to the person who defines the data and the person who runs vendors, with a paid sample offer on their task. Name the model, the modality and the gap. Revnu finds both people and a verified email for each, and writes to them in your voice.
- 05
Publish the pages AI assistants cite
Write one page per data type and use case, comparisons with the vendors buyers already know, and plain explanations of your quality method. These are the pages AI answers quote when a buyer asks for annotation vendors. Revnu writes and publishes them, and tracks which AI answers name you.
- 06
Turn samples into contracts
Agree the quality bar and the decision date before the sample starts. Report agreement scores and errors honestly with the delivery. Ask for the next batch and the MSA in the same message. This step is yours; Revnu drafts the follow-ups.
Who buys from a data labeling or AI training data company
A research or ML lead defines what data they need, a head of data or ML ops runs the vendors, and procurement and security sign off on access to models and data.
Research leads at model labs
They need post-training data: preference pairs, expert reasoning traces, evaluations and red-teaming. A new model run or a weak benchmark result sends them looking for specialists.
Heads of data and ML ops at AI companies
They manage several vendors and care about throughput, quality metrics and turnaround. A vendor missing deadlines or a new product line opens the door.
Autonomy and robotics perception teams
They label camera, lidar and radar data at volume, often with strict taxonomies. A new sensor, a new city or a new vehicle platform means new labeling work.
Applied ML teams at enterprises
They fine-tune models on their own documents, calls or images and need domain experts who can label them under an NDA. Data security and where the labelers sit often decide it.
Founders of seed and Series A AI startups
They need a first dataset or an evaluation set fast and have just raised. They buy on a sample and a quick start.
Where to find them
- Job posts for ML, research and evaluation roles
- A company hiring research engineers, an evaluations lead or a data operations manager is about to need data. Posts often name the modality and the models they work on.
- Model launches, papers and technical reports
- Release notes and papers say what data a team used and where results fell short. A gap they name is a pitch you can write.
- Funding announcements for AI companies
- A fresh round funds compute and data. Teams that raise often start a new training run soon after.
- NeurIPS, ICML, CVPR and ACL, plus their workshops
- Research leads attend in person and workshop organisers list the people working on your exact problem. Dataset and benchmark tracks are full of buyers.
- Hugging Face, GitHub and research Discords
- Teams publish datasets, model cards and evaluation code in the open. Who maintains what tells you who owns data at each lab.
- AI assistants and comparison searches
- Buyers ask ChatGPT, Claude and Perplexity for annotation vendors and search "data annotation companies", about 1,900 times a month in the US (Google Ads keyword data, November 2026). If those answers don't name you, you're off the shortlist.
What to say
Experts, not crowds
Our [domain] labelers are [qualified practitioners], so [model] gets [reasoning traces / evaluations] a generalist workforce can't write.
Timed to the launch
Congratulations on [model]. Your report mentions [weakness]. We build [data type] for exactly that, and can start on a small batch this month.
Security and control
Your data stays in [your cloud / our secure workspace], labelers sign NDAs, and nothing is used to train anything else. Happy to send our security pack to [their security lead].
Which channels fit
- Search pages and AI answersStrong fitBuyers shortlist vendors by asking AI assistants, so pages on each data type, comparisons and quality methods decide whether you're named. Revnu writes and publishes these pages and tracks which AI answers name you.
- Founder-led email to research and data leadsStrong fitA note that names their model, their gap and a sample offer reaches the person who defines the data. Revnu finds the teams and the right people, and writes to them in your voice.
- LinkedIn outreach and postsStrong fitML leads are findable by title and read posts that show real labeled examples and quality numbers. Revnu runs the outreach and drafts posts you share with one tap.
- Research conferencesSometimesThey put you in a room with research leads, and a dataset or benchmark paper builds credibility. Revnu builds the list of speakers and workshop organisers in your area and follows up after.
- Search ads on Google and MicrosoftSometimesSearches like "lidar annotation services" or "RLHF data provider" come from buyers shortlisting. Revnu runs these ads and asks before it spends.
- Facebook and Instagram adsWeak fitML leads aren't picking a data vendor while scrolling a feed, and these ads often draw people looking for labeling work instead. Spend on search and LinkedIn first.
Lead generation
Lead generation for data labeling companies
Data labeling leads come from signals that a team is about to train or evaluate. ML and research engineering job posts, roles with "evaluation", "post-training" or "data operations" in the title, funding rounds, model launches and papers that name a data gap are the strongest. A move into a new modality, such as video or speech, is another. For each company, find the research lead who defines the data and the person who manages vendors. For example, a medical annotation company could list health AI startups that raised a seed or Series A this year and are hiring ML engineers with imaging experience. Revnu searches 800 million+ people profiles and 200 million+ companies, then reads each company's job posts, papers and news before it keeps the lead.
Cold email
Cold email for data labeling companies
Cold email to ML teams works when it shows you read their work. Name the model, paper or job post, the data it needs and the sample you'll do. Skip "high-quality data at scale", which every vendor says. Give one quality number you can back and say who your labelers are. One example: "Your new speech model's report mentions weaker results on accented Spanish. We record and transcribe Latin American Spanish with native speakers. Send me 30 test prompts and I'll return labeled audio with our agreement scores this week." Send from the founder, keep it short, and follow up with a sample of real labeled data rather than a reminder.
Advertising
Advertising for data labeling companies
Bid on the data type, not the category. "Data labeling" draws job seekers looking for labeling work, while "lidar annotation services", "RLHF data provider" or "medical image annotation" come from buyers. Exclude job and career terms from every campaign. Send clicks to a page for that data type with real examples and your quality method, and offer a paid sample instead of a demo. LinkedIn ads can reach ML leads by title and company, and work best with a concrete example of labeled data. For example, a speech data company might run Google ads on "speech dataset provider" and LinkedIn ads to heads of audio ML at voice AI companies.
How AI teams decide
Most teams start with a paid sample or pilot batch, judged on quality metrics they set, before a larger contract. Security review covers who sees the data and where labelers work, and procurement adds an MSA. A fast sample, clear quality reporting and a security pack sent before anyone asks move it along.
One prompt, worked through
We supply expert-written reasoning and evaluation data for model labs, written by PhDs in maths and physics. Find AI companies that raised in the last six months and are hiring research or evaluation engineers. The research lead at each, with a sample offer.
- 1Revnu reads the company's website and works out that buyers are research and post-training leads at model labs and AI companies training their own models.
- 2It finds companies that raised in the last six months and are hiring research or evaluation engineers, reads each job post and recent model release, and saves every company to Leads with the evidence.
- 3It finds the research lead at each and writes to them in the founder's voice with the sample offer. The first sequence waits for the founder's yes.
- 4It writes and publishes pages on reasoning and evaluation data so AI answers name the company, and reports every morning on Slack or iMessage what moved.
Frequently asked questions
How do AI labs choose a data labeling vendor?
Usually on a paid sample judged against their own quality bar, then on security and turnaround. Many start their shortlist by asking an AI assistant or a peer which vendors do their data type. Being named in those answers and having a fast sample ready are what get you tested.
Why does GEO matter for data labeling companies?
Because buyers ask ChatGPT, Claude and Perplexity for vendors, and those answers quote the pages they can find. Revnu has done this before: AI answers had named Minicor 0 times in 471 before Revnu, and have named it 2,274 times since, after 24 new guides (Minicor customer story).
How do we stand out from crowd labeling marketplaces?
Specialise and show it. Name the experts who label, publish your quality method and agreement scores, and pick data types where a generalist workforce can't do the work. Buyers pay more for data they don't have to relabel.
Which signals show an AI team needs data now?
Hiring for ML, research or evaluation roles, a fresh funding round, a model launch, a paper or report that names a weakness, and a move into a new modality. Revnu searches for all of them and shows the evidence on each lead.
What does it cost?
Plans start at $99 a month, and Revnu starts on what you typed as soon as you sign up. Cancel any time in Settings.
Does anything run before I sign up?
No. Nothing is checked or generated on this page. Revnu does the work inside your account, where it can read your website, keep what it finds and carry on from there.
Where do I learn how Revnu works?
The docs at revnu.com/docs/get-started walk through your first day, and revnu.com/docs/what-revnu-can-do lists everything it can do.
Last updated November 7, 2026
Hand it to Revnu
Keep going
- How to get customers for an AI startupBuyers are tired of AI demos. Win them with a narrow workflow, proof on their own data, and answers for security.
- How to market a developer tool and get more usersWhere engineers find new tools, what to say to them, and which channels to skip.
- How to get customers for a B2B SaaS companyWhere software buyers compare vendors, which channels fit your price point, and the outreach that gets you onto a shortlist.
- How to rank in ChatGPTGet named when buyers ask ChatGPT what to use in your category.
- How to check your AI visibilitySee whether ChatGPT, Claude, Gemini and Perplexity recommend you, and who they name instead.
- How to find clientsFind the companies that need what you sell, and the right person at each. For a consumer audience, Revnu reaches people through search content, AI answers and ads, and it emails and messages businesses and the people who work at them.
- Revnu for data labelingRevnu finds the AI labs and ML teams that need training and eval data now, reaches the research or data lead in your voice and books the call.