Data labeling and evaluation for AI startups

Data labeling for AI startups has to fit how startups work: small batches, guidelines that change every sprint, and no procurement team. Deepen AI gives you a trained, managed team at a published rate, working in your tools, starting with a two-week pilot.

Who this is for

This page is for seed to growth-stage teams building language model products, agents, AI search or text classifiers. Usually the founders or engineers labeled the first data themselves. That stops working when an evaluation set needs hundreds of items, a launch needs a human review step, or a customer asks how you measure quality. The work is often too small or too changeable for a large data vendor, and too important for an open crowd with no continuity from one week to the next.

What our team does

• Build evaluation sets from your production logs, with reference answers
• Score agent and chatbot outputs against your rubric, before and after each change
• Produce pairwise preference data: which answer is better, and why
• Check retrieval-augmented (RAG) answers against the retrieved sources
• Label text training data to your taxonomy, and relabel it when the taxonomy changes
• Tag failure types to feed your error analysis
• Review low-confidence predictions in a human review queue, such as Amazon A2I

For more depth, see AI output review outsourcing and data annotation outsourcing.

How the engagement fits a startup

1. You share what you have. A rubric or guidelines if they exist, or simply 20 examples you consider correct. We draft guidelines from the examples for your approval.
2. We agree scope and train. Our ops team sets the people and hours in your pilot plan, with no sales call needed. The team trains on your examples, and we build a gold set with you.
3. We deliver, with a named team lead and QA sampling. The team works in your tool or spreadsheet. The team lead sends you each edge case as a guideline question, which is often how a startup's guidelines get written down for the first time.
4. You get a weekly report. Items done, QA sample results, failure types and open questions. It doubles as evidence when a customer or investor asks how you check quality.

Illustrative example: an evaluation set built from production logs

Illustrative only. The product, counts and intents are invented.

• Input: 500 support-bot conversations from last week, personal data masked by your pipeline
• Input, your brief: a 100-item evaluation set across your top 8 intents, each item with a reference answer from your help center
• Output, selection: 100 items across 8 intents. Two intents had only 7 and 9 usable conversations, so the shortfall came from the largest intents under your rule.
• Output, references: a reference answer for each item, citing the help-center article it came from
• Output, difficulty: an easy, medium or hard tag on each item, by your definitions
• Output, exclusions: 6 conversations removed because they still contained email addresses, and reported to you so the masking can be fixed
• Output, coverage gaps: the two thin intents listed for your next data collection
• Who did what: two team members selected items and wrote references. The team lead settled 9 unclear references with your contact. A QA reviewer re-checked a sample of 20 references.

How we keep labels trustworthy

Startup guidelines change, so every label is tied to a guideline version, and the weekly report notes the date of each change. Gold items measure each person in training and in live work. QA reviewers re-check a sample of finished items against the guidelines and the gold set. They do not re-check every item. The weekly report shows the acceptance rate on the sample and the label pairs or failure types that cause the most disagreement. See how we check quality.

Pricing scope

Labeling, rubric scoring, preference judgments and review of general English text are standard tasks at $5 per person-hour, with a named team lead and QA sampling included. Work starts with a two-week prepaid pilot: people × hours per week × 2 weeks × $5, with at least 20 hours per person per week. After the pilot, we invoice weekly in arrears, net 7, by card or ACH. Work that needs domain experts, such as code, medicine, law or finance, and other languages are quoted after our ops team reviews the task. See the pricing page.

When to use the enterprise team instead

Physical AI startups building robots, drones or driving systems need image, video, lidar and multi-sensor data, plus calibration and evaluation against real recordings. That is Deepen AI's core work, run by the enterprise team since 2017 and scoped per project. See enterprise AI data services.

Send your rubric or 20 good examples. We email you a pilot plan with a quote.

Request a pilot plan