Training data that actually works in the real world
Diverse perspectives. Preserved intent. Superior data. AI Signal Lab combines data labeling from a diverse annotator network with a validation layer that checks intent, not just format compliance - so your training data holds up once it leaves the lab and meets real users.
Models trained on homogenous data fail where it matters most. In real markets. With real users.
Indic languages make up roughly 1% of the data most large models are trained on, despite representing 18% of the world's population. Sarvam AI proved what happens when you fix that - its diverse-annotator approach outperformed a model four times its size on Indic language benchmarks.
Your annotation vendor tells you their work is high quality. You have no way to verify that at scale. So you either pay for expensive spot checks, or you ship and find out from your users.
From raw data to validated training data
- 01
Scope
Tell us your guidelines, data type, and quality bar. We match you with annotators who fit the task and the market.
- 02
Annotate
Our diverse annotator network gets to work, drawing from the demographics and markets your model needs to understand.
- 03
Validate
Every batch runs through our Intent Preservation Engine. We catch misunderstood intent, not just formatting errors.
- 04
Deliver
You get production-ready training data in 2 to 14 days, not the 6 to 21 day industry average.
Three things that move the needle on training data
Annotators from the Tier 2 and Tier 3 Indian cities your models will actually serve, not just metro talent pools.
Confirms the annotator understood what they were labeling - the difference between annotation that looks right and holds up in production.
Workflows, quality guides, and access to our annotator network for teams that want more control, not less.
Pick the level of control you need
Managed Services
Full data annotation, handled for you. Diverse annotators, validated quality, delivered on your timeline.
Learn moreManaged Services + IPE
Managed annotation with full visibility into quality. Every batch scored by the Intent Preservation Engine.
Learn moreAnnotation Platform
Run your own annotation workflows on our platform, backed by our diverse annotator network. Launching soon.
Learn moreEarly results from the field, not just the pitch
annotations in our structured pilot
annotators across 5 Indian states
intent preservation, validated
annotators, scaling by end of year
Built for teams that cannot afford to get this wrong
Stays in your environment
For the IPE API, your data never leaves your environment. IPE runs in-process, scores it, and returns validation metadata. Nothing is stored on our side.
Encrypted, under NDA
For Managed Services, all data is encrypted at rest, and every annotator works under NDA.
SOC 2 in progress
A SOC 2 audit is currently in progress. We would rather tell you where we are than stay quiet about it.
Built by people who have done this at scale before
AI Signal Lab was not started by people learning the annotation industry from scratch. The founding team has managed over $160 million in AI and ML data infrastructure programs, run operations overseeing $20 billion in annual spend, and worked inside a leading annotation platform managing GenAI data programs across more than 10,000 contributors. This team has already lived inside the exact problems this company was built to solve.
in AI/ML data infrastructure programs managed
in annual spend overseen by team operations
contributors across GenAI data programs
Common questions
How is AI Signal Lab different from Scale AI or Appen?
We compete on quality verification, not just cost or scale. Every batch runs through our Intent Preservation Engine before it reaches you, so you're not relying on spot checks or trust alone.
How fast can you deliver?
2 to 14 days depending on volume and complexity, against an industry average of 6 to 21 days.
Do you only work with AI labs, or can annotation vendors use you too?
Both. Managed Services and Managed Services + IPE are built for AI labs and enterprise teams. The Annotation Platform tier is built for annotation vendors who want more visibility and control over their own quality process.
What does pricing look like?
Pricing depends on tier, volume, and data type. Book a demo and we'll walk you through a quote based on your specific project.
How do you ensure annotator quality across different markets?
Our annotators are sourced directly from the regions and demographics your model is meant to serve, then validated through our Intent Preservation Engine before delivery.
Looking at Scale AI, Appen, or Surge?
Most annotation vendors sell you on speed or scale. We built AI Signal Lab because neither matters if the model still fails once it meets real users in real markets. If you're comparing vendors, ask them one question: how do they verify quality beyond a spot check? That's the question our Intent Preservation Engine was built to answer.