Messy annotation in. Clean, ML-ready data out.
Without losing what the annotation actually meant.
IPE turns messy, real-world annotation into clean, ML-ready training data. Available bundled inside a Managed Services engagement, or as a standalone product your team runs its own batches through directly.
Most quality checks confirm a label was applied correctly. We check whether the annotator understood the task in the first place.
That distinction is the whole reason IPE exists. It checks intent, fidelity, and meaning across 15-plus dimensions and 18-plus sub-dimensions before approving or flagging an annotation — not just whether the format is right.
Bundled, or on its own
Bundled with Managed Services
Every Managed Services engagement can add IPE as an opt-in layer. It runs full context-aware validation against your project guidelines and the actual job, flags anything that doesn't meet the bar, and gives you a clear signal on whether the annotation completed is actually correct. Billed on tokens plus a one-time setup fee, on top of your Managed Services engagement.
- Runs inside your Managed Services workflow
- Same 15+ dimension validation
IPE as a Service, standalone
Already have your own annotation pipeline? Run your own batches through IPE directly, independent of any Managed Services engagement. Same full context-aware validation, same 15-plus dimension check, same flagging. Billed on tokens plus a one-time setup fee.
- Vendor agnostic — works with any annotation source
- Runs independently of Managed Services
What actually happens to a batch
Your raw annotation comes in — messy, real-world, exactly as it was produced. IPE checks it against your project guidelines and the actual job context, scoring across dimensions like intent, fidelity, and meaning. Anything that doesn't meet the bar gets flagged with a reason attached. What passes comes out the other side as clean, auditable, ML-ready training data.
Batch in
Raw, messy annotation from any source
Scored
Checked against guidelines and job context
Flagged or approved
Clear reasons on every flagged item
Clean data out
ML-ready, auditable training signal
Where your data actually goes
Your data doesn't leave your environment to be validated. IPE runs in process on your annotation batches and returns validation metadata — nothing is stored beyond what's needed for delivery.
Billed on what you actually use
IPE is billed on tokens processed, plus a one-time setup fee. Bundled pricing (as part of a Managed Services engagement) and standalone pricing both work this way. Exact rates depend on volume and complexity.
Talk to us about pricingWant to see the dashboard on your own data?
We can walk you through a live sample so you see exactly what gets flagged and why, before you commit to anything.
Request a live demoCommon questions
What's the difference between the bundled and standalone versions?
Same validation engine, same 15-plus dimension check. The bundled version runs inside a Managed Services engagement as an opt-in add-on. The standalone version runs independently, on annotation batches from any source, including your own pipeline.
Can I use IPE on annotation that wasn't done by AI Signal Lab?
Yes, the standalone version is built exactly for that. IPE is vendor agnostic.
How is this different from a basic formatting pass?
A formatting layer cleans up structure without checking meaning. IPE checks whether the annotator actually understood the task, validates against your real project guidelines, and flags anything that doesn't hold up — not just formatting.
What happens when a batch gets flagged?
You get a clear signal on what was flagged and why, so your team can decide whether to send it back for correction or review directly.
Ready to see what your annotation actually looks like after IPE?
Book a demo and we will walk you through a live sample, so you can see exactly what gets checked before you decide anything.
Book a demo