Observations
Continuous observation, not one-off datasets.
A standing pipeline from your model’s gaps to the real-world evidence that closes them.
The pipeline
Gap analysis → Request → Query → Get / Create
- 01Gap analysisWe work from your evals, failure cases and training mix to map what the model does not yet understand — precisely enough to act on.
- 02RequestEach gap becomes a specified observation request: modality, context, conditions, volume and the eval it should move.
- 03QueryWe check what already exists — your private data, Ancor’s proprietary corpus, and verified public sources — before anything is captured.
- 04Get / CreateWhat exists is delivered. What doesn’t is captured in the real world, by people, with context and consent, and delivered on a continuous cadence.
Anatomy of an observation
Not a file. A record of something that happened.
Every observation carries what a model needs to learn from it, and what a team needs to trust it.
Observation № 00417 — specimen
- Modality
- Video · audio · sensor · text · interaction
- Context
- Where, when, under what conditions, doing what
- Provenance
- Who captured it, how, with which device
- Consent
- Recorded at capture, revocable, auditable
- Gap link
- The eval or failure case this observation addresses
- Cadence
- One-off, weekly, or continuous stream
Three tiers of data
Private. Proprietary. Public.
Private
Your own data. Analysed for gaps, never pooled, never leaves your boundary.
Proprietary
Observations Ancor captures and curates. Licensed to you, provenance-tracked, built to match gaps not to fill volume.
Public
Verified, deduplicated and filtered public sources — used only when they genuinely close a gap.
Reality surfaces
Where the observations come from.
Childhood
Give AI its first experiences of the real world: objects, hands, weight, cause and effect. The basics a model never got to live through.
Gym
The same task under a thousand real conditions. Repetition with the variation the physical world provides for free.
School
Structured curricula of observations, sequenced to how a model learns rather than how data happens to be available.
OS
Everyday software and device use as people actually do it — hesitation, correction and all.
Edge
Sensors and devices in the field. The long tail no lab reproduces.
Collective Memory
Humanity sensing the world for AI: a growing, consented corpus of lived observation.
Playground
Where your team probes a model’s gaps and requests the observations that close them.
Ancor ⊷ Playground
Bring a model. Probe where it breaks. Request what fixes it.
The Playground is where your team explores a model’s gaps directly and turns them into observation requests. Early access is open to a small number of teams.
Start with a gap
Tell us where your model breaks.
We'll come back with a first read on the gaps and how we'd observe our way to closing them.