Ancor
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Providing reality to AI

Book a meeting →How it works

Observed, not generated.

Ancor gives AI teams the real-world observations their models are missing — continuously — so data gaps close faster and more precisely.

  • Captured in the physical world
  • Matched to your model’s current gaps
  • Delivered as a continuous stream

The gap

Models don’t fail at random. They fail where they’ve never looked.

Every model has gaps: situations it has never seen, physics it has never felt, people it has never met. Today those gaps get papered over with synthetic data and more compute.

Ancor fills them with observation. We work from the model’s actual failure surface, go to the real world, and bring back exactly what was missing — with provenance, context and consent attached.

Tracing a knowledge gap01 / 07

How it works

Identify. Observe. Complete.

  1. Identify

    Find the gap.

    We start from your evals, failure cases and training mix to pinpoint exactly what the model does not yet understand.

  2. Observe

    Capture what is missing.

    We query what already exists across private, proprietary and public sources. When it does not exist, we go into the real world and capture it.

  3. Complete

    Close it. Continuously.

    Structured, provenance-tracked observations delivered in your format, on your cadence, matched to the gaps the model has right now.

See the full pipeline

Why it matters

The same GPU budget produces more progress when compute is spent on data that contains more useful information and better matches the model’s current gaps.

Better data. More progress per GPU dollar.

Read our approach

The missing anchor

AI=Compute×Model×Data

Has its anchor. NVIDIA.

Has its anchors. Anthropic, OpenAI, Google, Meta.

No “NVIDIA of data”. Yet.

Where reality comes from

Seven surfaces. One stream.

Observation is sourced from the places where the real world actually happens — then matched to the gap it closes.

  • ChildhoodFirst experiences of the physical world.
  • GymRepetition under real variation.
  • SchoolCurricula sequenced to how a model learns.
  • OSSoftware and devices, as people use them.
  • EdgeSensors in the field, not the lab.
  • Collective MemoryHumanity sensing the world for AI.
  • PlaygroundWhere your team probes a model and requests observations.
Explore the surfaces

Next step

Find the gaps. Bring back what’s missing.

Tell us what you're training and where it breaks. We'll come back with a first read on the gaps and how we'd close them.

Book a meeting →or email us