Work you can check.

01 · Custom model training

Most repeated work does not need a frontier model. It needs a small one that has seen your parts, your documents or your requests, and runs where your data already is.

02 · Interpretability experiments

Interpretability explains a model's behaviour from its internals: which neurons and features respond to an input, and how the output changes as the input, the prompt or the environment changes. We test published claims on open-weight models with causal methods (ablation, activation steering, probes and weight edits), each against a control, and publish every number, including the claims that failed. You can rerun each one. The same tests check the models we train for you.

03 · MCP for knowledge work

Your spreadsheets and documents stay in your Google Drive or Dropbox; the file is the record. Every AI assistant, and every run it starts, reads and edits that file through one layer, the workbook. Its schema declares each table's columns, types, required cells, keys and references, so the tooling that keeps code correct now keeps your operating records correct.

Accurate

Every edit is checked against the schema before it is saved. A change an assistant proposes is checked on a copy first and redone with the errors, so you are only asked to approve one that passes.

Resilient

Every version is kept and can be restored. An edit based on an old version, or over a file someone changed in Excel meanwhile, is rejected, never merged over.

Fast and cheap

An assistant reads and edits one table or section by its address, not the whole file, so a change costs only what it touches.

{
  "answer": "yes",
  "claims": [{ "figure": "public float", "value": "$9.6B",
               "as_of": "2026-06-28" }],
  "sources": ["MRNA 10-K, cover page",
              "biotech-diligence-7fq2 · §Moderna"]
}