FULL-COMPANY ARCHIVE
Company Operating Archive
Complete operating histories of real companies - code, business data, communications, documents, and databases - as a training corpus for frontier models.
Inspect a real sample
Representative records in the delivery format, ready to inspect before licensing the full dataset.
Application database surface (anonymized)
Representative surface-inventory row, not raw data. A database is represented by counts, schema, dates, and size - never document payloads.
{
"archive_id": "arc_company_a",
"surface_id": "srf_0042",
"surface_class": "database",
"source_system": "mongodb",
"record_count": 4200000,
"count_basis": "estimate",
"date_start": "2021-02-01",
"date_end": "2026-05-01",
"storage_bytes": 18800000000,
"schema_summary": "{collections:[users,sessions,events,orders],indexes:12}",
"linkability": "joins to code surface via account_id and to comms via user_id",
"sensitivity": "pii_present_deidentification_required",
"provenance": "founder_owned_consented",
"confidence": "confirmed"
}Code surface integrity-checked by the evaluator
Representative. count_basis is exact here because it comes from a real repo evaluator run (eval_kit v0.6.2).
{
"archive_id": "arc_company_a",
"surface_id": "srf_0008",
"surface_class": "code",
"source_system": "git",
"record_count": 955,
"count_basis": "exact",
"date_start": "2024-05-01",
"date_end": "2026-06-01",
"storage_bytes": 60290000000,
"schema_summary": "{repos:3,files:982,primary_language:TypeScript,has_test_runner:true}",
"linkability": "issue refs join to comms and project surfaces",
"sensitivity": "secrets_stripped",
"provenance": "founder_owned_consented_evaluated",
"confidence": "confirmed"
}Broker / trade data surface
Representative. The ambient exhaust an archive deliberately captures - orders, fills, positions, P&L - that ordinary workspace exports miss.
{
"archive_id": "arc_company_b",
"surface_id": "srf_0117",
"surface_class": "market_trade_data",
"source_system": "broker_export",
"record_count": 310000,
"count_basis": "estimate",
"date_start": "2023-01-01",
"date_end": "2025-12-31",
"storage_bytes": 2400000000,
"schema_summary": "{tables:[orders,fills,positions,pnl_snapshots],fields:64}",
"linkability": "strategy configs join to code surface; research notebooks join to docs",
"sensitivity": "no_pii_account_ids_masked",
"provenance": "founder_owned_consented",
"confidence": "needs_review"
}Record shape
Every field, its type, whether it can be null, and a representative value.
| Field | Type | Constraint | Description |
|---|---|---|---|
| archive_id | string | required | Stable id for one company archive (one founder-owned entity). e.g. arc_company_a |
| surface_id | string | required | Stable id for one data surface within the archive (a repo, DB, mailbox, log store, dataset). e.g. srf_0042 |
| surface_class | string | required | Category: code, database, object_storage, logs, comms, docs, scraped_data, market_trade_data, legal_finance, llm_eval_traces. e.g. database |
| source_system | string | nullable | Originating system or tool. e.g. mongodb |
| record_count | int | nullable | Count of the primary record (commits, rows, files, emails, messages, objects, orders). e.g. 4200000 |
| count_basis | string | required | Whether record_count is exact or an estimate. Estimates are never presented as exact. e.g. estimate |
| date_start | string · ISO-8601 date | nullable | Coverage start. e.g. 2021-02-01 |
| date_end | string · ISO-8601 date | nullable | Coverage end. e.g. 2026-05-01 |
| storage_bytes | int · bytes | nullable | Approximate logical footprint of the surface. e.g. 18800000000 |
| schema_summary | string · json | nullable | Field, table, or collection inventory: names, types, indexes - structure without payload. e.g. {collections:[users,orders],indexes:12} |
| linkability | string | nullable | How this surface joins to others in the archive (shared user/account/ticket/repo keys). e.g. joins to code via issue refs |
| sensitivity | string | required | PII or confidentiality level and de-identification path. e.g. pii_present_deidentified |
| provenance | string | required | Ownership and consent plus how the surface was accessed; founder-owned, consented, sanitized. e.g. founder_owned_consented |
| confidence | string | required | Confidence in company association: confirmed, likely, needs_review. e.g. confirmed |
Operational Record
Email, chat, documents, and project history from real companies - how work actually happened, end to end.
Systems & Data
Application databases, logs, warehouse queries, and broker/market data - the structured exhaust of a running business.
Decision Trail
Strategy docs, research, and legal/finance materials that connect decisions to outcomes across a company lifecycle.
How it is built
- 01
Founder-owned, consented sourcing
Archives are assembled only from companies the founder owns or founded, with consent. This is the property that separates whole-company corpora from scraped data.
- 02
Whole-surface inventory
Every accessible data surface is inventoried - code and repo history, application databases, logs and telemetry, communications, docs, stored datasets, market and trade data, legal and finance, and LLM/eval traces - represented by counts, schemas, date ranges, sizes, and de-identification notes, never raw payloads.
- 03
Cross-surface linkage
Surfaces are joined where they share keys (a repo to the issues that reference it, a database account to the contract that names it, comms to the work they coordinate) so the archive reads as one operating history, not disconnected dumps.
- 04
Sanitization and de-identification
Credentialed remotes, secrets, and raw PII are stripped; sensitive categories are represented through counts, schema, date ranges, and reviewed sample pathways. Buyer-facing names are sanitized.
- 05
Provenance chain capture
For datasets that went through real buyer diligence, the full chain is preserved: request, DDQ, data room, scoped trial, delivery, compliance confirmation, and legal closeout.
- 06
Confidence labeling and point-in-time
Each surface is tagged confirmed, likely, or needs-review with an exact-vs-estimate basis; histories are dated so an operating period can be replayed point-in-time.
How we validate
What each evaluation measures and how it is run. Where no benchmark is published, we show the methodology and say so.
Agentic Operating-History Replay
Measures
Whether an agent, given a company archive up to a point in time, would have taken the operating action the company actually took next - the decision, the code change, the customer reply, the trade.
Method
Reconstruct the linked archive as of a cut date; pose the in-flight situation; withhold subsequent surfaces; compare the agent action to the recorded outcome across code, ops, and comms.
Result
Methodology-stage. No trained-model or benchmark result is reported; this is a data-and-methodology contribution.
Codebase Integrity / Packaging Check
Measures
Whether a company repositories are real, substantial, and eval-grade before inclusion.
Method
Run the reference codebase evaluator (eval_kit v0.6.2) per repo to extract commit, PR, issue, CI, and test metrics plus production-quality signals; review before scaling to more repos.
Result
Methodology-stage. These are packaging and integrity checks on real repositories, not an agent-performance benchmark.
Ground truth
What correct means for this data, and how it is established.
Ground truth
The company own recorded operating history - the code that shipped, the decisions taken, the customer and counterparty interactions, the trades and database state that actually occurred - reconstructed point-in-time from the linked archive.
How it is established
Replay-based comparison against the real recorded outcome. Integrity is anchored to founder-ownership, explicit consent, and a preserved provenance chain (request, DDQ, data room, trial, delivery, compliance, legal closeout). Codebase surfaces are integrity-checked with the reference evaluator before inclusion.
Agreement
Correctness is anchored to the production record and to consent and provenance rather than a separate human-rater pass. No inter-rater agreement figure is published at this stage.
Frontier-Model Training
Whole-company corpora - code plus the business context around it - for models that reason about how real organizations operate.
Agentic Eval Ground Truth
Replay real operational histories to test whether an agent would have matched what a real company actually did.
Acquisition-Ready Packages
Curated, consented, provenance-clean archives assembled for AI-training-data buyers and acquisition programs.
How you load it
Delivery
S3, Parquet, Custom export, Restricted data room / acquisition package
Formats
JSON, Parquet, mbox, SQL, Git bundle
Auth
Restricted acquisition or training-data package under a signed license. Founder-owned, consented, PII-scrubbed; credentialed remotes and secrets stripped; buyer-facing identities sanitized. Sensitive categories are represented as counts, schema, and date ranges with reviewed sample pathways. Full provenance under NDA.
Cadence
One-time archive of the whole-company operating history, optionally re-snapshotted.
Request access.
Restricted-scope evaluation access for qualified teams. We share real samples, full schema, and provenance under a mutual NDA.