Enterprise documents for frontier AI
Real Enterprise Documents. Not Templates.
We source, anonymize, and deliver private enterprise documents — the operational artifacts that public datasets miss. Board packages, financial models, compliance reports, internal memos. Created by real teams making real decisions.
Enterprise Documents
Real Operational Artifacts
Not synthetic templates — real PDFs, spreadsheets, memos, board packages, compliance documents created by real teams in real enterprises. Each document carries the decision-making context that AI needs to learn how enterprises actually work.
Board Resolution 2024
Q3 Revenue Model
Due Diligence Report
Investment Committee Memo
Cap Table Waterfall
Compliance Package
Management Presentation
Architecture Review
Vendor Evaluation Matrix
Headcount Planning Model
Why private documents?
Public datasets capture templates and examples. Private enterprise documents capture how real teams actually make decisions under constraints — the signal AI needs to be useful in the real world.
What makes them valuable?
Connected context. A board resolution is useful. A board resolution linked to the financial model, cap table, and committee memo that produced it is training gold.
How we source
Direct enterprise partnerships, shutting-down companies, legacy system migrations, and document estates. Every artifact is anonymized before delivery.
Decision Context
The Why Behind Every Choice
Code and documents are artifacts. But what makes them valuable for AI training is the context that produced them — the tickets that drove the work, the specs that shaped it, the conversations where trade-offs were debated, and the postmortems when things broke. We acquire connected datasets where the decision trail is intact.
Tasks & Planning
Specs & Design
Communications
Reflections
Decision Thread — 4 messages
Decision points highlighted
VP Engineering
Mar 12, 10:47 AM
After load-testing Stripe's API against our throughput requirements, the 2.9% fee at our volume projects to $1.8M/yr.
Building in-house with Adyen raw processing drops that to $0.4M but adds 6 months and 3 FTEs.
Recommending the hybrid: Stripe for consumer, Adyen direct for enterprise invoicing above $10K.
Head of Platform
Mar 12, 2:15 PM
Agreed on hybrid. Two concerns:
1. PCI compliance scope expands significantly with direct processing. Need InfoSec review before committing.
2. Reconciliation between two processors adds ops burden — who owns the ledger?
CTO
Mar 13, 9:02 AM
Approved hybrid approach. Adding CFO for budget sign-off.
InfoSec review scheduled for Thursday. If PCI scope is manageable, we proceed with Adyen for enterprise tier in Q3.
Platform team owns reconciliation. Let's scope the ledger service this sprint.
CFO
Mar 13, 3:30 PM
Budget-wise this works. The $1.4M delta covers the engineering investment in 14 months at current volume.
One flag: if enterprise tier grows as projected, we hit Adyen's volume discount at Q4. Factor that into the ROI model.
Signing off on the hybrid. Finance will track blended processing cost monthly.
Why context matters
A code change without its ticket is a diff. A ticket without the spec that drove it is a task. A spec without the conversation where it was debated is a document. We acquire connected datasets where the full decision trail — from conversation to artifact — stays intact. That's what teaches AI how enterprise work actually happens.
Specialized Domains
Private Data From Specialized Industries
Public datasets cover these industries at a surface level, but the real operational artifacts — internal workflows, decision processes, proprietary documentation — stay behind corporate firewalls. We source private enterprise data directly from these domains.
Medical & Clinical
Clinical trial protocols, diagnostic workflows, lab results, pharmacy operations, claims processing. De-identified patient pathways and treatment decision trees.
3D & CAD Engineering
Mechanical design files, BIM models, product specs, assembly instructions, tolerance analyses. The engineering decisions behind physical products.
Manufacturing & Industrial
Quality control documentation, bill of materials, production schedules, equipment maintenance logs, safety inspections. The operational backbone of physical production.
Scientific & Research
Lab notebooks, experimental protocols, peer review correspondence, analysis pipelines. How discoveries are made, validated, and documented in private labs.
Agriculture & Supply Chain
Crop management systems, livestock tracking, cold chain logistics, cooperative management, quality certification workflows across food and materials.
Energy & Mining
Field operations documentation, extraction planning, environmental assessments, equipment maintenance, safety certifications across energy and natural resources.
Why specialized domains?
Public AI training data skews heavily toward tech and finance. But the industries with the highest operational complexity — healthcare systems, manufacturing floors, energy infrastructure — keep their documentation behind corporate firewalls. We partner directly with enterprises in these domains to source the private operational artifacts that shape how work actually gets done.
Global Coverage
Enterprise Data In Every Language
Business processes, corporate governance, and decision-making work differently across cultures and regulatory environments. An AI trained only on English-language US enterprise data is missing how most of the world actually works. We source from enterprises globally.
Japanese
稟議書 — 決裁プロセス
Nemawashi consensus-building, ringi-sho approval workflows, multi-layered quality control documentation
Manufacturing, Electronics, Precision Engineering
German
Vorstandsbeschluss — Protokoll
Mittelstand manufacturing documentation, industrial engineering specs, apprenticeship training systems
Automotive, Industrial Engineering, Chemicals
Mandarin
董事会决议 — 审批流程
Cross-border trade documentation, manufacturing QA processes, supply chain coordination across regions
Manufacturing, Logistics, Hardware
Korean
이사회 결의 — 승인 절차
Semiconductor fabrication workflows, shipbuilding engineering documentation, quality assurance systems
Semiconductors, Shipbuilding, Telecom
Tamil
குழு தீர்மானம் — ஒப்புதல்
Textile manufacturing operations, agricultural cooperative management, traditional medicine documentation
Textiles, Agriculture, Healthcare
Arabic
محضر اجتماع — قرارات الإدارة
Private equity deal workflows, Islamic finance structures, real estate development coordination
Private Equity, Real Estate, Construction
Portuguese
Ata de Reunião — Deliberação
Mining operations documentation, agricultural supply chain management, ethanol production workflows
Mining, Agriculture, Energy
French
Procès-verbal — Délibération
Luxury goods supply chain, vineyard and agriculture cooperatives, pharmaceutical manufacturing
Luxury, Agriculture, Pharma
Kazakh
Хаттама — Шешім қабылдау
Oil and gas field operations, livestock management systems, mineral extraction documentation
Oil & Gas, Mining, Agriculture
Berber
ⴰⵙⵉⵡⴹ — ⵜⵉⵎⵙⵙⵓⵔⵜ
Artisanal cooperative management, traditional agriculture documentation, craft production workflows
Cooperatives, Agriculture, Crafts
Spanish
Acta de Junta — Acuerdos
Telecom infrastructure operations, wine production management, financial services workflows
Telecom, Agriculture, Finance
Quechua
Tantanakuy — Kamachiy
Agricultural cooperative documentation, textile production records, community water management systems
Agriculture, Textiles, Water Management
Why global coverage matters
A Japanese ringi-sho approval workflow is structurally different from a German Vorstandsbeschluss. An Arabic PE deal memo follows different conventions than a Brazilian mining operations report. AI that only trains on English-language US enterprise data learns one way of working. We source from enterprises globally so models learn how business actually operates across cultures and regulatory environments.
Anonymization
The How, Not The Who
The point of enterprise artifacts for AI training isn't who made the decisions — it's how they made them. Our anonymization pipeline strips personally identifiable information while preserving the operational knowledge, decision points, and process patterns that make the data valuable.
Raw Artifact
Anonymization Pipeline
Anonymized Output
What stays, what goes
Names, emails, company identifiers, and proprietary details are replaced with consistent anonymous tokens. The business logic, financial structures, decision rationale, and process flows remain intact — exactly what AI needs to learn how enterprises work.
Private data. Verified environments.
Production-ready agents.
Tell us what you need — we will scope availability, anonymization, and pricing.