Data rooms for AI startups.
AI startup diligence adds questions about where training data came from and whether the company had the right to use it, how models are evaluated, what compute is committed, which third-party models the product depends on, and what inference costs do to margins. Prepare a data provenance register, model cards and evaluation results, compute contracts and a gross margin breakdown.
AI room
- 01 Company
- 02 Data provenance
- 03 Data agreements
- 04 Models
- 05 Evaluations
- 06 Third-party models
- 07 Compute
How it works for a startup.
Investors in AI companies separate products built on third-party foundation models from companies training their own. For the first group, they look at dependence on one model provider, the provider's terms and pricing, and what makes the product hard to copy. For the second, they look at training data rights, compute commitments and the team's research record.
Data provenance has become a core legal question. Reviewers ask for a register of datasets with source, licence or contract, any scraping and its legal basis, personal data handling and customer data used for training. Customer contracts are read for what the company may do with customer inputs and outputs.
Margins are the other focus. Inference costs per user, committed cloud or GPU spend, reserved capacity contracts and pricing per seat or per use decide whether revenue growth turns into gross profit. Acquirers add model documentation, evaluation results, safety testing and any regulatory obligations in markets where customers operate.
Top 10 data rooms for this job.
Our five measures, re-weighted for ai. Places 11 to 17 are listed below the cards.
- Setup speed 19%
- Investor experience 17%
- Access control 25%
- Diligence tools 21%
- Startup pricing 18%
- 1 9.2/10First-rate
- $149 monthly
- 14-day free trial
- 8/12 feature checks
- Investor Q&A
- NDA e-signing
- Dynamic watermarks
- View-only and download control
- SOC 2 (hosting)
- 2 8.3/10Strong pick
- Quote only
- Free trial offered
- 9/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- GDPR
- 3 8.2/10Strong pick
- Quote only
- Free trial offered
- 8/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- 4 8.1/10Strong pick
- $250 monthly
- Free trial offered
- 8/12 feature checks
- Investor Q&A
- NDA e-signing
- Dynamic watermarks
- View-only and download control
- SOC 2
- 5 8.1/10Strong pick
- Quote only
- Demo, no trial
- 10/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- GDPR
- 6 8.1/10Strong pick
- Quote only
- Free trial offered
- 7/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- 7 7.8/10Strong pick
- Quote only
- Demo, no trial
- 8/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- GDPR
- 8 7.7/10Good option
- $299 monthly
- Free trial offered
- 7/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- 9 7.7/10Good option
- $55 monthly
- Free trial offered
- 9/12 feature checks
- NDA e-signing
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
- HIPAA
- 10 7.6/10Good option
- Quote only
- Demo, no trial
- 8/12 feature checks
- Investor Q&A
- Dynamic watermarks
- View-only and download control
- Viewer activity log
- SOC 2
- ISO 27001
Show places 11 to 17
- DealRoom 7.5
- SmartRoom 7.4
- ShareVault 7.2
- Onehub 7.1
- Digify 6.8
- Venue by DFIN 6.6
- DocSend 6.5
Folder index.
A starting structure for this room. Rename folders to fit your company and keep the numbers.
Charter, cap table, financing documents, board consents.
Register of every training and evaluation dataset with source, licence, date and restrictions.
Data licences, partnerships, customer terms on use of inputs and outputs for training.
Model cards, architecture summaries, training runs, version history.
Benchmark and internal evaluation results, red teaming and safety testing reports.
API terms of model providers used, usage volumes, fallback options.
Cloud and GPU contracts, reserved capacity, committed spend, monthly compute cost.
Inference cost per user or request, gross margin by product, pricing.
Top customer contracts, usage, pilots and conversion to paid.
Privacy policy, DPA, acceptable use policy, regulatory assessments.
Invention assignments, patents, publications, key researcher agreements.
Need every file by stage? Use the startup data room checklist.
Must-have features.
| Provider | View-only and download control | Dynamic watermarks | Investor Q&A | AI assistance | Viewer activity log |
|---|---|---|---|---|---|
| Ellty | |||||
| iDeals | |||||
| Ansarada | |||||
| SecureDocs | |||||
| Datasite | |||||
| Firmex | |||||
| Drooms | |||||
| CapLinked | |||||
| Citrix ShareFile | |||||
| Intralinks |
- View-only and download control
- Set each file to view only, print or download, and change it later without resending anything.
- Dynamic watermarks
- Stamps the viewer email and the time across every page, so a forwarded screenshot points back to its source.
- Investor Q&A
- Funds and buyers ask questions against a file; you assign, answer and publish replies, and the thread stays on record.
- AI assistance
- Vendor-listed help such as auto-filing, document summaries, search across files or drafted answers.
- Viewer activity log
- Shows which investor opened which file, for how long and when, and exports the record.
Set up the room in 6 steps.
- 1
Build the data register
List every dataset used for training or evaluation with its source, rights basis and restrictions. Gaps here are the first legal question.
- 2
Check customer terms
Confirm what your contracts say about training on customer data and make sure practice matches the paper.
- 3
Document the models
Write a model card for each production model: purpose, data, evaluations, limits and known failure modes.
- 4
Show the cost curve
Break down gross margin with inference and compute costs, and show how they move with volume and model choice.
- 5
Protect model and data files
Keep weights and raw datasets out of the room. Share documentation view-only with watermarks.
- 6
Track reviewer questions
Answer data rights and safety questions in Q&A so legal and technical answers stay consistent.
Common mistakes.
- No record of where training data came from or under what terms.
- Customer contracts that forbid training on their data while the product does it.
- Gross margin shown without inference costs or with credits that will expire.
- One third-party model provider with no fallback and no review of its terms.