How to choose AI for a corporate finance or M&A advisory firm: a checklist
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Short answer
Choose AI software for a corporate finance firm on four things: where client data is stored and processed and who can see it; whether the output matches your firm's own templates and standards; whether the knowledge it builds belongs to your firm; and whether the vendor helps your team change how it works. The checklist below turns these into questions you can put to any vendor, followed by Mindro's own answers.
The checklist
| Question to ask | Why it matters for a corporate finance firm |
|---|---|
| Where is data stored and processed, and can EU-only be guaranteed? | Client materials are confidential and often covered by NDAs and GDPR |
| Who are the sub-processors, and is there a Data Processing Agreement? | You need to know which model and cloud providers touch client data |
| Can access be limited per tool and per classification level? | Not every AI tool should see client data under NDA, let alone inside information |
| Is every data access logged by user, tool and time? | You must be able to show who saw what, and through which tool |
| Is client data used to train models? | Your deal knowledge should not improve a model everyone else uses |
| Can every figure in a draft be traced to its source? | IMs and pitch materials must stand up to buyer and client scrutiny |
| Does it follow your own templates and output in the formats you use? | Most firms work in PowerPoint and in their own house style |
| How deep is the integration with where your documents live? | Most deal history sits in SharePoint, CRM, email and meeting notes |
| Who owns the knowledge the system builds, and can you take it with you? | Your firm's knowledge is part of its value |
| Which models does it use, and can you switch without renegotiating compliance? | Models change quickly; your data rules should not |
| What implementation, training and engineering support is included? | Software alone rarely changes how a team works |
| Can it connect to the AI tools your team already uses, under your rules? | Some teams already work in Claude, ChatGPT or Microsoft Copilot |
How Mindro answers the checklist
| Question | Mindro |
|---|---|
| Data storage and processing | SaaS run fully in Europe; EU data residency guaranteed. Institutions can also run Mindro in their own VPC, on-premise, or ring-fenced in another region |
| Sub-processors and DPA | Mindro is data processor under a DPA. Sub-processors: Google (Vertex AI / Google Cloud), Weaviate, and Anthropic (Claude) when Claude is used |
| Per-tool, per-level access | Configured per client in the Institutional setup: Mindro and the client agree which data levels each connected AI tool may use |
| Audit trail | Every data access logged by user, tool and time |
| Training on your data | We do not train on your data |
| Traceability | Every figure in a draft links back to its source document |
| Templates and formats | IM drafts follow your firm's own template; output is PowerPoint, with PDF export from there |
| Integrations | SharePoint (deep, proven), CRM, email, meeting notetakers, Microsoft 365 add-ins, and data providers such as Gain and Inven |
| Ownership | Your firm owns its firm brain, portable by contract |
| Models | Claude, Gemini and Mistral; an AWS-based setup on request also allows OpenAI models |
| Implementation | Transformation support, training and data and AI engineering included in every plan. Services |
| Other AI tools | MCP and API connections configured per client on request. Setups for institutions |
Questions to ask in a demo
- Show a draft built from our own materials, and trace three figures back to their source.
- Show the draft in our template, in PowerPoint.
- Show who accessed a document, when and through which tool.
- Explain what happens to our data, and our firm's knowledge, if we leave.
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