Trust
Define who can see, approve, reuse, and share AI output.
Investment teams should not trade speed for uncontrolled exposure. MindLab is designed around private workspace boundaries, human review, source discipline, and separation between internal work and approved external knowledge.
Discuss trust requirementsTrust Console
Internal workspace
Full source room, notes, partner debate
Internal only
Approved records
Saved decisions and reviewer notes
Approval gate
External advisor
Approved facts only
Restricted answer
Unsupported claim
No source or weak evidence
Blocked
Current external response
12 approved facts available. Internal notes, valuation debate, and partner comments blocked.
Workspace separation
Customer materials stay inside the customer workspace and support only that customer’s agreed workflows.
No public model training by default
Client documents, deal flow, proprietary outputs, and customer records are not used to train public models by default.
Human approval
AI prepares drafts and organizes context. The customer controls final records, final language, and external communication.
Source-aware review
Important claims can preserve source references, missing-information markers, and review flags so teams can verify the work.
Advisor boundaries
Internal advisors can use permissioned internal knowledge. External advisors answer only from approved knowledge.
Scoped enterprise controls
Dedicated deployment needs, data residency, advanced permissions, audit reporting, and deletion commitments can be scoped with the customer.
Enterprise Review
The trust conversation should produce written boundaries.
Before sensitive materials move through the first workflow, MindLab and the customer should define what is ingested, who can access it, what becomes reusable, and what commitments belong in the agreement.
Data use
Customer materials operate the customer workspace and agreed workflows.
No sale of institutional data. No public model training by default.
Workspace boundary
Source rooms, company records, reviewer notes, and outputs stay scoped to the customer workspace.
Dedicated deployment, stricter isolation, or residency requirements can be scoped.
Permission model
Internal work, approved records, and external-facing answers use different knowledge boundaries.
Advanced roles, groups, and workspace administration can be defined during implementation.
Review trail
Important claims can retain source links, missing-information markers, reviewer flags, and approval state.
Audit reporting and export requirements can be included in the customer agreement.
Retention and deletion
Customer records are retained to support agreed workflows unless removed under the contract.
Deletion timelines, backup handling, and export commitments should be written into the order form.
Compliance status
MindLab does not represent certifications unless separately stated in a signed agreement.
Security questionnaires, vendor review, and future certification needs can be discussed before rollout.
Safe Evaluation
You should not have to share the whole firm to test one workflow.
A serious evaluation starts with a narrow workflow, a clear review standard, and a boundary around what can be ingested, retained, reused, or shown externally.
Discuss fit first
Start with workflow shape, source types, review standards, and success criteria.
Use representative materials
An implementation review can begin with limited or sanitized materials before broader sharing is justified.
Keep reviewers in control
AI output stays draft until the customer reviews sources, language, and conclusions.
Save only what is approved
The customer decides what becomes part of the company record and what remains blocked.
Operating Principles
Trust is not a paragraph. It is how the workflow behaves.
Serious investment teams need source review, approval, permission boundaries, and a record of what the firm has accepted.
MindLab does not make investment decisions.
External-facing outputs should use approved knowledge only.
Fit can be discussed before broad confidential sharing.
Trust behavior is configured into the workflow, not left to generic prompts.
Next Step
Define the trust boundary before the workflow runs.
We can discuss data handling, permissions, approval rules, and what should stay internal before sensitive materials enter a workflow.