EVIDENCE-FIRST AI VENTURE STUDIO

Evidence-first AI for research, analytics and high-stakes business decisions.

We are building AI-native products for complex workflows where information is fragmented, evidence must be traceable, and plausible guesses are not enough.

Mind Bureau is a founder-led venture studio combining specialized AI agents, technical collaborators and domain partners.

Bring us a hard problem Explore our thesis
Evidence flow diagram Four input sources — Public data, Primary sources, Internal data and Domain input — feed a central evidence system that checks provenance, claim verification, contradictions and coverage. One input carries a conflicting signal that is flagged rather than hidden. The evidence system produces decision-ready analysis, with uncertainty exposed rather than concealed. Conflicting signal — flagged, not discarded Public data Primary sources Internal data Domain input Evidence system Provenance Claim verification Contradictions Coverage Decision-ready analysis Uncertainty exposed
How Mind Bureau's evidence system turns fragmented input into decision-ready analysis
Input source Flows into Status
Public data Evidence system Traceable
Primary sources Evidence system Conflicting signal — flagged, not discarded
Internal data Evidence system Traceable
Domain input Evidence system Traceable
Evidence system checks Decision-ready analysis Provenance, Claim verification, Contradictions, Coverage
Decision-ready analysis — Uncertainty exposed

SELECTED FOUNDER TRACK RECORD

  • 20+ years Building and operating technology businesses
  • $1.2M Raised for a US technology startup
  • 1M+ Users reached

OUR THESIS

For decisions where plausible is not enough.

Most AI systems are optimized to produce fluent answers. Mind Bureau is being built around a different standard: products intended for important decisions must also produce conclusions that are supported, inspectable and safe to act on.

These principles form the design standard for products developed by Mind Bureau.

  1. Traceable evidence

    Every material claim should link back to the source or underlying data that supports it.

  2. Verified claims

    The system should check whether the cited evidence actually supports the conclusion being made.

  3. Explicit uncertainty

    Confidence, coverage gaps, missing evidence and unresolved contradictions should remain visible.

  4. Auditable outputs

    Users should be able to inspect how a conclusion was formed and reconstruct the path from evidence to output.

  5. Abstention by design

    When the evidence is insufficient, the system should say so instead of fabricating certainty.

CURRENT WORK

What we are building and testing now.

Mind Bureau is currently developing its first evidence-first products and the reusable research infrastructure behind them. The work below represents active development, research and field-tested product experience — not a mature venture portfolio.

PILOT — VALUE CONFIRMED

International trade intelligence

We are developing an evidence-first system for identifying and evaluating international B2B trade opportunities from fragmented market, company, regulatory and operational information.

The goal is to move beyond generic company lists and market summaries. The system is intended to form traceable commercial hypotheses: where an opportunity may exist, which organizations may have a relevant need, what evidence supports the hypothesis, what contradicts it, and what must be verified before commercial action. The approach is at pilot stage, and initial value has already been confirmed with a real trade operator.

Current workResearch methodology, source collection, organization and buyer identification, opportunity qualification, contradiction handling and validation with trade operators.

Looking forManufacturers, export and trade operators, and domain experts with access to real products, markets and commercial execution.

INTERNAL R&D

Evidence-first research infrastructure

We are building a reusable research layer for products that must produce traceable and inspectable conclusions rather than unsupported summaries.

The infrastructure is being designed around source provenance, entity resolution, claim verification, contradiction detection, evidence coverage, explicit uncertainty and auditable outputs.

PurposeTo provide a common foundation for market intelligence, operational analytics and other decision-critical products developed by Mind Bureau.

PILOT — VALUE CONFIRMED

Dental operations analytics

An operational analytics system for dental clinics that reconciles financial and practice-management data and connects reported metrics back to underlying records.

The work has demonstrated how apparently reliable management reporting can be materially distorted by duplicated, inconsistent or incorrectly interpreted source data.

Why it mattersThis product work helped shape the evidence-first thesis: important business metrics should not be presented unless they can be traced back to the records from which they were calculated.

INTERNAL R&D

Multi-agent decision support

An internal reasoning system that convenes a structured panel of specialized AI agents around a business question and returns a synthesized analysis with an explicit verdict, rather than a single fluent answer.

It is used to stress-test venture and product decisions before they are made — surfacing disagreement, missing evidence and weak assumptions that a single-pass answer would otherwise smooth over.

PurposeTo bring structured, multi-perspective reasoning to Mind Bureau's own decisions before any of this is offered more broadly.

CLOSED BETA

Coaching session analytics

An analytics system for professional coaches that turns raw session transcripts into structured insight about coaching quality and client progress — without questionnaires or scoring rubrics.

Every insight is checked against the transcript itself: a claim is kept only if a specific quote in the session actually supports it, and removed otherwise.

Current workRunning in closed beta with a working coach, refining the coach and client analysis tracks before wider release.

INTERNAL R&D

Structured personal knowledge system

An internal system for keeping facts, decisions and supporting evidence about ongoing work versioned and traceable over time, instead of scattered across notes and conversations.

It underpins how the studio tracks its own decisions and context across projects — a working example of the discipline being built into its products.

PurposeTo keep the studio's own working knowledge auditable and evidence-linked as projects and context evolve.

FIELD-TESTED PRODUCT WORK

Meta ads account auditing

A tool that reviews exported Meta advertising account data — Facebook and Instagram campaigns — and produces a structured audit report, surfacing account and campaign-level issues that are easy to miss when reading dashboards by eye.

Used in practice against live Meta ad accounts, it produces a report with prioritized findings instead of a raw metrics dump.

Why it mattersIt reinforced the same lesson as the studio's other field work: a reported number is only useful once someone has checked what it was actually calculated from.

OPERATING MODEL

A founder-led venture studio built around the problem.

Mind Bureau starts with a real workflow, not a generic AI capability. We identify a recurring and economically meaningful problem, validate it with people who operate inside the domain, build the evidence and decision architecture, and develop a repeatable product around what has been proven.

Specialized AI agents support research, validation, orchestration, documentation and repetitive execution. Technical collaborators are assembled around the requirements of each product. Domain partners bring the workflow knowledge, data, access, distribution or execution capability that makes the venture real.

  1. Find a hard problem

    Identify a recurring workflow where people spend significant time assembling information, resolving contradictions or making decisions under uncertainty.

  2. Validate the economics

    Map the current process, users, available data, cost of delay, cost of error and willingness to adopt a better solution.

  3. Build the evidence system

    Design the source, provenance, verification, confidence and audit layers before automating the final output.

  4. Develop the venture

    Turn the validated workflow into a repeatable product, test distribution with the right partner and scale only what the evidence supports.

We build ventures, not generic AI projects.

Mind Bureau is not a general-purpose development agency. We do not take on arbitrary build requests. We pursue problems that fit the venture thesis and can become repeatable products with a credible path to adoption.

PUBLIC PRODUCT EXPERIMENT

CommonTime

CommonTime is a free public experiment exploring low-friction scheduling and AI-assisted coordination.

Open CommonTime

FOUNDER

Founder-led by design.

Mind Bureau is founded and led by Pavel Dmitriev, an entrepreneur and C-level operator with more than 20 years of experience building and developing technology businesses.

Previously, Pavel built a US technology startup that raised $1.2 million and reached more than 1 million users.

At Mind Bureau, he leads venture discovery, product thesis, business design and partner development — assembling AI agents, technical collaborators and domain partners around each problem.

View Pavel on LinkedIn Contact Pavel
PARTNER WITH MIND BUREAU

Bring us a hard problem.

We want to hear from operators and domain experts who repeatedly face a workflow in which people must assemble fragmented information, judge conflicting evidence and make a decision with material consequences.

Strong opportunities usually combine a recurring problem, identifiable economic cost, accessible users, relevant data and a credible path to distribution or execution.

A strong partner brings at least one of the following:

  • Deep domain knowledge and access to the real workflow
  • Relevant proprietary or hard-to-access data
  • Direct access to users, customers or distribution
  • Operational capability to execute in the market

Not a fit:

  • A generic chatbot idea without a specific workflow
  • Commodity software development or staff augmentation
  • A concept with no access to users, data or domain expertise
  • AI novelty without a clear economic reason to exist

Our submission form is temporarily unavailable. Please email us directly and describe the workflow, the cost of a wrong or late answer, and what you can bring to the venture.

Email hello@mindbureau.ai