AI-native strategy consulting

Inventurist is building a marketplace where subject-matter expertise, analyst judgment, trusted data, and AI automation deliver repeatable decision support services.

We are specialized in value chain analysis with a focus on AI infrastructure and specifically data centers.

Inventurist is delivering a sense-making platform where evidence, models, analysts, and experts work together to support high-stakes decisions.

  1. Clients bring high-stakes decisions
  2. Experts frame what matters
  3. Analysts check evidence and quality
  4. AI automation scales repeatable analysis

Strategic decisions need a better operating model

The hardest business decisions are too important for generic AI answers, too dynamic for one-time consulting memos, and too judgment-heavy for dashboards alone.

Consulting is trusted, but hard to scale

Expert work can be thoughtful and tailored, but it is often slow, expensive, and difficult to refresh when facts change.

Software is repeatable, but thin on judgment

Dashboards can organize data, but they usually leave the hardest interpretation to the client at the exact moment judgment matters.

Generic AI is fast, but hard to trust

AI can summarize quickly, but strategic decisions require traceable evidence, consistent methods, and accountable review.

An AI-native service marketplace for expert-led decision support

Inventurist brings together clients, data providers, analysts, domain experts, and AI automation around a specific decision. The output is not raw information. It is a live scorecard with benchmarks and playbooks to take action.

A Scorecard brings data, analysis, and expertise together so every stakeholder sees the same reality before a decision is made.

Clients bring named decisions

A campus, target, load, account, supplier, market, asset, or project that needs a defensible assessment.

Experts bring judgment

Subject-matter experts define what matters and interpret what the numbers mean in context.

Analysts bring quality control

Analysts source inputs, check evidence, resolve conflicts, and keep outputs grounded.

AI brings scale and repeatability

AI automation gathers signals, normalizes evidence, runs models, and refreshes structured outputs.

From expert judgment to repeatable scorecards

The method turns messy, changing signals into structured assessments that can be delivered, reviewed, refreshed, and compared over time.

  1. Frame the decision
    Experts and clients define the question, the relevant dimensions, and the evidence that could change the assessment.

  2. Structure the evidence
    Analysts collect public, private, and proprietary signals, then label sources, conflicts, freshness, and gaps.

  3. Model what is hidden
    AI automation and domain models estimate variables that are important but not directly observable from outside.

  4. Generate the scorecard
    The pipeline produces scores, confidence, benchmarks, gaps, and action playbooks in a consistent format.

  5. Review with people
    Analysts and domain experts check the result, adjust where needed, and explain what the assessment means.

  6. Refresh as facts change
    Scorecards can be updated as new filings, approvals, grid events, market signals, or project evidence appear.

The principles behind Inventurist

The company is built around a simple belief: AI becomes more valuable when expert judgment, evidence quality, and accountability are designed into the service.

Human judgment remains accountable

AI supports the work. People remain responsible for interpretation.

Expertise can be codified

Good judgment can be turned into repeatable methods, not just one-off advice.

Evidence is first-class

Important numbers should be traceable to sources, assumptions, and gaps.

Confidence matters

A useful answer shows how strong the evidence is, not only what the answer is.

Complex systems need systems thinking

Strategic outcomes depend on linked variables, constraints, and timing.

Change is continuous

Decision products should be refreshed when the facts move.

Map, Locate, Navigate

AI-native strategy consulting needs a simple repeatable path: establish the terrain, place the decision on that terrain, then choose the route forward.

Benchmarks

What does normal or good look like in this domain?
Benchmarks establish the terrain: baselines, distributions, peer context, and the variables that matter.

Scorecards

Where is this subject on the map?
Scorecards place a named company, site, target, load, or project against the benchmark context.

Playbooks

How do we move from here to the destination?
Playbooks turn the current position into scenarios, tradeoffs, and actions that can be reviewed over time.

Supporting strategic decisions since 2019

Inventurist has spent more than seven years building AI-enabled solutions for strategic decision-making, research-heavy workflows, and complex business problems.

Clients
10 Fortune 500 companies and 1st tiers of institutional finance

Team
Decades of experience in building and commercializing AI enterprise software

Technology
Proprietary sense-making AI engine

Leadership Team

Inventurist is led by people with long experience in AI, strategy, enterprise operations, product, growth, and finance.

Cirrus Shakeri, Ph.D.

CEO/CTO & Co-Founder
Cirrus has worked on AI-enabled strategic decision support for years. His AI background goes back to Ph.D. work in multi-agent modeling and simulation of complex systems.

Gil Heydari, MBA

COO/CAO & Co-Founder
Gil brings finance, capital markets, and enterprise strategy experience to the work of turning scorecards into useful commercial decisions.

Advisors

Strategic advisors guide Inventurist's growth, product development, financial model, and SaaS platform direction.

Leandro Chique

Product

Sam Heidari

Growth

Randy Sternke

Financials

Kaj van de Loo

SaaS

Legal Counsel

Legal counsel supports the company as the platform, partner model, and customer base grow.

Daniel Zimmermann

WilmerHale
Daniel Zimmermann has extensive experience in complex corporate transactions and venture technology issues. Daniel has experience advising in a wide variety of areas in technology.

Bring us the decision you need to make

Built for expert collaboration
The platform vision depends on a broader network of analysts, data providers, and subject-matter experts. The goal is to make specialized knowledge easier to apply, easier to audit, and easier to deliver where strategic decisions are being made.