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.
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.
1Clientsbring high-stakes decisions
2Expertsframe what matters
3Analystscheck evidence and quality
4AI automationscales repeatable analysis
Status Quo
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.
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.
The Inventurist Model
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.
How strategy decisions are streamlined
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.
Frame the decision
Experts and clients define the question, the relevant dimensions, and the evidence that could change the assessment.
Structure the evidence
Analysts collect public, private, and proprietary signals, then label sources, conflicts, freshness, and gaps.
Model what is hidden
AI automation and domain models estimate variables that are important but not directly observable from outside.
Generate the scorecard
The pipeline produces scores, confidence, benchmarks, gaps, and action playbooks in a consistent format.
Review with people
Analysts and domain experts check the result, adjust where needed, and explain what the assessment means.
Refresh as facts change
Scorecards can be updated as new filings, approvals, grid events, market signals, or project evidence appear.
Principles
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.
Repeatable frameworks
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.
Map
Benchmarks
What does normal or good look like in this domain?
Benchmarks establish the terrain: baselines, distributions, peer context, and the variables that matter.
Locate
Scorecards
Where is this subject on the map?
Scorecards place a named company, site, target, load, or project against the benchmark context.
Navigate
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.
Our story
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.
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.