The questions leaders ask before trusting AIPath with the growth decision.

Growth Decision Intelligence is software that generates and tests growth strategy before a company commits to it. Where operational Decision Intelligence platforms decide loans, fraud, claims and supply chains, Growth Decision Intelligence decides the question above all of those: where should the company grow next? AIPath is the world's first Growth Decision Intelligence Platform.

AIPath is the Growth Decision Intelligence platform. AIPath maps every way your company could grow, ranks the options by the ROI that matters at your stage, simulates customer response, and tests the strongest in market before the budget moves. AIPath then hands every team one sequenced roadmap, with the reasoning and the proof attached.

Because the cost of building collapsed and the cost of knowing what to build did not. AI made execution cheap, so the scarce decision moved upstream: which strategy deserves the quarter. Gartner formalised Decision Intelligence with its first Magic Quadrant in January 2026; every platform named decides operational risk, and AIPath launched Growth Decision Intelligence for the CEO in the same window. AIPath has been building for that seat since 2023, on method research that began seven years earlier and has never stopped.

No, and the distinction matters. The platforms in Gartner's first Decision Intelligence Magic Quadrant are built for operational decisions, and they are excellent at them. Growth strategy is not their mission. AIPath's dedicated mission is prescriptive growth strategy generation and testing, the parent decision the others execute. The category Gartner formalised is the shelf; AIPath is building the growth seat on it.

AIPath builds a digital twin of your customers, competitors and market, then runs one loop continuously: map hundreds of thousands of growth scenarios, rank them by the ROI that matters at your stage, simulate buyer response to the strongest, test the winners in market against controls, and hand every team the instruction with proof attached. Each cycle starts where the last one ended, so the analysis compounds instead of resetting every planning season.

A ranked, tested shortlist of your three highest-confidence growth opportunities, each with its evidence and its named fallback if the market says no. One sequenced roadmap per team. Weekly re-ranking as conditions change. Board-ready exports. Not a slide deck: a living system that updates as your market moves.

A website is enough to begin: AIPath builds the first analysis from your public footprint and a short set of inputs about your goal and your customer. From there, the model deepens with what you connect and confirm, and where decisive data does not exist, AIPath generates it with live in-market tests. You do not need a data team, a warehouse, or a clean CRM to start.

You learn it for the price of a test instead of a quarter. A null result is information: AIPath registers where the value is not, climbs back up the ranked list to the next strongest path, and re-targets the test, with the next move named before the result came in. It does not pronounce. It proposes, and then the market decides.

The first working session runs on your company's numbers and takes 45 minutes; you keep the analysis either way. In one live session, a telco leader made more progress in 17 minutes than his 50-person team had in 18 months. The compounding value builds from there, cycle by cycle.

Yes. Product-market fit is one of the growth decisions AIPath ranks and tests: for earlier-stage companies, AIPath maps the paths to PMF, ranks them by the ROI that matters at your stage, and tests the strongest before you commit a quarter to them. The same loop then carries you from PMF into scale, so the analysis compounds rather than restarting at each stage.

A general model is the engine, not the platform for continuous growth. AIPath attaches every growth idea to the experiment that would prove it, redevelops your data to be testable, runs the most promising simulations and experiments in your market, and feeds the verdict back into a model of your company that gets more intelligent every week. A chatbot's confident answer with hidden reasoning would be a slot machine with good grammar; AIPath shows the reasoning behind every recommendation.

Dashboards describe what already happened; the growth decision is about what has not happened yet. You will not find tomorrow's best move in yesterday's dashboards. AIPath is forward looking: it simulates futures and validates them in market, rather than reporting the past in higher resolution.

A consulting engagement is episodic: it validates once, presents, and leaves, and the learning leaves with it. AIPath runs continuously, tests rather than argues, and stores every learning in your company's own twin, so the intelligence stays and compounds. A tier-1 management consultancy runs AIPath itself, which says where the leverage sits.

Your team could build parts: a scenario model, a test harness, some agents. What does not transfer by inspection is the method under AIPath, a structured model of how businesses grow, accumulated across years of practice and hundreds of deployments, plus the discipline that pairs every simulated future with an in-market validation. The build would cost more than the subscription and arrive without the corpus that makes it sharp.

The difference is testable: AIPath does not stop at recommendations. Every recommendation carries the experiment that would prove it, runs it with controls, and re-ranks as evidence arrives. Statistical validation and machine learning separate what caused a lift from what merely coincided. If a tool cannot show you its tests, it is a very articulate opinion.

Your company's twin is ring-fenced to you. AIPath never shares or resells inputs, never trains shared models on your strategy, and deletes free-analysis inputs after your analysis. What your company learns through AIPath belongs to your company, and it is precisely that accumulated memory that makes leaving costly and staying compounding.

AIPath Pte. Ltd. is registered in the Republic of Singapore and aligned to the PDPA. Data handling terms are stated plainly in the terms and privacy policy, and enterprise deployments agree scope, residency and access in writing before anything connects.

AIPath was founded by David Isaac, former ASEAN Innovation Co-Lead at EY-Parthenon and former Chief Growth Officer at GrowthOps (ASX:TGO), after 22 years on the growth-decision problem across corporate strategy, venture building and commercial due diligence. AIPath took first place in the 2025 AI Agents Global Challenge (USD 1 Mn prize pool), builds inside the HP Garage 2.0 programme (first ten globally, Cohort 1) and the BLOCK71 x Microsoft Enterprise AI Accelerate programme, and has completed a pilot with the California innovation lab of a global electronics leader.

Forty five minutes, run live on your company's data, not a slide deck. AIPath maps your growth opportunities in front of you, ranks them by the ROI that matters at your stage to weed out busy work, and shows the strongest roadmaps to test and execute with priority, sequence, dependencies and evidence. You keep the analysis either way, and no preparation is required beyond showing up.

AIPath is priced on the decision loop, not on seats, so the price tracks the value events the loop delivers rather than headcount. Current tiers are on the pricing page. The comparison that matters: one wrong quarter costs more than a year of AIPath, and the free mini-demo and working session cost nothing.

No. The simulation is free, takes 30 seconds, and needs no sign-up. The working session is free, takes 45 minutes, and you keep the analysis either way. Commitment in AIPath happens on purpose, at an explicit gate, after the evidence, which is exactly how AIPath treats your growth decisions too.

The centre of gravity is the CEO of a $5-50M ARR B2B company, and the same decision lands on other desks: advisors and fractional executives run AIPath across client rosters, accelerators and investor portfolios deploy it cohort-wide, and enterprise innovation teams use it to rank what deserves the next tranche. If you carry a growth decision, the working session will tell you quickly whether AIPath fits.