FAQ & Changelog

AIPath is an AI‑native product‑strategy engine that helps you find and prove product‑market fit faster. It automates customer/market insight, runs strategy simulations (your “digital twin”), prescribes what to build now/next/never, and keeps GTM assets in sync as you iterate.

Think of it as a self‑serve strategist that turns assumptions into tested plans—with a built‑in loop to learn from results.

Watch AIPath at Work

Founders, product leaders, and PMM teams who need evidence‑based decisions—fast.

AIPath is used by early‑stage startups validating direction, scale‑ups prioritizing roadmaps, and innovation teams pressure‑testing new bets.

If you’re deciding what to build, for whom, and why it will win, AIPath is designed for you.

Three big ones: validation, prioritisation, and alignment.

1. Validate before you build using digital‑twin simulations and rapid tests. Clarify what combination of product & GTM next steps will drive the impact you prefer.
   

2. Prioritise objectively with AI‑augmented scoring that ranks features and bets by expected impact on your business. Useful for customizing roadmaps to address churn issues, or activation rates, or margin compression, for example.
   

3. Align product and GTM: Every strategy change auto‑updates collateral, so launch materials never go out of sync.

Most tools describe; AIPath prescribes.

Because AIPath is designed to support the workflows of product and GTM leaders, it's AI-Native engine can get you started in just minutes; no complex, expensive or tedious setup required. Just point it as your existing (even unstructured) data and it does the rest.

Roadmap tools track work, and you manually populate the tool ground up. Research tools collect inputs, not insights, and don't proactively prescribe next steps and optimize from many tradeoffs .

AIPath fuses both with simulation + prescriptive recommendations, then closes the loop by generating GTM assets and refreshing the plan based on results.

It’s an end‑to‑end strategy → execution → learning system, not another tracker.

No, AIPath works even from light inputs and gets better as simulations & experiments enrich your data. Begin with any materials you have, including your website, product documentation, sales brochures and backlog information.

Four steps you can run in a day: Define → Simulate → Decide → Launch & Learn.

• Define: import a few facts (customer, problem, competitors) or start from websites, pitch decks, sales brochures, Jira tasks/ backlogs.
  

• Simulate: AIPath spins scenarios in a digital twin—segments, features, messaging per target niche. It's focused around customer jobs to be done, so it help you define feature and GTM priorities in actionable ways.
  

• Decide: it ranks “now/next/never,” explains trade‑offs, and uses clear scoring ( that you control) to help you create the right roadmap sequences and strategy for your current situation.
  

• Launch & Learn: it generates landing pages/emails/decks; run tests; results flow back to reprioritise automatically.

Most teams ship a defensible strategy and first experiment within 24–72 hours.

Expect fewer wasted sprints, faster iteration cycles, and clearer evidence for stakeholder buy‑in.

Outcomes depend on your inputs (see “Data sufficiency” below), but teams typically cut weeks of research/coordination to days. Expect roadmaps with less 'busy work,' fewer strategy gaps and all this based on AIPath solving expensive data gaps around customer needs and competitor weak spots to underpin all your strategy options.

Start with assumptions or a brief we create together. AIPath has rapid diagnostics.

For example,

- if you describe high churn for one of your target customer niches, AIPath will suggest product and GTM strategies for that, and

- sequence new acquisition experiments later on the roadmap.

With AIPath, opportunities are always suggested, but you're always in control, and able to reject or re-order roadmaps as you see fit.

A digital twin is a safe sandbox of your product/market so you can test strategy before you spend.

AIPath models segments, features, channels, and competitive responses.

You can ask “What if we target X with Y feature and Z price?” and see likely outcomes, recommended next moves, and the collateral to execute.

You can even create twins of your competitors, and use AIPath to spot what features they may build next.

Everything you need to make and act on decisions.

• Prioritised roadmap (now/ next/ never) with rationale
  

• Segment strategy (ICP/Persona options, value props, JTBD maps)
  

• GTM plan + assets (landing pages, emails, ads, sales one‑pagers, pitch deck drafts)
  

• Experiment designs (what to test, success metrics, )
  

• Competitor & feature gap radar (with watchlists and alerts coming soon)

It complements them by deciding what to do and generating the first 80% of assets that have the context of your customer unmet needs and competitor gaps.

AIPath is a strategy simulation tool that also solves data gaps. It doesn't stop there; it helps you clarify the sequence of decisions you need to make, helps you rank the priorities and helps you test before you build and launch. It augments key teams by supporting product and GTM workflows. You can keep your delivery stack (Jira/ Linear), knowledge base (Notion/ Confluence), and campaign tools (HubSpot/ Marketo), because AIPath sits upstream to prescribe strategy and feed those tools with prioritised work and ready‑to‑polish content.

Run this 60‑minute Quickstart:

1. Create a workspace → choose a goal (e.g., “Find early‑adopter ICP for Feature X”).
   

2. Select and reject 10–15 industry/ niches/ roles you service from the long lists AIPath will generate for you.
   

3. Generate simulations → review top 3 segment/feature options.
   

4. Accept the “Now/ Next” plan → auto‑create the GTM kit for the chosen segment.
   

5. Launch one experiment (Landing page + email/ ad/ interviews) and validate, knowing that after you'll have clear next steps on what to build next

Day‑7 goal: 2–3 experiments completed, backlog re‑sequenced, and teams understanding where growth will come from and what they need to do.

New founder (solo or <5 team) – Week 1

• Load brief → pick ICP candidates → accept “Now/Next” → publish one LP test → review results → iterate.
  

Scale‑up PM/PMM – Week 1

• Import backlog + win/loss → re‑score with AIPath → publish segment‑specific GTM kits → run 2 experiments.
  

Innovation team – Week 1

• Model at least 3 venture scenarios → run parallel twins → graduate one bet with live‑data calibration and exec‑ready deck.

Agencies (Marketing, GTM, Research, Customer Discovery)

• Rapidly develop killer research on segmentation, customer needs and value propositions, customer weak spots in positioning and product capability → create deep strategy recommendations and insights even for free proposals → graduate one path with live‑data calibration from experiments and guarantee rapid kick-offs and better chances to win bigger projects.

We never sell customer data, never make it public, and do not use it to train AI models.

Your content stays inside your workspace, encrypted in transit and at rest, with controls aligned to SOC 2.

We run on certified cloud providers and platforms and follow a shared‑responsibility model for security.

We're designed to be enterprise-grade and work together with you if you have specific needs, just reach out.

AIPath works with whatever you have today—then enriches it. We combine your internal product, customer, and deal data with public competitor signals and AI‑generated “digital twins” to simulate markets, prescribe what to build, and validate it with experiments. Results loop back in to keep product and GTM always in sync. No heavy setup required. You can start with your existing assets like sales brochures and Jira backlogs. As you develop new data, the simulations calibrate and recommendations sharpen.

How we turn data into decisions (and back again)

1. Unify & enrich: We ingest your facts and files, then add public competitor and market signals to close gaps.
   

2. Simulate: For each target segment, AIPath builds a customer digital twin (needs, journey, willingness to pay) and competitor twins (features, claims, likely moves).
   

3. Score & compare: We run feature/market scenarios, scoring expected value and PMF likelihood per segment vs. competitor twins.
   

4. Prescribe & execute: You get a ranked plan (now/next/never) and ready‑to‑ship GTM assets to test the top bet.
   

5. Close the loop: Experiment results flow back to retrain the twin, auto‑refresh the roadmap, and update GTM—keeping product and marketing in lockstep.