business

Verdict

Submitted 5/22/2026, 10:52:28 AM · Completed 5/22/2026, 11:05:51 AM

5.5
pivot
The idea

Most founders debug symptoms, not problems. Here's how to tell the difference.

Show original source text →
You fix the thing that's broken. It doesn't work. So you fix it again, differently. Still doesn't work. You try a third approach. Same result. You're not doing it wrong. You're looking in the wrong place. Most diagnosis stops at the symptom. You see where the problem IS and you work there. But the information that would actually tell you what's wrong almost always lives somewhere else: in the places where the problem ISN'T. There's a diagnostic technique called Is/Is Not Analysis that flips this around. Instead of only mapping where a problem exists, you map where it doesn't. Then you look at the contrast. You run it across five dimensions: - **WHERE** does this happen? And where doesn't it? - **WHEN** does it occur? And when doesn't it? - **WHO** experiences it? And who doesn't? - **WHAT** exactly is the problem? And what isn't it? - **HOW MUCH / HOW OFTEN?** And where does it stop? The rule: "everywhere" and "always" aren't answers. Find the edges. Here's what it looks like in practice. **Scenario: a habit-tracking app. 30-day retention dropped from 44% to 11% right after adding a premium upgrade prompt. The obvious diagnosis: the paywall is too aggressive. The obvious fix: lower the price.** Is/Is Not says: not yet. **Problem being diagnosed: users are churning after seeing the upgrade prompt.** | | Users who churned | Users who stayed | |---|---|---| | WHERE | iOS users only | Android users: retention completely unchanged | | WHEN | Day 7 to 9 specifically | Users who pass day 14 stay for months | | WHO | Users who tapped "No thanks" on the upgrade prompt | Users who upgraded or dismissed without declining | | WHAT | Notifications turned off the same day they declined | Notifications stayed on regardless of upgrade decision | | HOW OFTEN | Missed 2 to 3 days in a row, then never returned | Skipped one day, came back the next | **What the contrast reveals:** on iOS, tapping "No thanks" on a premium prompt triggers the system's "reduce notifications" suggestion. Most users don't notice it. They decline the upgrade, notifications quietly turn off, they miss their habit reminders, break their streak, and lose their reason to open the app. Android has no such behavior. The price isn't the problem. The churn trigger isn't even the paywall. It's a notification side-effect of how Apple handles the "no thanks" interaction. The fix is one UX change, not a pricing strategy. Same symptom. Completely different root cause. Completely different solution. The reason this works: it stops you from confusing where a problem appears with where it comes from. That difference is the signal. Everything else is noise. Once you've run the Is/Is Not, you have the boundaries of the problem. Then you can apply root cause analysis (Five Whys, Fishbone, whatever fits) to find what's actually driving those boundaries. Most people jump straight from "I observed a symptom" to "here's my solution." Is/Is Not is the step that fills the gap. This is a real case from one of DeliberAI's beta testers, a tool I've spent the last 4 months building. Five sessions, each built for a different stage of your project: brainstorming your idea, building your product vision and context file, framing a problem when execution hits a wall, then solving it. Each one runs on methodologies used by top-tier consulting firms. Is/Is Not Analysis is what the Problem Framing session is built around. The session walks you through it, then root cause analysis, and won't move to solutions until you have a definition that's actually precise. You walk away knowing what you're fixing, not just what hurts. Launching May 25. Waitlist at http://deliber.ai/ (Founder here) Next time something isn't responding to fixes, try this before touching the solution: Map where the problem ISN'T. Ask what's different there. The answer is usually in that gap.
TRIZ inventive level: 3/5· Principles: segmentation, preliminary action
Synthesis verdict
**Pivot**. The idea of creating a diagnostic tool, DeliberAI, based on the Is/Is Not Analysis methodology has potential, but it faces significant market and risk challenges. The concept is well-structured and has a clear, actionable value for businesses. However, the target audience is ill-defined, and the competitive landscape is brutal. The pricing and business model are also unclear, which raises concerns about the tool's viability. The strongest path forward might be to monetize the content or sell to enterprises who already spend on problem-solving training.

Strengths

  • - The Is/Is Not Analysis methodology is genuinely valuable and well-articulated.
  • - The tool has a clear, actionable value for businesses struggling with misdiagnosed problems.
  • - The case study demonstrates tangible ROI, strengthening the pitch.

Weaknesses

  • - The target audience is ill-defined, and the competitive landscape is brutal.
  • - The pricing and business model are unclear, which raises concerns about the tool's viability.
  • - The tool faces significant risks, including regulation, platform risk, and churn due to no-budget customers.

Best angle

The tool should focus on monetizing its content or selling to enterprises who already spend on problem-solving training, rather than trying to sell a SaaS tool to a broad, potentially budget-constrained audience.

Panel verdicts

Viability

meta/llama-4-maverick-17b-128e-instruct

8.0

“The tool's effectiveness relies on successfully implementing structured problem-solving methodologies in an accessible and user-friendly format.”

The idea is to build a tool, DeliberAI, that guides users through a Problem Framing session using Is/Is Not Analysis and root cause analysis methodologies. The concept is well-structured and based on real case studies. The technical complexity seems manageable as it's likely a web-based application with guided sessions. The development timeframe of 4 months is reasonable for a solo or 2-person team to build a functional v1, considering it's likely built on existing frameworks and methodologies. However, the actual development time may vary depending on the scope and features included in v1. The key challenge lies in creating an intuitive user interface that effectively guides users through the analysis process and integrates the methodologies seamlessly. The fact that the founder has already spent 4 months building the tool suggests significant progress has been made.

Risk

nvidia/llama-3.3-nemotron-super-49b-v1

3.0

“Innovative methodology at risk due to external dependencies and potential user affordability issues.”

The idea's strength lies in its innovative diagnostic approach, Is/Is Not Analysis, which could revolutionize problem-solving in various industries. However, several critical factors threaten its viability within 6-12 months. Firstly, **regulation** poses a significant risk, particularly in data privacy and protection laws (e.g., GDPR, CCPA), as the tool may handle sensitive user data from various projects, potentially leading to non-compliance if not adequately addressed. Secondly, **platform risk** is substantial due to dependence on third-party services (e.g., for payment processing, cloud hosting) which could experience outages, policy changes, or security breaches, disrupting DeliberAI's operations. Lastly, **churn** due to **no-budget customers** is a concern; the waitlist and May 25 launch suggest initial interest, but if the primary user base (likely startups or small businesses) cannot afford the tool post-beta, retention will plummet. The beta tester success story, while promising, may not scale broadly across diverse, potentially budget-constrained users.

Competition

no model

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Monetization

mistralai/mistral-medium-3.5-128b

8.0

“The revenue model's strength lies in selling a repeatable, high-margin diagnostic process, but lacks pricing/channel clarity to evaluate full unit economics.”

The idea leverages a proven diagnostic framework (Is/Is Not Analysis) with clear, actionable value for businesses struggling with misdiagnosed problems. The monetization path is concrete: a SaaS tool (DeliberAI) with a session-based model, targeting project stages (brainstorming, problem framing, etc.). Pricing isn't specified, but the waitlist suggests a pre-launch validation strategy. Unit economics hinge on conversion from waitlist to paid users and session adoption. The key differentiator is the structured methodology, which reduces trial-and-error costs for clients. Margins should be high (typical SaaS), but cost-to-serve depends on session scalability (e.g., automated vs. human-guided). The case study (habit-tracking app) demonstrates tangible ROI, strengthening the pitch. Weakness: No pricing or channel details (e.g., self-serve vs. sales-led) to assess scalability.

Market

moonshotai/kimi-k2.6(fallback #1)

3.0

“The free technique will spread; the paid product has no demonstrated audience with budget or reason to choose it over free alternatives.”

The core methodology (Is/Is Not Analysis) is genuinely valuable and well-articulated, but the business venture itself faces severe market positioning problems. The founder has built a broad 'five sessions' consulting-toolkit product (DeliberAI) when the viral content that actually resonates is the single free technique explained in the post. The target audience is ill-defined: aspiring founders who can't afford real consultants? Small teams without analysts? These groups have limited budgets and abundant free alternatives (Miro templates, Notion guides, YouTube explainers of McKinsey frameworks). The competitive landscape is brutal - framework libraries, AI coaches, and actual consultants all occupy this space. Most critically, the pricing and business model are completely absent from the pitch, and 'waitlist with no price' is a red flag for unvalidated willingness to pay. The May 25 launch deadline suggests building before proving demand. The strongest path would be monetizing the content (course, certification, corporate training) rather than the tool, or selling to enterprises who already spend on problem-solving training. As presented, this is a content marketing success chasing a product-market fit that likely doesn't exist at scale.

Synthesized by meta/llama-3.3-70b-instruct · 27.2s