Why AI Dev Planner
Trust should come from evidence you can inspect, assumptions you can challenge, and experiments you can actually run.
Evidence you can inspect · Assumptions you can challenge · Experiments you can run
Sources
24
Each with date & excerpt
Key assumptions
8
Ranked by risk
Biggest risk
Willingness to pay at $49/month is unproven — evidence is mixed.
Recommended experiment
7-day paid-pilot test with 10 qualified prospects.
Evidence-first architecture
AI Dev Planner is built around a fixed pipeline: evidence is retrieved and structured before any analysis happens. A polished answer without its evidence never reaches you.
The pipeline we follow
Never
IdeaGiant promptPolished answer
Collects current evidence from relevant public sources.
Stores sources, claims, timestamps and provenance.
Analyses the structured evidence — not raw web text.
Generates scores, assumptions and experiments.
Produces MVP plans, architecture, budgets, timelines and GTM actions.
“The AI is not the source of truth. The evidence is.”
The short version
Not promises about the AI — properties of the method. Each one is something you can verify in the product itself.
Important claims are built from retrieved sources — not from a model’s memory. You can trace analysis back to what it rests on.
Sources carry a publisher, publication date, retrieval date and excerpt, so you can judge freshness and bias yourself.
When sources disagree, the disagreement is shown and confidence drops — it is never averaged into one convenient answer.
We never output a success probability. You get separate, explainable dimensions plus an evidence-confidence score.
Budget, team, skills, geography and timeline change the recommendation — because they change what is actually feasible.
Research ends in 7-day and 30-day experiments with explicit pass/fail thresholds, not an optimistic report.
Server-side provider calls, tenant isolation, access controls, encryption and data export/deletion controls.
Evidence ledger
Important claims keep a trail: source, publisher, dates, excerpt, confidence and what the claim supports — so you can verify the research instead of taking it on faith.
We show what we know — and what we don’t
How confidence is built
EvidenceAnalysisConfidence
What we don’t do
AIAnswer
A confident answer isn’t useful when the evidence is weak.
Evidence ledger
Claim
High confidenceCustomers in this segment frequently struggle with manual scheduling.
Evidence excerpt
“We spend hours every week copying shift requests between a spreadsheet and a group chat. One missed message and someone doesn’t show up.”
Supports
Customer pain hypothesis
Impact on decision
Contributes to the demand evidence assessment.
Example ledger for a demo project. In a real report every claim carries its own source, dates and confidence — and you can open the original source.
Contradiction check
If sources conflict, the system doesn’t silently average them into one convenient answer. Contradictions are surfaced, confidence is reduced, and the recommendation accounts for the disagreement.
Source A· Industry newsletter · Aug 2026
Reports increasing demand in Segment A.
Source B· Community survey · Jul 2026
Reports declining demand in another segment.
Typical AI answer
“Based on the market, this is a promising opportunity.”
Confident, smooth, and impossible to verify. The disagreement between sources has disappeared — along with the detail you needed.
AI Dev Planner
“Evidence is mixed. Demand appears stronger in Segment A than Segment B..”
Mixed evidence lowers confidence in the demand dimension, which flows into the score and makes segment choice an explicit experiment.
Segment-level differences are the insight. An averaged answer hides exactly the detail a founder needs to choose a beachhead.
Contradictory evidence lowers confidence in the affected dimensions, which flows into the score and the recommendation.
Each side of the disagreement keeps its own provenance, so you can decide which source you believe — and why.
Not just another AI opinion
A chatbot gives you prose. AI Dev Planner gives you a decision process: structured evidence, explicit confidence, your constraints, and a test you can run — all of which you can inspect and disagree with.
Sources
May not show any
Every important claim keeps source, publisher, dates and excerpt
Contradictions
Usually smoothed into one answer
Surfaced explicitly and reflected in confidence
Confidence
Sounds equally sure either way
Based on evidence quality — and allowed to drop
Your constraints
Generic unless you prompt carefully
Budget, team, geography and timeline are inputs to the analysis
Next step
An opinion in prose
An experiment with pass/fail thresholds and a Keep / Pivot / Drop decision
Checkability
You take the answer on faith
You can open the evidence trail and challenge it
The difference isn’t a smarter model. It’s evidence you can inspect, assumptions you can challenge, and experiments you can actually run.
Research is only the beginning
Research that never meets reality is speculation. AI Dev Planner turns uncertainty into measurable validation — with thresholds you agree before the test runs.
From research to decision
The validation workflow
7-day experiment
ExampleAssumption
“Small businesses will pay $49/month for this solution.”
Test
Interview 10 target customers + present a paid pilot offer.
Duration
7 days
Budget
$0 – $150
Thresholds — decided before the test runs
Pass:3+ qualified prospects agree to a paid pilot.
Fail:No meaningful purchase commitment after qualified conversations.
Example experiment for a demo project. Every report ends with 7-day and 30-day validation plans — with explicit pass/fail thresholds, so the result can change your Keep / Pivot / Drop decision.
Experiments are sized to your actual budget — the goal is the cheapest credible test, not the most impressive one.
No success probability
What it can do is help you understand the evidence, expose the risks, and identify what to test next.
Never“Your startup has an 82% chance of succeeding.”
These dimensions are designed to show what appears strong, what appears risky, and what needs to be tested — not to predict the future.
“We don’t predict the future. We help you make better decisions with the evidence available today.”
Founder constraints
Budget, geography, team and skills aren’t footnotes — they decide what is feasible. The same idea gets different recommendations for different founders, because the constraints are different.
Recommendation emphasises
Recommendation may allow
“There is no single correct startup plan. The right plan depends on your constraints.”
Red-team / quality layer
Before a recommendation reaches you, a quality pass attacks it: looking for unsupported claims, contradictions, invented competitors and reasoning that got too optimistic.
Quality pass
The system should be able to lower confidence. Reducing confidence when evidence is inadequate is a feature, not a failure — a report that stays certain on weak evidence is the product you should distrust.
Assertions with no source behind them are flagged or removed.
Disagreements between sources are surfaced, not smoothed over.
Named competitors must resolve to real, retrievable sources.
Budget lines are checked against published vendor pricing.
One-sided analysis is challenged before it reaches the report.
Thin or indirect evidence lowers the relevant confidence score.
Stale sources are marked so you know how fresh the picture is.
Security & privacy
Startup ideas are commercially sensitive. The architecture is designed so your research, sources and reports stay yours — with the same honesty applied to security claims as to research claims.
Protected API credentials
AI and search provider keys stay on the server and are never shipped to the browser.
Server-side AI processing
Provider calls run server-side, where access can be controlled and rate-limited.
Prompt-injection defenses
Retrieved web content is treated as untrusted data — never as instructions.
SSRF protection
URL-fetching systems validate destinations to prevent server-side request forgery.
Tenant isolation
Projects, reports and sources are scoped to your account from day one.
Row-level access controls
Database policies verify ownership before any row is read or written.
Encryption in transit and at rest
Managed platform encryption protects data in motion and at rest.
Data deletion & export
Export or delete your projects — startup ideas can be commercially sensitive.
Rate limiting & abuse prevention
Limits and abuse controls protect research jobs and shared endpoints.
Input & content sanitization
User and retrieved content is sanitized before rendering or storage.
Source provenance
Every generated claim keeps a provenance record linking back to its source.
Backups & versioned reports
The evidence database is backed up and reports keep version history.
“We build security into the architecture from the beginning.”
“We don’t claim enterprise compliance before we have earned it.”
We don’t claim SOC 2, ISO or GDPR certification, “military-grade” security, or zero data risk — because we haven’t earned those claims. What we can point to is the documented architecture above, and we’ll update this section as guarantees are independently verified.
Questions about how your data is handled? Contact us — we answer security questions directly.
Our hard limits
Trust is easier when the boundaries are written down. These aren’t settings we might change later — they’re the rules the product is built on.
“Our job isn’t to tell you what you want to hear. It’s to help you find out what you need to know before you build.”
FAQ
The questions founders ask about evidence, predictions and data — answered the same way the product answers them.
A separate research layer collects information from relevant public sources. Every source keeps a publisher, publication date, retrieval date and excerpt, so you can judge freshness and bias yourself rather than trusting a summary.
No. It never outputs a success probability. You get separate, explainable score dimensions plus an evidence-confidence score, so you can see what looks strong, what looks risky, and what still needs to be tested.
The disagreement is shown and confidence drops for the affected dimensions. Contradictions are never averaged into one convenient answer, and each side keeps its own provenance so you can check both sources and decide which you believe.
Yes. The evidence ledger links each claim back to its source with the publisher, dates, an excerpt and a confidence rating — so the analysis can always be traced back to what it rests on.
A chatbot goes from one large prompt straight to a polished answer. AI Dev Planner moves from research questions to retrieved evidence, structured facts, claims, analysis, scoring and experiments — with research kept separate from reasoning.
AI provider keys stay on the server, calls run server-side, projects and reports are scoped to your account, database policies verify ownership before any row is read, and data is encrypted in transit and at rest. You can export or delete your projects at any time.
No — and we don't claim certifications we haven't earned. What we can point to is the documented security architecture on this page, and we update it as guarantees are independently verified.
Research the opportunity. Find the biggest risk. Run the cheapest credible test. Then decide what to build.
No credit card required · Start with one idea