App Gap Analysis (Deep Research)

prompt Deep Research

Send any planned app's detailed plan to a deep-research agent and get back a structured gap-analysis report — competitive landscape, architectural gaps, risks, and ranked recommended additions/removals, every non-obvious claim cited.

Works with

Kimi K2 (multi-agent)
Manus
ChatGPT Deep Research
Claude (web)
Gemini Deep Research

Version: 1.0
Updated: 2026-05-06
Runtime: 60–180 min
Est. cost: $20–80
Output: 8,000–15,000 words

Fill these placeholders first

Placeholder	What to put
`{{APP_NAME}}`	Working name (e.g., “Atlas”)
`{{APP_ONE_PARAGRAPH}}`	1-paragraph thesis: what the app is, who it's for, what it replaces
`{{APP_PRIMARY_PERSONA}}`	Wedge persona at V0 (role + seniority + industry + WTP signal)
`{{APP_KEY_FEATURES_BULLETS}}`	6–12 bullet list of headline features
`{{APP_DATA_MODEL_SUMMARY}}`	1-paragraph: entities + relations + retention notes
`{{APP_AGENT_OR_PIPELINE_LIST}}`	If AI-agent product: list agents + their contracts. If not: list workflows.
`{{APP_STACK_PROGRESSION}}`	V0 → V1 → V2 → V3 → V4 stack picks
`{{APP_PRICING}}`	Tier names + prices + limits
`{{APP_ARCHITECTURAL_RULE}}`	Hard architectural commitment (e.g., "vertical-agnostic"). Optional.
`{{APP_REFERENCE_PLANS_OR_COMPETITORS}}`	List of comparable products to benchmark against
`{{APP_SPECIFIC_INVESTIGATIONS}}`	Domain-specific things to dig into (HIPAA chain, CRDT, eval harness…). Free-form.
`{{TODAY_DATE}}`	Today's ISO date (e.g., 2026-05-06)

Fill the placeholders above, then copy the whole block below into your target agent. It runs under a disconfirmation methodology — the goal is to discover now that something is wrong, missing, or misjudged, not after committing $50K+ to build.

The prompt

You are a senior product architect + market analyst. Conduct a deep gap analysis
of a planned product called "{{APP_NAME}}". Your job:

1. Read the plan below.
2. Web-research the {{TODAY_DATE}} competitive landscape, technical best
   practices, compliance landscape, and AI/automation ecosystem relevant to this
   product category.
3. Identify GAPS in the plan — features, safeguards, observability, eval
   harnesses, business mechanics — that successful comparable products have but
   this plan does not.
4. Identify RISKS — assumptions that may fail under load, scale, regulation, or
   competition.
5. Recommend ADDITIONS (with rationale + estimated effort + phase placement).
6. Recommend REMOVALS (anything in plan that current evidence suggests is
   unnecessary or counterproductive).
7. Cite every non-obvious claim. Primary sources preferred (vendor docs,
   regulator publications, peer-reviewed research, official repos) over
   secondary (blogs, summaries).

Operate under DISCONFIRMATION methodology. Assume the founder wants to discover
NOW that something is wrong, missing, or misjudged — not after committing
$50K+ to build. Be a senior contrarian, not a cheerleader. Cite specific
section/article/guidance ID for any compliance reference. Cite specific
repo/version for any code/library reference.

— PRODUCT (full context) —

Name: {{APP_NAME}}

Thesis (one paragraph):
{{APP_ONE_PARAGRAPH}}

Primary persona (V0 wedge):
{{APP_PRIMARY_PERSONA}}

Key features:
{{APP_KEY_FEATURES_BULLETS}}

Data model:
{{APP_DATA_MODEL_SUMMARY}}

Agents / pipelines / workflows:
{{APP_AGENT_OR_PIPELINE_LIST}}

Stack progression:
{{APP_STACK_PROGRESSION}}

Pricing:
{{APP_PRICING}}

Architectural rule (NON-NEGOTIABLE):
{{APP_ARCHITECTURAL_RULE}}

Comparable products / reference plans to benchmark against:
{{APP_REFERENCE_PLANS_OR_COMPETITORS}}

Domain-specific investigations to perform:
{{APP_SPECIFIC_INVESTIGATIONS}}

— DELIVERABLES (single markdown report, ~10–15K words) —

§A. Competitive landscape ({{TODAY_DATE}} state). Map the space. For each
direct + adjacent competitor: target market, pricing, retention signals,
funding/revenue, gap vs the plan above. Pricing benchmarks. Retention benchmarks
(30-day, 90-day, annual gross retention).

§B. Architectural gap analysis. For each gap: numbered GAP-N. Severity (high/
medium/low). Affected phase. Description (2-3 sentences). Why it matters
(1-2 sentences). Recommended addition (1 paragraph). Estimated effort (S/M/L).
Evidence (citations). Specifically investigate (where applicable to product
category): eval harness for agent quality / prompt registry + versioning / cost
tracking primitive / public API for users / customer-co-debug tooling /
compliance evidence collection / pre-build POC for highest-risk thing / status
page + incident comms / rate limiting + abuse prevention / backup + point-in-
time restore / email deliverability / OCR for image drops / voice transcription
/ accessibility (WCAG 2.2 AA) / mobile PWA vs native / real-time CRDT
implementation / shared workspace presence / provider abstraction / multi-LLM
fallback / token cost optimization (prompt caching) / vector search /
distributed tracing / EU data residency / HIPAA BAA chain / GDPR data
export+delete / audit log integrity (append-only, hash-chained).

§C. UX gap analysis. Onboarding completion benchmarks. Empty-state design.
Mobile-first patterns. Accessibility beyond WCAG. Density (Notion-vs-Linear-vs-
ChatGPT). Motion language. Command palette. Keyboard shortcuts grammar.
Notification hygiene.

§D. Business model + pricing gap analysis. Are tier prices calibrated to current
WTP? Cite benchmarks. Credit-based vs all-you-can-eat? Free tier strategy
(leak vs leverage). Enterprise pricing floor. PLG vs sales-assist crossover.
Reseller / white-label / partner channel.

§E. Compliance + regulatory landscape ({{TODAY_DATE}}). EU AI Act enforcement
state. US state AI regulation (CA, CO, NYC bias-audit). HIPAA + AI BAA state
with major LLM providers. SOC 2 Type I/II realistic timeline + cost. ISO 27001/
42001 (AI management). GDPR + AI inference (right-to-explanation, data
minimization). AI-specific liability + insurance market state.

§F. AI coding-agent / autonomous-build infrastructure. Reverse-engineer leaders
(Anthropic Claude Code, Cognition Devin, Cursor Agent, Cline, Aider, Lovable,
Bolt.new, Vercel v0, Replit Agent, Emergent.sh, Lindy, AutoGen, LangGraph,
CrewAI, OpenAI Swarm, Mastra). What works? What fails? Mid-flight requirement
mutation handling? Eval-first methodology? Modular code-output guarantees?

§G. RECOMMENDED ADDITIONS (top 20, ranked by impact-per-effort). Each: name +
why + phase + effort (S/M/L) + evidence.

§H. RECOMMENDED REMOVALS. Anything that current evidence suggests is wrong,
redundant, or counterproductive.

§I. TOP 10 RISKS (pre-mortem). For each: probability, impact, mitigation, early
warning signal.

§J. OPEN QUESTIONS for human follow-up.

— SOURCES TO CONSULT (non-exhaustive — go further on your own) —

Vendor product/pricing/changelog pages for direct + adjacent competitors.
Official vendor docs (vercel.com/docs, supabase.com/docs, anthropic.com/docs,
openai.com/docs, langchain.com, langsmith.com, langfuse.com, braintrust.dev,
helicone.ai, opentelemetry.io). Regulator publications (EU AI Act portal,
oag.ca.gov, hipaa.gov, soc2.com, drata.com, vanta.com, iso.org). Analysis
blogs (a16z.com, benn.substack.com, every.to, latent.space, swyx.io). Honest
signals (Reddit r/Cursor, r/SaaS, r/LocalLLaMA; Hacker News top threads on the
category; X search for competitor names).

— OUTPUT FORMAT —

Single markdown document, ~10K-15K words. Tables + bullet lists where they
help; prose where prose is better. Numbered citations [1], [2], ... and a
final reference list. Date the report. If any specific claim cannot be
verified to ≥ 70% confidence, mark it `(unverified)` and flag in §J. If any
sources are paywalled or unavailable, list them in §J as "needs human follow-up."

— DON'T —

Don't repeat the plan back. Don't be diplomatic about gaps. Don't pad. Don't
fabricate. Don't recommend without citation.

— DO —

Be the senior architect who has shipped 5 SaaS companies and knows where this
plan will break. Steel-man strengths AND weaknesses. Push back on architectural
commitments if current evidence suggests they're wrong. Suggest things the
founder hasn't thought of. Tell me what's missing that everyone else has.

Begin research now.

deep-research
gap-analysis
product
architecture
pre-mortem