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Jan 2, 2026

Building a Market Intelligence Platform in 45 Minutes: From Claude Research

Sandeep Lamba

TL;DR: Used Claude's research mode to generate a 15-page IFE market analysis, then had Claude build a full Next.js website to visualize it—all in under an hour.

The Problem: Information Overload

Market research generates dense reports. My Claude research query on in-flight entertainment systems returned 15 pages of structured analysis covering oligopolies, financial metrics, competitive matrices, and regulatory frameworks. Valuable insights buried in walls of text.

Reading time: 2+ hours. Attention span: 15 minutes.

The Solution: Automate the Presentation Layer

Instead of manually building a dashboard, I meta-programmed the process:

Step 1: Generate Research (11m 26s)

javascript
Input: Market structure analysis prompt for IFE systems
Mode: Claude Web + Research
Output: Claude.pdf (15 pages, investor-grade dossier)

Key requirement: The prompt enforced strict sourcing rules—only primary filings (10-K, 20-F), regulatory documents, and verified tier-one research. Every metric traceable or tagged "single-source (low confidence)."

The prompt is like below. Please change the sector name to your sector name.

My sector name - In-flight entertainment (IFE) systems for commercial aviation

yaml
## ROLE
Market-structure analyst for long-term investors. Produce an investor-grade, qualitative-first dossier.
Facts must be traceable and cross-verified. If evidence is weak, say so.

## INPUTS
sector = YOUR_SECTOR_NAME

## OBJECTIVE
Understand 
- how the sector is structured
- who the true players are, and 
- how competition works.

Deliver a 1-page executive overview + a full structured analysis with compact tables.

## ENFORCEMENT RULES (NO GUESSING)
Source bounds: 
- use only A–D. 
- If none available → write “Insufficient evidence.”

A) Primary filings/IR (10-K/20-F/URD/S-1, audited IFRS/GAAP, investor days, transcripts).
B) Regulators/statistics (industry regulators, national stats, IMF/OECD, BIS, etc.).
C) Official price lists, methodologies, technical specs, product catalogs, rulebooks.
D) Tier-one journals/consultancies with disclosed primary data; major data vendors with methodology.

### Verification
Any figure must be checked against ≥2 independent A–C sources → else tag “single-source (low confidence).”

### Uncertainty
If unsure → write “Unknown.” Do not estimate.

### Conflicts
If sources differ >5% → show both, define scope/period, and explain likely cause.

### Recency
Prefer ≤24 months; older = “Legacy.”

### Citations
Bracketed footnotes [1], [2], mapping to a Source List (≤15 items).

## UNIT OF COMPETITION (MANDATORY) :
For the given sector, automatically identify:

1. Lanes = the 5 universal types of competition
(Only use those that exist in the sector.)
- Upstream Inputs / Extraction
- Core Production / Infrastructure / Operations
- Distribution / Access / Channels
- Data / IP / Standards / Certification
- Services / Wrap-around Support

2. Customer groups = the 5 universal buyer types
(Only use those that exist in the sector.)
- End consumers
- Enterprise/professional buyers
- Intermediaries/distributors
- Regulators/standards bodies
- Complementors/ecosystem partners

3. Define true peers per lane
- Do not compare players across unrelated lanes.

## OUTPUTS
A) Executive Overview (≤300 words)
- Sector structure, lanes, main players, differentiation, constraints, catalysts.

B) Player Atlas (qualitative)
- For every significant player in each lane:
- Legal/brand; ownership; geography in North America, Europe, Asia-Pacific
- Vertical scope (upstream → core → distribution → data/IP → services).
- Products/offerings.
- Customers served.
- Why customers choose them (price, quality, reliability, service, design, IP, network, availability, latency, brand, regulation, etc.).
- Pricing model.
- Market-design specifics (industry-dependent).
- Competitive stance (local monopoly, fragmented, oligopoly, global scale, niche).
- Moats: network effects, IP, scale, regulation, switching costs, ecosystem lock-in.
- Vulnerabilities: regulation, commoditization, disruption, outages, supply risk.

C) Peer Sheet — Normalized KPIs (per lane)
- Revenue/EBIT mix.
- Margin structure (EBITDA %, FCF conversion).
- ROCE/ROIC.
- Capex intensity.
- Pricing power indicators.
- Volume metrics relevant to the sector.
- Productivity/efficiency metrics.
- 10-year through-cycle resilience.

D) Moats & Switching Analysis
- Lane-by-lane qualitative assessment.

E) Customer-Choice Narratives
- For each customer group: why they choose one supplier over another.

F) Regime, Regulation, & Catalysts
- Regulatory constraints.
- Structural changes.
- M&A patterns (10-year).
- Technology shifts.
- Macro drivers and risks.

G) Watchlist (5–8 items)
Key KPIs, catalysts, and risks to monitor.

## COMPARATIVE MATRICES (MANDATORY)
1) Players × Customer-Choice Drivers
(price, quality, service, network, distribution, IP, regulation, reliability, etc.)

2) Players × Moats/Frictions
(network effects, IP, scale economics, regulatory entrenchment, switching costs)

3) Lane-based cluster map
Shows true peers vs adjacent players.

## FORMAT
Do the following:
- 1-page Executive Overview
- Full Player Atlas & Peer Sheet
- Two matrices + short narratives
- Appendices with
  - Assumptions
  - Methods, and
  - CSV-ready tables

## BEGIN
Do the following:
- Identify the lanes (from the universal categories).
- Map players into lanes; define true peers.
- Build Player Atlas.
- Populate Peer Sheet using evidence rules.
- Write customer-choice narratives + moat analysis.
- Add regime, catalysts, and watchlist.
- Deliver all sections.

Step 2: Generate Instructions (5 minutes)

Asked Claude to write deployment instructions for building a website from the PDF. The generated instructions.md specified:

  • Tech stack: Next.js 14 + TypeScript + Tailwind + shadcn/ui
  • Architecture: 6 page routes with tabbed interfaces
  • Component specs: DataTable with color coding, PlayerCard layouts, MatrixView visualizations
  • Design system: Professional BI aesthetic with semantic color palette (green=strong, yellow=moderate, red=weak)
  • Data modeling: TypeScript interfaces for players, metrics, and competitive matrices

Step 3: Build the Site (30-40 minutes)

Dropped instructions.md into a new Cursor/Claude session. The AI generated:

  • Responsive Next.js app with 6 main sections
  • Interactive data tables with sorting and color-coded cells
  • Tabbed player profiles (Panasonic, Thales, Viasat, Starlink, etc.)
  • Competitive heat maps showing moats and customer drivers
  • M&A timeline visualization
  • KPI watchlist dashboard

Zero manual coding. Just iterative refinements through prompts.

Cursor Project looks like this

Technical Highlights

Color-Coded Intelligence

Tables use semantic colors automatically:

  • (strong) → bg-green-100 text-green-800
  • (moderate) → bg-yellow-100 text-yellow-800
  • (weak) → bg-gray-100 text-gray-600

Data Fidelity

The site preserves research rigor:

  • "Not Disclosed" for missing data (never blank cells)
  • Asterisks for single-source estimates
  • Footnotes linking to verification sources
  • Currency timestamps ("As of Q4 2024")

Mobile-First Tables

Complex financial tables adapt responsively:

  • Desktop: Full matrix view
  • Tablet: Horizontal scroll with sticky columns
  • Mobile: Card-based layout

Website Page-1

Website Page-2

Why This Matters

Traditional workflow:

Workflow Comparison

PhaseTraditional WorkflowTimeAI-Assisted WorkflowTime
ResearchCommission research2 weeksClaude research12 minutes
AnalysisAnalyst reads/synthesizes8 hoursGenerate site specs5 minutes
DesignDesigner creates deck16 hours
DevelopmentDeveloper builds dashboard40+ hoursClaude builds site40 minutes
TOTAL~80 person-hours~1 hour
ROI~64x time multiplier

Results

The deployed site includes:

  • Executive overview with 6 key stat cards
  • Player atlas with detailed profiles for 8+ companies
  • Comparative matrices (Players × Moats, Players × Customer Drivers)
  • Interactive M&A timeline (2015-2025)
  • Regulatory framework documentation
  • 8-metric watchlist dashboard

Run locally:

bash

bash
npm install && npm run dev

Universal Applications

This approach works across multiple domains:

Business Intelligence:

  • Competitive intelligence analysis (this example)
  • Product comparison platforms
  • Financial analysis dashboards

Knowledge Management:

  • Technical documentation portals
  • Educational course materials
  • Internal wiki systems

Adapt to Any Industry

The research prompt is sector-agnostic. Simply modify the input:

javascript
sector = [Your Industry]

Examples:

  • Cloud infrastructure providers
  • SaaS security tools
  • Industrial automation
  • Pharmaceutical manufacturing
  • Renewable energy storage

The instruction template automatically adapts to your domain, generating appropriate data structures, visualizations, and competitive frameworks.

The future of technical work transcends code volume. It demands exceptional specification writing, intuitive information architecture, and the ability to distill complex data into clear visual narratives.

More on SDD(Spec Driven Design) in future blogs

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