Construction Niche Research
The Challenge
Finding viable SaaS opportunities in the construction industry is like finding needles in a haystack. The industry is massive, fragmented, and filled with:
- • Overwhelming complexity - 97,249+ distinct workflows across 3,414+ market segments
- • Hidden pain points - Real user frustrations buried in Reddit threads, reviews, and forums
- • Misleading signals - "Solutions exist" doesn't mean users love them - most are tolerated, not celebrated
- • Manual research bottleneck - Traditional market research takes weeks per workflow and misses non-obvious opportunities
- • Analysis paralysis - Without systematic evaluation, it's impossible to prioritize which opportunities to pursue
The challenge was to build an AI-powered research system that could systematically evaluate tens of thousands of construction workflows, surface genuine SaaS opportunities, and provide evidence-based insights—all while thinking beyond the obvious.
The Solution
I built a multi-stage AI research pipeline that combines taxonomy generation, intelligent filtering, web research automation, and deep competitive analysis to systematically evaluate construction SaaS opportunities at scale:
Research Architecture
Taxonomy Generation
Built a comprehensive 6-level industry taxonomy using AI enrichment scripts that progressively drill down from subsectors → categories → trade types → market segments → workflows. Each level is enriched with context-aware AI prompts that understand construction industry nuances.
Intelligent Filtering
Pre-filters 97K+ workflows using a 7-factor evaluation framework that thinks beyond the obvious. Scores workflows 0-100 based on pain point severity, market gaps, workaround signals, competitive landscape, segment value, automation potential, and market timing. Uses structured JSON output with Zod validation.
Query Generation
Generates 14-15 optimized search queries per workflow targeting specific intelligence categories: Pain Points (4 queries from Reddit/forums), Competitive Analysis (3 queries), Workaround Detection (3 queries for Excel/manual processes), User Sentiment (2-3 queries), and Market Reality (2 queries on pricing/adoption).
Web Research Automation
Performs automated web searches using Perplexity AI's search API, executing all queries sequentially and collecting results with proper mapping. Saves raw search results as structured JSON with titles, URLs, and snippets for subsequent analysis.
Two-Step Analysis
Step 1: Market Intelligence Extraction - Analyzes search results to extract pain point matrices with evidence, competitive landscape, workaround ecosystems, customer segmentation, feature priorities, and market maturity. Step 2: Solution Generation - Produces 5 strategic frameworks with evidence-based recommendations and segment-specific targeting.
Deep Research Mode
For high-potential workflows (score ≥70), conducts comprehensive autonomous research using Perplexity's sonar-deep-research model. Generates 5,000-15,000 word reports covering pain points with evidence, competitive gaps, workaround validation, customer segments, feature priorities, pricing analysis, and construction-specific compliance context.
Key Innovation: Beyond the Obvious
Most AI research tools fail because they accept surface-level answers. This system is explicitly instructed to think out of the box and challenge assumptions:
- • Hidden Market Gaps - Looks beyond "solution exists" to find tools users hate but tolerate
- • Emerging Trends - Identifies fresh pain points from new regulations, technologies, market shifts
- • Underserved Niches - Finds segments ignored by mainstream tools (SMBs vs enterprise)
- • Adjacent Value - Recognizes small workflows that are part of larger painful processes
- • Counterintuitive Opportunities - Values "infrequent but extremely painful" over "frequent but minor"
- • Workaround Detection - Actively searches for Excel/manual processes as demand signals
Technical Implementation
Core Technologies
- • TypeScript - Type-safe pipeline orchestration
- • OpenAI GPT-4/4o - Taxonomy enrichment & analysis
- • Perplexity AI - Web search & deep research
- • Zod - Schema validation & type safety
- • Jest - Comprehensive testing suite
Pipeline Features
- • Modular Scripts - Independent steps for flexibility
- • Combined Workflow - End-to-end automation
- • Progress Tracking - Resume from interruptions
- • Structured Output - JSON + Markdown reports
- • Rate Limiting - API quota management
Results & Impact
Discovered High-Value Opportunities:
Severe compliance risk, multi-year retention, Excel/PDF chaos despite existing tools - top-tier vertical SaaS opportunity
Mandatory reporting, Excel hell, rising enforcement - one of strongest SaaS opportunities
Emerging regulations, high liability, fragmented tools, heavy Excel reporting - high willingness to pay
The system transforms market research from a weeks-long manual process into an automated, scalable operation. Each workflow receives a scored evaluation, detailed competitive analysis, and evidence-based recommendations—enabling rapid identification of genuine SaaS opportunities in a massive, complex industry.
Query Generation Strategy
Reddit complaints, forum discussions, "worst part about" searches
Existing solutions, reviews, feature gaps
Excel usage, manual processes, validation signals
Unfiltered community discussions
Pricing data, automation adoption trends
Key Learnings
- • Structured outputs with Zod validation are essential for reliable AI pipeline processing at scale
- • Multi-stage filtering (quick pre-filter → detailed analysis → deep research) is more efficient than analyzing everything deeply
- • Explicit "think beyond the obvious" instructions dramatically improve AI's ability to find non-obvious opportunities
- • Workaround detection (Excel/manual processes) is one of the strongest demand signals for SaaS opportunities
- • Evidence-based scoring with specific examples prevents AI from hallucinating opportunities
- • Modular architecture allows running individual steps independently for debugging and iteration
- • Progress tracking and resume capability are critical when processing 40K+ workflows over hours/days
- • Combining search APIs with analysis models (Perplexity + OpenAI) leverages strengths of each