Client-ready
market research
in hours, not weeks
Competitive landscapes, pricing intelligence, market sizing, and live competitor signals built on 400K+ real products and 10M+ mapped user intents. Data that LLMs aren't trained on, structured in ways they can't replicate.
The data collection part of market research is tedious. It doesn't have to be.
Every engagement starts the same way: pull together a competitive landscape, map out feature gaps, gather pricing data. Your team juggles Crunchbase, G2, Gartner, Reddit, and a dozen other sources. Days of tab-switching before the real analytical work even begins.
Days of data gathering per landscape
Manual competitive research eats into billable hours. The data collection phase alone can take a week before the real analysis begins.
Best data is scattered and gated
Useful user feedback, pricing details, and competitive signals are spread across dozens of platforms. Even with AI browsing tools, structuring and aggregating it is manual work.
Inconsistent quality
Different analysts, different approaches, different depths. Hard to maintain a consistent standard across multiple client engagements.
400K+
Products analyzed
10M+
Mapped user intents
Real
Pricing data
Live
Scraped at analysis time
From question to client deck in three steps
Describe the market
Tell us the sector, target companies, or research question. The system asks clarifying questions to narrow the scope before running the analysis.
We analyze the data
BuildSherpa matches competitors, aggregates real user feedback, clusters features, extracts pricing tiers, estimates market size, tracks discussion momentum, and captures live competitor signals. Takes about 10 minutes.
Deliver to your client
Review the interactive report online. Use the data, charts, and findings as a starting point for your client deliverable.
What makes this different from ChatGPT or manual research
Bottom-up insights, not top-down guesses
Most competitive analysis starts with company websites and press releases. Ours starts with what real users actually said: their frustrations, feature requests, and switching reasons. Aggregated across reviews and discussion threads, these patterns surface market dynamics that top-down research often misses.
What your client report includes:
Time spent on data collection per landscape:
| Manual research | 1-2 weeks |
| AI-assisted + manual verification | 2-3 days |
| BuildSherpa | ~15 minutes |
Spend your time on interpretation, not collection
The data gathering phase of competitive analysis is where most hours go. BuildSherpa handles that part: matching competitors, aggregating feedback, extracting pricing, clustering features, sizing the market, and scanning competitor blogs for live signals. Your team focuses on what actually requires human judgment: strategic interpretation and client-specific framing.
Think of it as a first draft built on real data, not a finished deliverable. Your expertise makes it valuable.
A structured dataset you won't find elsewhere
We've indexed 400K+ real product launches with their user feedback, pricing tiers, and feature sets. This data is scattered across review platforms, discussion threads, and comment sections. We've structured and aggregated it into a queryable database.
General-purpose AI tools can browse individual pages, but they can't aggregate patterns across thousands of reviews or extract structured pricing tiers at scale. That's what our pipeline does.
Behind-paywall data
Reviews, ratings, and feedback from paid platformsDiscussion threads
Extracted from HN, Reddit, and forumsPricing tiers
Structured extraction from competitor sitesFeature matrices
Auto-clustered from product pagesMarket sizing
TAM/SAM/SOM estimatesLive signals
Launches, funding, partnershipsHow BuildSherpa compares
| Manual research | ChatGPT / Perplexity | BuildSherpa | |
|---|---|---|---|
| Aggregated user feedback | Manual (one review at a time) | Can browse, can't aggregate at scale | Pre-indexed across 10M+ user intents |
| Pricing intelligence | Manual screenshots | Can browse, needs verification | Structured tier extraction |
| Feature gap analysis | Spreadsheet by hand | Unstructured feature lists | Auto-clustered matrix |
| Market sizing | Manual modeling | Generic estimates | TAM/SAM/SOM with confidence levels |
| Live competitor signals | Google Alerts | Can browse, one page at a time | Auto-extracted from blogs & news |
| Data collection time | 1-2 weeks | 1-3 days + verification | ~15 minutes |
| Repeatable methodology | Varies by analyst | Varies by prompt | Consistent every time |
From analysis to client deliverable
Every analysis is designed to be shared. Export it, brand it, and track what your client does with it.
Branded PPTX export
Export to PowerPoint with your firm's colors and logo. Presentation-ready slides your client sees as yours, not ours.
Shareable client page
Password-protected report page under your firm's domain. Built-in analytics show when clients open it and what they view.
Monthly auto-update
Competitor data refreshes monthly. Your analysis stays current without re-running it. Great for retainer clients who expect ongoing insight.
Version & market history
Every update is versioned. Show your client how the market evolved over time. Useful for quarterly reviews and strategy updates.
Common questions
Our database spans 400K+ technology products across B2B and B2C categories. We're strongest in SaaS, fintech, healthtech, edtech, e-commerce tools, developer tools, and productivity software. If it launched on Product Hunt, appeared on Hacker News, or has reviews on G2/Capterra, we likely have data on it.
Yes. The PPTX export is designed to be presentation-ready. You can customize the output and add your firm's branding. The interactive report itself is for your internal use. Your clients see a polished deliverable, not the tool.
Those platforms excel at funding data, company financials, and deal tracking. We're complementary: we focus on the product-market layer. What are real users saying? What features are competitors actually shipping? Where are the gaps in the market? Think of us as the voice-of-customer intelligence that CB Insights doesn't provide.
Our core database of product launches and reviews grows continuously. When you run an analysis, we scrape competitor websites in real-time for the latest feature and pricing data, and scan their blogs and news pages for live signals like recent launches, funding rounds, and partnerships. The review data reflects historical patterns (which is the point for aggregation), while pricing, feature, and signal data is current at analysis time.
See what the output looks like before you commit
Your first analysis is free. Run it on a market you know well so you can judge the quality yourself.
Run your first analysis