Tracking what developers ship, investors fund, and enterprises adopt
From GitHub commits to SEC filings, scored by AI to separate real trends from noise.
AI Retail
AI retail is transforming business operations by enhancing efficiency and personalization.
4/6
Sources with signal
Solid
Signal strength
654
Evidence points
Solid
Signal strength
B2B
Market tracked
Signal breakdown by source
We track real-time data from developer platforms, startup ecosystems, academic research, and enterprise filings to identify trends backed by hard evidence.
Hacker News
Community awareness · Last 14 days
Upvotes from the tech community. More points = more attention.
Stories and discussion on Hacker News indicate tech community awareness. High HN activity often precedes mainstream adoption by 1-3 months.
-What Retail AI and Compute Infrastructure Looks Like in 2026
-Show HN: I Talked to 500GB of Retail Data with Zero Domain Knowledge
GitHub
Developer creation velocity · Last 30 days
Stars are bookmarks from developers. More stars = higher quality projects.
New repositories signal active developer investment. GitHub creation velocity is a 3-12 month leading indicator of market adoption.
-JX-76/ai-agent-retail-assistant: 基于 Dify 的零售运营智能助手，支持设备报修、库存查询、销售报表等场景
-aniket-work/SiteScanner-AI: Autonomous engine for retail site selection using ge…
-KelleyHuntPLLC/returnclaw: Voice-first AI agent for retail returns. One command.…
Y Combinator
Startup funding signal · Last 4 batches
YC runs batches twice a year. More batches = sustained investor interest over time.
YC-backed companies in this space represent the strongest conviction signal: venture capital betting on market potential.
-Corvera: Fully autonomous operations for CPG brands
-Candytrail: AI agents for in-person sales teams, starting with coaching
-Kirana AI: AI manager for physical stores
Stack Overflow
Developer adoption · Last 90 days
Stack Overflow questions indicate developers actively using the technology. A 3-6 month leading indicator of production adoption.
Academic Research
Research publication signal · Last 24 months
Citations count how often other researchers reference this work. More citations = higher influence.
Academic papers represent the earliest indicator. Researchers publishing findings that drive the next wave of applied innovation.
-Embracing the power of AI in retail platform operations: Considering the showroo…
-How AI enhances employee service innovation in retail: Social exchange theory pe…
-Framework for adoption of generative AI for information search of retail product…
SEC Corporate Filings
Enterprise adoption signal · Latest filings
Mentions in SEC 10-K/10-Q filings mean public companies are disclosing this technology as material to their business. This is the strongest enterprise validation signal.
All data is sourced from public APIs: Hacker News (Algolia), GitHub (Search API), Stack Overflow (Stack Exchange API), Y Combinator (public batch data), OpenAlex (academic database), and SEC EDGAR (regulatory filings). Numbers reflect verified matches. Each item is checked by AI to confirm it's genuinely about the topic, not just a keyword overlap.
What the data tells us
AI Retail is showing meaningful activity across 4 sources. The trend is real and building momentum. Not every source has picked it up yet, which often means the opportunity is still early enough to act on.
We predicted what the market wants
The trend data tells you what's happening. We ran a prediction survey to find out what the market actually demands.
36.7% chose "Somewhat likely" when asked: How likely is your company to adopt AI technologies in retail operations within the next 12 months?
Key finding · N=500 · ±2-6pp accuracy
Why this data matters
We don't guess. We count what's actually happening.
Most trend reports are opinions. Inqvey Trends is different . We track what developers are building, what investors are funding, what researchers are publishing, and what enterprises are reporting to regulators. When multiple independent sources point to the same trend, it's not hype. It's a real market shift.
Hacker News
Community awareness (1-3 month lead)
GitHub
Developer creation velocity (3-12 month lead)
Y Combinator
Startup funding conviction signal
Stack Overflow
Technology adoption (3-6 month lead)
OpenAlex
Academic research (earliest indicator)
SEC EDGAR
Enterprise disclosure (strongest validation)
Each source is scored on a 0-100 scale based on activity volume. When a topic shows up across multiple unrelated sources (developers on GitHub, startups at Y Combinator, filings at the SEC) that agreement between independent signals is what separates real trends from noise. We use AI to verify that each result is genuinely about the topic, not just a keyword match.
Data freshness
Sources are scanned daily. Hacker News and GitHub reflect the last 14–30 days. Y Combinator covers the last 4 batches. Academic papers span the last 24 months. SEC filings cover the most recent quarterly and annual reports.
Go from trend to market intelligence
Knowing what's trending is step one. Knowing what the market actually wants is what drives product decisions.
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