Startups & SaaS

Barie researches financial institutions by size, API adoption signals, recent security incidents, technology stack, and regulatory exposure from live data sources including FFIEC directories, SEC EDGAR filings, Apollo.io company intelligence, and Explorium transaction signals. It delivers 50 qualified enterprise prospects with company context, buying signal notes, and a decision-maker contact for each. Exported directly to HubSpot. Research to pipeline in one prompt.

Barie researches market entry strategies, regulatory requirements, competitive pricing benchmarks, distribution channel effectiveness, and ICP segmentation from live European sources — EUR-Lex, EDPB, G2, Crunchbase, Apollo.io, and Ahrefs. It delivers a four-pillar launch plan grounded in current data, not a consulting template filled with placeholder advice. Then it routes every component to the tool your GTM team actually uses to execute.

Barie identifies the highest-impact edtech data points from live, credible sources — market size, growth rate, investment trends, adoption metrics, regulatory tailwinds. Each data point includes the source, the publication date, and the context that makes it relevant to an investor conversation. Then Barie auto-generates a presentation-ready slide deck with the data visualised. Research to boardroom in one prompt.

Five parallel research threads. Features extracted from live product pages. Pricing retrieved from published plans at query time. Positioning decoded from marketing copy and SEO strategy via Ahrefs. Funding and investor data from Crunchbase. User-reported weaknesses pulled from G2, Trustpilot, and Reddit via Firecrawl and LunarCrush. Every data point sourced. Every claim traceable to the page it came from.

Barie researches industry reports, analyst estimates, regulatory drivers, and competitive landscape from live sources. It delivers a sourced TAM, SAM, and SOM with full methodology notes — a market sizing you can present to investors and defend in a board meeting, not a number hallucinated from training data at an unknown historical point.