Feynn

Feynn's core bet is that the bottleneck in AI research is not answer generation but answer defensibility. That shows up in the product architecture. The workflow is fixed and five steps long: onboarding verifies and disambiguates the entity, Industry Lens produces a source-backed briefing, Signals monitors what shifts week to week, Game Theory stress-tests a move against likely competitor responses with the payoffs and equilibria shown, and Strategy Studio packages a board-ready recommendation with rationale, risks and KPIs. You are not dropped into a blank prompt box; you are walked. The trust layer is the part competitors most often fake and Feynn most clearly specifies. Non-trivial facts link to publisher, date and snippet. Each fact carries a confidence level, and unsupported claims are flagged rather than laundered. Research runs on demand against current sources, with recency surfaced per fact. Source diversity spans filings, regulatory pages, company sites, reputable news, and industry analyst content. Just as important, the vendor publishes what it does not do: it does not license broker research, expert call transcripts, or proprietary financial datasets, and it says to pair it with a primary-research vendor when a decision turns on those inputs. That is a rarer and more useful disclosure than a feature list. The use-case framing is specific rather than generic: strategy teams defending a plan in the boardroom, innovation and product teams mapping emerging tech and white space across time horizons, M&A and corp dev teams briefing on targets without a six-week wait, and procurement teams watching supplier posture for changes. Decision Engine adds a distinctive mechanism — five specialist agents debate the call before a recommendation is synthesised, which gives you something to interrogate rather than a single opaque answer. The July 2026 expansion is the real plot twist. Pulpie ships pareto-optimal models for cleaning web data, and FeyNoBg does background removal with a training library attached. Neither is strategy software. Both point at data and image teams who need ready-to-analyze inputs, and they widen the buying committee beyond the strategist who originally signed up. The honest weaknesses: this is a structured platform, and teams that want a fast conversational answer will find the five steps friction. Its evidence base is public sources, so anything turning on live ticks or non-public data is out of scope by design — Feynn says so itself. And it is in early access, which means the surface will keep moving under you. Where it fits: consultants, investment analysts, corporate strategists, and diligence teams where an unsourced claim costs more than a slow one. Where it doesn't: casual question-answering, latency-sensitive decisions, and research that depends on internal or licensed datasets you would have to supply yourself.