The Hidden Cost of Public-Market Research
Every investment opportunity hides behind a mountain of data. For example, to build an understanding on a mid-cap stock, analysts slog through over 10,000 pages of filings, transcripts, industry reports, and expert-call notes often taking 30-40 days. Once a position is taken, the “scared cow” effect sets in: teams spend 2–3 hours a day monitoring incremental news. This is time that could otherwise be spent exploring new ideas or stress-testing portfolios .
Despite investment teams pouring over hundreds of analyst-hours per quarter into manual workflows, coverage of mid-cap companies continues to shrink. Wall Street shrinks research budgets; buy-side shops lack deep pockets for bespoke data teams. The result? An information bottleneck that limits both the depth and breadth of analysis and ultimately, the alpha investors can generate.
Rethinking “Alpha”: Depth, Speed, and Scale
What if analysts could scan hundreds of stocks for key metrics like P/E ratios, regional exposures, margin trends and get structured answers in minutes, not weeks? Imagine seamlessly combining quantitative outputs (Y/Y growth, cohort analyses) with qualitative color (management commentary, thematic narratives). That’s the capability that today’s data-heavy and model-light tools simply don’t deliver:
- Alternative-data platforms (a market set to hit $140 billion by 2030) often stop at raw signals: “here’s a spike in Google searches,” but not “what does that mean for retail margins?” .
- Generic LLMs (even GPT-4 Turbo) stumble on finance-specific reasoning, failing 81% of benchmark questions; long-context variants still miss 20–25% of the mark .
Investors need a truly integrated research partner; one that marries institutional-grade data, domain-tuned AI, and workflow-native interfaces.
Enter Matterfact: Your AI Research Analyst
At Matterfact, Ashutosh, Vishal, and Daniel have stitched together an AI research “team” that feels less like a toolkit and more like a seasoned analyst sitting beside you. Under the hood, their domain-specific LLMs have been painstakingly trained and fine-tuned on decades of SEC filings, earnings-call transcripts, and the messy reality of analyst workflows, so much so that they consistently hit a 93% accuracy score on the FinanceBench benchmark.
But raw power isn’t enough. By building a workflow-first product suite, they’ve turned that power into practical speed and insight: a chat interface that thinks in tables, KPIs, and market narratives rather than generic prose; a Bulk Analyzer that can slice through hundreds of tickers: “show me mid-cap industrials growing revenue north of 15% with EV/EBIT under 10x” in under five minutes; and a forthcoming Model Bench that marries what management is saying with what the spreadsheets are telling you, spitting out draft forecasts that you can tweak rather than build from scratch. Early users report 50–70% reduction in time spent preparing sector overviews and deep-dive briefs, freeing analysts to focus on strategy rather than data wrangling.
Behind the scenes, an institutional-grade data backbone keeps everything honest. SEC filings flow in real time, earnings calls are parsed automatically, press releases are crawled, and niche news sources are added on the fly; so every answer is rooted in the freshest, most reliable information. That means when a sudden shift in consumer demand or a surprising regulatory filing hits the tape, Matterfact’s AI instinctively ties it back to your portfolio, surfaces the outliers, and flags the questions you didn’t even know you needed to ask. It’s this seamless fusion of deep finance expertise, cutting-edge AI, and granular data that turns what was once a weeks-long slog into an interactive, living research process into one where you spend less time hunting for facts and more time crafting the questions that lead to true alpha.
Timing Is Everything: Riding Two Tailwinds
The convergence of two massive, high-growth markets makes Matterfact’s timing impeccable:
- AI in Asset Management: A projected $48 billion market by 2031, growing at 34% CAGR, as firms embrace AI to augment human decision-making .
- Alternative-Data Explosion: Expected to balloon to $137 billion by 2030 at a 53% CAGR, driven by the relentless hunt for unique alpha sources .
Yet incumbent platforms like Bloomberg and FactSet remain fundamentally data-centric. Matterfact flips the script: intelligence-first, with seamless data fusion and AI-native workflows.
A Vision Beyond Research
Matterfact’s vision extends well beyond simply speeding up analyst workflows. In the coming years, they’re on track to transform every step of the investment process: first by replacing the painstaking manual legwork with a seamless AI “research analyst” that any firm – large or small can plug into and use; then by becoming the go-to authority for in-depth, sell-side–quality research on the thousands of mid-cap and under-covered names that today fall through the cracks; and ultimately by leveraging that same AI engine to power a next-generation prime-brokerage business, where trade execution, capital financing, and settlement are all informed by real-time, data-driven insights. It’s a three-stage journey from democratizing research, to owning the narrative around every sector, to redefining how markets actually move.
At z21 Ventures, we invest in founders who’ve been in the trenches and built for the battle and Matterfact’s team has lived the analyst’s grind at Nomura, WorldQuant, Google, and Apple. We saw their domain-tuned AI outperform all other models on industry benchmarks, and we watched top-tier buy-side firms lean in as early design partners, cutting research time by more than half and uncovering new ideas faster than ever before. With tens of billions of dollars spent annually on data, research, and execution and with AI tooling in asset management poised to hit $48 billion by 2031—this is exactly the kind of problem and opportunity that z21 was built to back. We believe Matterfact won’t just accelerate workflows; it will rewrite the playbook for how investment intelligence is created, shared, and acted upon.