Zoa Research

What do they actually do

Zoa Research builds forecasting models that combine time-series and event data to predict short‑horizon outcomes. Today, they use these models internally to generate trading signals and run a revenue‑generating proprietary trading operation (“For now, our models trade”) zoaresearch.com, Y Combinator. They hire domain experts (e.g., pharma/biotech SMEs) to translate specialist intuition into labels and rules the models can learn from, and a small research team iterates and deploys models in production YC job posting, Team page.

They plan to productize these forecasts for external customers as a secure API and dashboard (Forecasting‑as‑a‑Service), while expanding into more domains via expert hires and research collaborations LinkedIn, Y Combinator, zoaresearch.com.

Who are their target customer(s)

• Quant/prop trading desks and hedge funds: They need reliable, event‑driven signals that can be integrated into automated strategies without long build cycles; internal quant teams often lack cross‑domain event coverage or fast iteration on new signals zoaresearch.com, Y Combinator.
• Pharma/biotech investors and analysts: Clinical and regulatory news is noisy and time‑sensitive; without deep SME coverage, it’s hard to quickly translate readouts into probabilistic outcomes. Zoa’s SME‑guided labeling aims to encode that expert intuition YC job posting, Team page.
• Supply‑chain and operations leaders at manufacturers/retailers: They face fragmented signals and sudden disruptions; existing tools often miss event‑driven shocks that matter for procurement and inventory decisions zoaresearch.com, Y Combinator.
• Energy and commodity trading teams: Markets are volatile and event‑heavy; teams need short‑horizon, event‑aware forecasts and quicker model iteration to respond to shocks Y Combinator, LinkedIn.
• Government agencies, NGOs, and corporate strategy groups: They need external, secure forecasting because they lack large in‑house research teams and must inform planning/policy with timely forecasts LinkedIn, zoaresearch.com.

How would they acquire their first 10, 50, and 100 customers

• First 10: Run paid, hands‑on pilots with trading desks/hedge funds via founder and YC networks, using the same signals Zoa already trades to prove edge; set weekly feedback loops and clear evaluation windows to convert to contracts Team page, Y Combinator.
• First 50: Expand into vertical 3‑month programs (pharma, energy, supply chain) with SME support to co‑develop use cases and provide references; publish anonymized case studies and add targeted outbound plus data/prime broker partnerships YC jobs, zoaresearch.com.
• First 100: Productize delivery (secure API + dashboard) with self‑serve pilots and enterprise tiers (SLAs, privacy/compliance) and scale via docs, platform integrations, channel partners, and ongoing research output LinkedIn, zoaresearch.com.

What is the rough total addressable market

Top-down context:

Upper‑bound, non‑deduplicated adjacent markets total about USD 70–75B: algorithmic/automated trading platforms (~$21.1B, 2024), SCM software (~$33.4B, 2024), and predictive analytics (~$18.9B, 2024) Grand View Research, Gartner, Grand View Research.

Bottom-up calculation:

A more conservative SAM focuses on the forecast/insights slices: trading‑signals demand (~$21.1B), demand‑planning solutions (~$5.3B), and insights‑as‑a‑service (~$7B), summing to roughly ~$33.4B Grand View Research, Grand View Research, Grand View Research.

Assumptions:

• We count only the forecasting/insights share of larger software markets, not full-stack budgets.
• Zoa targets both financial buyers (trading) and enterprise forecasting buyers (SCM, pharma, energy, public sector) Y Combinator, YC job posting.
• Market categories overlap (e.g., predictive analytics within SCM); the SAM removes some double counting.

Who are some of their notable competitors

• RavenPack: Provides structured event and sentiment data from news and other unstructured text to trading and corporate risk teams; overlaps where event extraction drives forecasts RavenPack, Supply‑chain use case.
• Kensho (S&P Global): Sells cross‑domain market analytics, curated datasets, and APIs used by financial firms to build event‑aware models; competes on data/analytics supply to institutional buyers Kensho, S&P acquisition.
• Numerai: ML‑driven hedge fund with a public ‘Signals’ platform; comparable to Zoa’s trading‑first model outputs being turned into strategies Numerai, Signals overview.
• Predata: Offers predictive event intelligence and early‑warning signals for geopolitical/market risk; overlaps on event‑driven forecasting for macro and risk teams Predata, CME data partner.
• o9 Solutions: Enterprise supply‑chain planning and forecasting SaaS used by manufacturers/retailers; competes with Zoa’s planned Forecasting‑as‑a‑Service in operations Coverage.