attimet

What do they actually do

attimet is a very early‑stage research lab that builds prediction systems for financial markets. The team develops temporal/adaptive models that ingest market data, train, and get tested in live trading experiments, starting with options markets (site; YC job listing; CO/AI coverage).

There is no public SaaS product or API today; users are internal researchers/engineers who run and monitor experiments and low‑risk live execution. Public listings describe a ~3‑person team focused on rapid research‑to‑live iteration rather than shipping a customer product (YC company page; Work at a Startup; PitchBook profile).

Who are their target customer(s)

• Small prop / in‑house quant teams: They need short‑horizon predictions and low‑latency pipelines to push models live but lack bandwidth to build safe live‑testing infrastructure end‑to‑end (YC profile; PitchBook).
• Options market‑making desks: They need adaptive signals to price options and manage inventory through regime shifts; deploying new models safely in production is risky (CO/AI coverage).
• Small hedge funds / quant boutiques without large ML teams: They want predictive edges but face long development cycles and high hiring costs to build a research→execution workflow (PitchBook; YC job listing).
• Execution/ops leads at trading firms: They need to integrate new models without slippage or operational incidents and lack tooling to A/B test and monitor model performance in production (YC profile; PitchBook).
• Other quant startups or research teams buying signals/APIs: They want plug‑and‑play predictive primitives to avoid building core infra, but struggle to trust and validate third‑party models before integration or payment (PitchBook).

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

• First 10: Recruit pilot partners from known small prop teams and options market‑making desks via YC/network intros; run low‑risk managed pilots where attimet handles execution/ops to prove live value quickly (YC profile; CO/AI).
• First 50: Convert successful pilots into short paid engagements and use case studies + referrals to target similar small hedge funds and in‑house quant teams; standardize a one‑page pilot, sandbox feed, and clear success metric to reduce engineering lift (PitchBook).
• First 100: Integrate with execution brokers/venues and market‑data vendors to ease plug‑in; offer a self‑serve signals/API tier alongside a managed service, with clear docs and pricing (subscription, performance fee, or hybrid) to lower sales friction (PitchBook).

What is the rough total addressable market

Top-down context:

Published estimates place the AI/algorithmic trading platform market around $9–12B in 2024, with significant growth expected (MarketResearchFuture; Precedence Research). Options/derivatives trade trillions in notional daily and hedge funds manage ~${4.5}T AUM, creating real budgets when tools improve returns or reduce risk (FIA/Cboe, Cboe stats; Reuters/HFR).

Bottom-up calculation:

If attimet productizes for a narrow initial segment—say 200–400 small prop desks, options market‑makers, and quant boutiques—at $100k–$500k per year, the near‑term SAM is roughly $20M–$200M.

Assumptions:

• Focus on options‑centric teams actively trading but lacking low‑latency experimentation infrastructure (200–400 globally).
• Average annual contract value of $100k–$500k for high‑value predictive infra/signals.
• Estimates exclude potential performance‑fee upside; software/signals only.

Who are some of their notable competitors

• QuantConnect: Cloud platform for quant research, backtesting, and live trading; overlaps on the research‑to‑production workflow (docs; live trading).
• QuantRocket: Python‑first research and execution stack deployable locally or in the cloud for backtests, ML experiments, and live trading (homepage; docs).
• Numerai: Hedge fund that sources external predictive models and sells/uses aggregated signals—an alternative to in‑house model development (site; signals docs).
• Kavout: Vendor of ML‑based asset scores and live signals (e.g., K Score), for teams seeking ready‑made predictive primitives (product; homepage).
• AlgoTrader: Institutional algorithmic trading platform for running, monitoring, and scaling live strategies with risk controls and deployment tooling (company; summary).