Reific GTM brain

Reific licenses reinforcement learning environments built from real time-series records to frontier labs training and evaluating AI agents.

category

AI agent training and evaluation environments

market profile

  • text: Static evaluations can miss decisions made while data is still arriving and timing affects reward.
  • text: Reific offers environment families spanning personal agents, industrial systems and science or compute operations.
  • text: A growing set of providers sells RL environments and verifiers to model labs, with differing domains and data sources.

personas

  • title: Frontier lab reinforcement learning research lead
    pains:
    • Models can saturate static or easy tasks without learning long-horizon decisions
    • Training needs difficult tasks and reliable reward signals
  • title: Agent evaluation scientist
    pains:
    • Needs repeatable comparisons with ground-truth outcomes and decision cutoffs
    • Real operational data has missing, delayed and conflicting signals
  • title: Industrial or health data partnership lead
    pains:
    • Longitudinal records have training value but contain sensitive operational or personal information
    • Data licensing requires confidence in de-identification and appropriate use

icp

  • text: Frontier AI labs training agent models with reinforcement learning and maintaining evaluation programs.
  • text: Research teams developing agents that reason about longitudinal personal, industrial, scientific or compute data.
  • text: Potential data suppliers include organizations holding real operational or health time-series records for licensing.

message pillars

  • text: Evaluate time-aware decisions against real outcomes.
  • text: Use environments for training as well as evaluation.
  • text: Reflect the messiness of real operating records.
  • text: Layer privacy protection and adversarial review before release.

objections

  • objection: How is this different from a static benchmark?
    response: The environments release records on a clock and check outcomes against what happened, making timing part of the task.
  • objection: Can sensitive records be used safely?
    response: Reific describes layered protection and adversarial testing; evaluate data rights and controls for each dataset.
  • objection: Can we reproduce the results or train on them?
    response: Request samples and examine the graders and licensing terms on a representative environment.

competitors

  • name: Mercor
    website: https://www.mercor.com/research/
    why: Builds expert-backed RL environments and evaluation datasets for frontier labs.
  • name: Prime Intellect
    website: https://www.primeintellect.ai/
    why: Offers an environment hub and infrastructure for agent reinforcement learning.
  • name: Scale AI
    why: Provides custom RL environments and evaluations to model builders.

influencers

  • name: Antanas Žilinskas
    url: https://www.ycombinator.com/founders/antanas-zilinskas
    why: Founder and CEO with published rare-event forecasting research.

keywords

  • reinforcement learning environments for agents
  • time series agent benchmark
  • long horizon AI evaluation
  • frontier model training environments
  • industrial time series reasoning
  • agent reward verifiers

voice

  • Research-led
  • Precise and evidence-oriented
  • Ambitious

signals

  • text: A frontier lab is expanding RL training or evaluating agents on time-dependent tasks.
  • text: An operator holds longitudinal sensor or health records and is exploring data licensing.

stack

  • Harbor

hiring

  • Research
  • Expert

pricing

model: unknown

motion

sales_led

channels

  • research publications
  • leaderboard

confidence

medium

Founders

  • name: Antanas Žilinskas
    title: Founder/CEO
    role: CEO
    bio: I’m the founder and CEO of Reific (YC F26): RL environments for superhuman reasoning over time. I’m first author of two ICLR 2026 papers on rare-event forecasting (SOTA results). Before Reific, I co-founded Entangl (YC S24) as CTO, taking AI for mission-critical engineering from prototype to Fortune 500 deployments. I led the engineering and launch of Orion, the UK’s most powerful student-built rocket, and simulated superconductors at CERN. MEng, Imperial College London, ranked 1st in my cohort.

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