Unify GTM brain

category

market profile

• text: The current site presents a continual-learning research agenda and publications, rather than the LLM routing product in its older YC launch.
• text: Its public GitHub describes post-training agent models from production work with evaluation gates; the present commercial offering should be confirmed.

personas

• title: AI research lead
  pains:

  • Models do not automatically incorporate experience from tasks
  • Continual learning must improve performance without losing prior capabilities
• title: Applied AI engineering lead
  pains:

  • Agent corrections and retries do not directly improve a frozen model
  • Teams must validate changes before putting a trained model into production
• title: Agent product founder
  pains:

  • Agent quality plateaus even while usage creates feedback
  • Prompt and memory maintenance accumulates across repeated workflows

icp

• text: AI-native product companies operating agents repeatedly on tasks and collecting feedback or execution traces; inferred fit.
• text: Research and applied ML teams investigating learning from sequential experience and model evaluation.

message pillars

• text: Build models that continue to improve.
• text: Make agent experience a source of improvement.
• text: Share the underlying research.

objections

• objection: How do we prevent a model update from degrading previous tasks?
  response: Define held-out evaluations and forgetting checks before attempting a production rollout; discuss the update gates with the team.
• objection: We already add memory to the agent harness.
  response: Discuss the tradeoff between recalling notes in the harness and incorporating learning in model weights, and test on a repeatable workflow.
• objection: Is there a commercial product ready to buy?
  response: Confirm availability, deployment and access directly because the current homepage emphasizes research and does not publish terms.

competitors

• name: Ashr
  website: https://ashr.io/
  why: Offers post-training, evaluation and continual learning for enterprise models.
• name: Thinking Machines Lab
  website: https://thinkingmachines.ai/tinker/
  why: Tinker supports training models on production traces and continual learning research.
• name: Overmind
  website: https://www.overmindlab.ai/
  why: Targets specialized agents using production traces and model training.

keywords

• continual learning AI agents
• agent post training
• learn from agent traces
• online model learning
• catastrophic forgetting LLM
• reinforcement learning agent training
• agent evaluation benchmarks

voice

• research-led
• technical
• plainspoken and conceptual

signals

• text: An AI team is logging agent corrections, retries and outcomes but cannot turn them into model improvements.
• text: A research group is investigating continual learning or evaluating forgetting in agent models.

pricing

motion

channels

• research blog
• GitHub
• YC launch

confidence

Founders

• name: Daniel Lenton
  title: Founder
  role: Founder
  bio: Love to dive deep into engineering rabbit holes 🕳️🐇