Build vertical AI agents. Connect them to real tools via MCP. Train them using structured human feedback. Operate them safely in production. Continuously improve them over time.
Most AI agents rely on static prompts with no memory of past mistakes. They repeat the same errors indefinitely without learning.
Tool connections are brittle and break easily. There's no standardized way to govern, permission, or measure tool performance.
"Black box" decision-making prevents enterprise adoption. Behavior is uninspectable and decisions are not auditable.
Most teams either: Use frameworks that are hard to control, or build internal systems that are expensive and unscalable. There is no infrastructure layer focused on agent learning + operational reliability.
Four integrated layers that transform static prompt-based systems into trainable, domain-specific workers.
Agents connect to MCP servers with explicit tools. Tool usage is permissioned, observable, and measurable. Tool performance becomes a learning signal.
Agents ingest domain-specific sources: documents, logs, policies, databases. This creates a verticalized reasoning space. Each agent becomes specialized.
The agent performs real tasks. It selects tools, reasons over domain knowledge, and produces actions or responses. But this is not where the system stops.
After each interaction, users give structured feedback. The system updates behavioral rules, tool preferences, memory, and constraints.
A flywheel effect where feedback continuously improves performance and reliability.
Link APIs & databases via the standardized Model Context Protocol.
Index documentation and wikis into the RAG memory system.
Agent plans and uses tools to solve requests autonomously.
Human review or auto-eval scores the quality of outputs.
Update policies and behaviors for improved future runs.
Human-Trained Vertical Agent Infrastructure — or more simply: The operating system for AI workers.
Trained on deep vertical knowledge, eliminating hallucinations common in general-purpose models.
Adheres strictly to enterprise compliance rules, safety protocols, and standard operating procedures.
Users trust tools that speak their language and understand their specific context.
Your platform sits between LLM capabilities, MCP tools, and human supervision — turning them into reliable, specialized, continuously improving AI workers.
Moving beyond prompt engineering to true onboarding, skill acquisition, and performance reviews for digital workers.
Tools are governed. Behavior is inspectable. Decisions are auditable. Feedback improves future outcomes.
The fundamental infrastructure layer that enterprises rely on to manage, govern, and orchestrate their AI workforce.
Build specialized agents that understand your domain and integrate with your tools.
Resolve tickets by querying knowledge bases, executing actions in CRM, and escalating complex issues to humans.
Qualify leads, update pipelines, draft personalized outreach, and sync across your entire sales stack.
Monitor infrastructure, trigger runbooks, coordinate incident response, and learn from post-mortems.
Be among the first to build AI agents that actually learn and improve.
Early access for founding customers. No credit card required.