Add AI where it makes the product better.
Hatchery uses AI to improve workflows without giving up architecture, UX, security, or ownership.
Start with the workflow.
AI belongs where it improves a real user task: search, review, extraction, routing, QA, or automation.
Trusted data, scoped tools, human review, logging
Define the task
Choose a workflow where AI can create visible product value.
Ground the data
Connect documents, records, product data, and business rules carefully.
Constrain the behavior
Use review paths, logs, access control, evaluation, and fallback behavior.
Operate it
Monitor quality, tune behavior, protect data, and keep humans accountable.
AI work that belongs in products
Product workflows
Search, assistants, extraction, routing, QA, summaries, and decision support tied to real work.
Production guardrails
Prompt design, retrieval tuning, evaluation sets, access control, logs, review flows, and fallback paths.
Agents need boundaries before autonomy.
A useful agent needs approved tools, identity, data limits, memory policy, audit trails, rollback behavior, and an owner.
Approved tools
Agents should only call approved APIs, MCP services, actions, and data sources.
Observable actions
Logs, traces, and review paths make behavior understandable after launch.
Protected data
Sensitive records, payment boundaries, secrets, and retained memory need explicit controls.
Operational response
Fallbacks, approvals, alerts, incident routines, and tuning cycles reduce risk.
Take the product idea, complex workflow, or AI opportunity and make it real.
Clarify what to build, what to simplify, what to protect, and what must work first.
Start with Hatchery when
- A SaaS product needs its first serious release
- An early product or inherited app needs architecture
- AI, payments, data, or hosting must be controlled
- The product needs ownership beyond launch
Clarify the path
- Outcome and first release
- Workflow, UX, and data model
- Architecture and technical risk
- Security, operations, and ownership