Plan, act, verify.
Goal decomposition, structured planning, tool selection, approval gates, retries, execution state, and audit history.
- Structured outputs
- Tool registry
- Human approval
- Run verification
CAPABILITIES
We build the execution layer between AI and work.
Kernolix combines product engineering, agent runtimes, integrations, and evaluation so an AI system can move from a useful answer to a controlled business outcome.
WHAT WE ACTUALLY SHIP
Four capabilities. One product direction.
Not a menu of generic agency services. These are the building blocks required to make controlled AI execution useful in production.
Connect to the work.
OAuth and API connectors that expose narrow, permissioned capabilities instead of handing an agent unrestricted access.
- Gmail connector beta
- CRM bridge architecture
- Webhook/API surfaces
- Secret handling
Build the product around the model.
Interfaces, multi-tenant application logic, workflow UX, admin controls, model routing, fallbacks, and production observability.
- Model-flexible runtime
- Workspace isolation
- Usage controls
- Failure-safe UX
Measure completed work.
Judge automation by useful outcomes—not impressive demos. Capture errors, approvals, correction rates, time saved, and task completion.
- Audit logs
- Run outcomes
- Quality checks
- Cost visibility roadmap
BUILD PRINCIPLE
Start narrow. Earn autonomy.
We start with one workflow, keep high-impact actions behind approval, prove that the system saves real work, then expand permissions and integrations only where the evidence supports it.
ONE WORKFLOW
Bring us the bottleneck.
Use the diagnostic to see whether a repetitive process is a good fit for controlled AI execution.