Reasoning vs execution
Process.co draws a bright line between what the model decides and what the platform runs:
- Reasoning: LLM decides intent, plans next steps — ephemeral inference
- Execution: platform runs workflows with contracts — persisted state, queue, retry, audit
Observability before automation
You cannot improve what you cannot see. Process.co respects years of operational expertise — businesses already have processes that work, often stuck in inboxes and spreadsheets.
Make work visible first. Improve incrementally with AI amplifying human judgment, not replacing discipline.
Institutional context
Today’s assistants are personal. That creates silos. Process.co designs for layered context:
- Personal — preferences and private threads
- Channel / department — shared routing and Slack/Teams surfaces
- Enterprise memory — datasets, files, execution history, templates
- Execution state — versioned flows, step outputs, audit trail
Codify, then execute
As processes mature, fewer runtime decisions should go through AI. Repeated judgment calls codify into versioned blocks — flows, elements, Skills — executed deterministically with full audit.
AI’s enduring role is on the improvement loop: observe runs, evaluate bottlenecks, and propose block upgrades — not re-deciding what you already know every day.
- Wrong tech, wrong job: clear business rules belong in blocks, not per-hop LLM inference
- Success: inference share per unit of work trends down while throughput and auditability trend up
How this differs from Zapier
Zapier connects triggers and actions. Process.co adds versioning, team governance, durable execution sessions, and agent contracts suited to operational scale.