10,908
Scheduled task runs since February 2026
What we build
Production AI agents, inside the operations of real companies.
A governed AI workforce and company operating layer. Agents live where teams already work, Slack, Telegram, WhatsApp, and the web, connect to current company systems, execute defined work, stop at explicit approval boundaries, and prove the result where it lands.
98.0%
Finished without error (10,685 of 10,908)
5
Production hosts, one codebase
41
Tool integrations in catalog
58
Governed skills
Task counts measured 2026-09-12 14:25 UTC across five production hosts, counts only. Last 30 days: 3,821 runs, 96.3% finished without error. The since-February total is a lower bound: four hosts' run logs begin in July 2026.
Architecture
How a request becomes finished work.
Every message runs through an isolated, per-tenant runtime. The agent reasons, calls governed tools, delegates to subagents, and returns the result to the channel it came from.
In
Message
Slack · Telegram · WhatsApp · Web
Route
Queue
Per-group, ordered, durable
Execute
Isolated runtime
Isolated agent runtime · per-workspace
Integrations
Skills
Subagents
Memory
Out
Channel
Finished work, in context
Isolation by default. Each tenant runs in its own workspace with policy-controlled tool access - no shared state, no cross-tenant reach. Governed tools. Agents only ever see the tools and credentials their workspace is permitted to use.
Capabilities
What the agents actually do.
Not chat. Completed operational work, delivered into the channel the request came from.
01
Produce finished reports
Pull from live systems, analyze, and return a structured deliverable - not a prompt-and-paste draft.
02
Run campaigns & outreach
Find leads, draft and sequence messages, and act across the tools that hold the data.
03
Manage operations
Move information between ERP, sheets, drive, and email; keep records and workflows in sync.
04
Reason over a knowledge base
Persistent, per-tenant memory means the agent remembers context across days, not just one thread.
Under the hood
Engineered for production, not a pilot.
Runtime
A real agent runtime: tool-use, planning, and delegation, not a thin API wrapper.
Model routing
Right-sized by task: the most efficient model that clears the work, selected by evaluation, not guesswork. Routed per workspace between frontier models and local ones.
Local inference
Routine work runs on open models on our own NVIDIA GPUs: Qwen3.8-27B in NVFP4 on vLLM across two RTX 5090s, in production, with an 82.6% prefix-cache hit rate over 28,097 production requests (2026-08-29 to 2026-09-12, engine counters).
Tooling
41 tool integrations in catalog, exposed to each tenant under policy-controlled access.
Skills
58 gated skills - reusable, permissioned capabilities composed at runtime.
Verification
Tool outcomes are checked for silent failures; where a workspace enables it, an independent check reviews the final claim against evidence before delivery, with bounded repair and escalation to a human.
Memory
Persistent, scoped per tenant - durable context across sessions.
Isolation
Per-workspace separation with governed tools and credentials. No shared state.
Channels
One platform across Slack · Telegram · WhatsApp · Web, streaming responses in real time.
In production
Live, today, doing real work.
Agents run day to day across reporting, content, campaigns, back-office, and data work. The same operating model extends to work orders, field updates, finance exceptions, and management decisions without making the model the source of truth. One accessible production snapshot, February to August 2026, across five hosts: 4,471 agent sessions, 3,623 subagent runs and 143,145 tool calls recorded with their tool results (143,043 result blocks), as decisions, not as chat: an accessible lower bound, snapshot 2026-08-24.
Built for the work
Bringing production AI agents into the verticals and markets that automation hasn't reached yet.