AI agents for real operations. Across your company.
Scalably builds a governed company operating system: an AI workforce inside Slack, Telegram, WhatsApp, and the systems your company already uses. Agents turn plans into assigned work, verified results, and decisions while your people control consequential actions.
Task counts measured 2026-09-12 14:25 UTC across five production hosts, counts only.
One operating layer. Across the company.
The agent receives work, retrieves current sources, acts through approved tools, handles expected failures, and verifies the result where it lands.
Plan & coordinate
- Turn decisions into assigned, tracked work
- Carry source evidence into each task
- Run daily and weekly operating cadences
- Route exceptions to a named owner
- Keep open loops visible until closure
Operate & confirm
- Collect text, voice, photos, files, and events
- Match updates to the correct person and task
- Ask for missing information
- Update approved operational records
- Read the accepted result back
Reconcile & report
- Join CRM, ERP, invoices, and operations
- Separate official values from estimates
- Prepare decision-ready reports
- Surface plan-vs-actual differences
- Turn a flagged number into a next action
Monitor & recover
- Detect stale sources and blocked work
- Retry without duplicating side effects
- Preserve unresolved inputs for review
- Alert the right role with evidence
- Show exactly where a workflow stopped
Research & prepare
- Gather cited market and account research
- Compare options against company constraints
- Draft documents, plans, and proposals
- Prepare CRM and customer work
- Preserve sources for review
Create & deliver
- Reports, spreadsheets, decks, and documents
- Campaign and customer communication drafts
- Approved delivery through the right channel
- Scheduled work with explicit ownership
- Destination-side completion proof
Map, prove, then expand.
Map the loop
Choose one recurring job. Define its source systems, authority, accepted examples, completion state, and failure owner.
Prove the result
Run read-only, replay, or shadow evaluation first. Add one reviewable output, one approval-bound action, and destination readback.
Expand authority
Add the next role, system, or automated action only after the previous loop produces trusted evidence and has a recovery path.
The systems that hold the truth.
Each deployment receives only the sources and actions its workflows require. We connect established tools and build narrow private adapters when needed.
The agent operating layer connects systems without pretending chat memory is the company database.
Every function, its own agent.
An agent can be configured around one function's work without becoming a generic company super-user. Each role receives only the context, tools, and authority required for its operating loop.
See a company get its own HQ: ERP shadow, paper archive, field books, worker check-ins by SMS. Or how the operating loop works in agriculture.
Scheduled task runs, last 30 days
Across five production hosts and 1,052 distinct scheduled tasks, measured 2026-09-12 14:25 UTC. Counts only; no task content is read.
Finished without error
3,681 runs finished without a scheduler error and 140 errored; this is the runtime status, not a human acceptance judgement. Since February 2026: 10,908 runs at 98.0%, a lower bound because four hosts' run logs begin in July. The rate is not a promise for every client, channel, or workflow.
Checked where it lands
Completion means the intended file, record, task, or delivery state exists outside the model response.
Context that never replaces the source.
The agent reads current systems, uses scoped working context, and retains accepted corrections. Model memory never replaces CRM, accounting, operational records, or approved documents.
Don't trust the claim. Install it.
Parts of the platform our own agents run on are public. Install a server, read its exact tool surface, and verify the checksum before you trust it. You bring your own keys, and each server talks to its vendor's API and nothing else.
- 8 servers we run in production, open under the io.scalably namespace
- On the official MCP registry, with packages on PyPI and npm
- Every release ships a checksum you can verify against the file
- 11 third-party servers we run are listed with the versions we pin
- 22 skills taken from the platform we run for clients
- Two plugins, agent-ops and seo-ops, installed in one command
- Each page carries the full source of what you would install
- Every page answers in markdown when an agent asks for it
Scoped to the work, not seats.
Every deployment is scoped to the work rather than sold as a fixed package. We map the operating loop, then quote the build and ongoing operation. See what an AI agent development company should deliver before you hire one.
- A one-time build, scoped to the agents and integrations you need
- We bill only what we can defend and only where value was created: invoices that stop, work that is accepted, time that management redeploys
- We take an engagement only when that value clearly exceeds the fee; production ownership for new engagements starts at $5,000 a month for that reason
- Every month's report shows the counted work behind the invoice; infrastructure and model usage at cost above the signed capacity
- Custom work, custom integrations, built around your operation
- The quote states what is built, operated, and measured
- We start with one real workflow you'd hand off
- We scope what it takes, integrations, channels, schedule
- You get a fixed quote before anything is built
- Replay or shadow proof before authority expands
Show us one operating loop.
Bring one recurring workflow your company would hand off. We will map the sources, authority, completion proof, and smallest production path.