Salesforce, NetSuite and HubSpot were built for humans to enter and read data. Their AI features are bolt-ons. We build the modules, the agents and the command center as one product, agent-first — humans supervise.
Every agent run is a closed loop. Context assembles from git, organizational memory and a semantic layer over your data sources. The agent reasons, calls typed tools, and renders widgets to a chat where humans supervise — approving, rejecting, editing, gating every outcome before it commits. Outcomes — state, learnings, artifacts, third-party actions, role refinements — feed the next cycle's context.
Most agent frameworks ship open-ended chains. Ours closes the loop — memory compounds run over run, and product iteration is a PR to a role definition, not a code change.
Agents work the same way whether the data lives in our shared database or in the tools you already use. Native modules give you a single-tenant schema, lightweight and built for agent traversal. MCP connectors expose your existing SaaS to agents as the same typed-tool API — no migration required. Mix both.
Built-in modules in your single-tenant database. Agents read and write directly through typed views — no integration layer, no ETL.
erp facturas · AR/AP · cash · bank reconciliation · periods live crm leads · pipeline · conversations · contacts wip operations deadlines · compliance · recurring filings · tasks planned Connect what you already use through Model Context Protocol servers. Agents see Salesforce, NetSuite, Stripe and the rest as the same typed-tool API as native modules.
salesforcehubspotnetsuitequickbooksstripegmailslacknotiondrivegithublinearasana + any MCP server The Central Command has one UI primitive: a versioned widget the agent emits as a tool call. Data fetch and render are separate, so the agent's reasoning stays visible — it chose what to render — and the same data tool can feed text replies, multiple widgets, or a subagent.
The agent calls a typed data tool, e.g. erp.list_overdue_facturas(days_overdue: 30), and gets back a clean dict of rows.
The agent emits a tool_use block for a widget — e.g. invoice_table_v0 — with those rows as input. The frontend has a React component per widget name and renders it inline in chat.
A widget is a React component the agent invokes by tool name. Here's what four of them look like rendered inline.
invoice_table_v0 shipped | Folio | Party | Amount | Days |
|---|---|---|---|
| 4523 | ACME Mining SpA | $850.000 | 45 |
| 4711 | Andes Logistics | $1.240.000 | 38 |
| 4801 | Norte Trading | $230.000 | 31 |
approval_card_v0 next ask_user_input_v0 next cash_position_chart_v0 planned
Every widget is suffixed _v0, _v1, and so on. When a widget's input schema changes, we add a new version — we never edit the old one. Old conversations keep working. New widgets are additive.
This is on-prem, local-first agentic enterprise software. Not multi-tenant SaaS. Customers deploy the stack into their own environment — their Mac, their server, their cloud. Whoever runs the deployment owns the data and the keys.
When customers want hosted-by-us, we run a dedicated per-customer stack. There is no shared infrastructure across customers, no shared schema, no row-level multi-tenancy. A breach of customer A has zero reach into customer B.
30-minute walkthrough on a real deployment — agents working a real ERP, widgets rendering in chat, the audit log.
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