Product

Four parts. One channel.

This is not a white-label chatbot. It is the place a network trains, isolates, and serves AI.

atlas-sp

Qualify this lead?

Score 86 · book demo

own memory

norte-rj

Delivery time?

3 business days

own memory

delta-ba

Resend the invoice

Sent by email

own memory

no shared context

Agents

An agent per partner, with its own tools and memory. A stable endpoint, a login, a cap. You decide what they can see. The runtime keeps that promise.

  • Gemini or Gemma through Vertex AI, or another model you name

  • No shared context between tenants

  • Hooks into the CRM or ERP the partner already has

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train · atlas-spSFT · LoRA

dataset.jsonl2.400 examples✓

loss 0.42

epoch

v1v2v3promoted

Training

Fine-tune as a job, not a six-week project. Metrics go to BigQuery. You get a versioned endpoint.

  • SFT or LoRA on Gemma, or an adapter you bring

  • Datasets in Cloud Storage, with a life cycle

  • Promote a model to one partner without promoting it to all

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  1. create
  2. run
  3. snapshot
  4. destroy

running

Sandboxes

A short-lived box for code, a file, or a tool the agent should not run on a laptop. Snapshot, restore, a ceiling.

  • Our image or the channel’s

  • Tight network, secrets injected

  • It turns off when the job ends

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POST /qualify200 · 51ms

worker-142ms

worker-238ms

worker-351ms

scaled to 0

Functions

A function becomes HTTP. It follows channel traffic and sits quiet after hours. You get a log and a line on the bill.

  • Python SDK to declare the agent

  • A gateway with a cap per partner

  • Queues for batch, webhooks for events

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How a first install goes

  1. 01 · A call

    We map the network, the data that cannot leak, and the first agent worth standing up.

  2. 02 · One environment

    A Google Cloud project, São Paulo, one pilot partner, a spend cap.

  3. 03 · The rest of the channel

    Onboard the others, keep your brand on the front, one invoice on the back.

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