/ The Real-Time Agent Factory /

Let’s Build Your First Real-Time Agent!

Bring us the problem. We build the agent end to end and run it in production. Then your team builds the next one.

/ Use cases /

See a real-time agent at work

What’s a real-time agent?

A real-time agent is an AI agent that reasons and acts on live context, kept current continuously. It can wake up when that context changes, then decide and take action.

1. the problem

A pricing error just went live. Deal sites will find it before your team does.

2. live context

  • orders_per_min_sku 380
  • normal_for_this_hour 6
  • margin_per_order -$41
  • traffic_from_deal_sites 72%

3. The agent

Pulls the price, holds unshipped orders for review and alerts merchandising.

4. The result

The error costs minutes of orders, not a weekend of losses.

/ How it works /

We made it simple

Let's build production-ready autonomous real-time agents. You bring a problem where stale data costs money, and we build your first one with you, end to end. Then your team builds every agent after it on the same platform.

Step 1

Bring the problem

One conversation. No RFP.


Step 2

We build it

Data, live context, the agent and its guardrails, end to end.

Step 3

It goes live

In production, in your cloud, once it proves its value.

Step 4

You build the next

Same platform, same playbook, your team.

/ Who it’s for /

One platform for every real-time agent after

Access over MCP

Agents see only the context they're allowed to.

Live context

Always-current context from your streaming data.

Triggers

Agents wake the moment a threshold is crossed.

Runs where your data is

Your own cloud, Snowflake or Databricks.

What goes wrong
when
your data is late?

Every business has decisions that go wrong when data arrives late. Tell us the one that costs you most. If a real-time agent can fix it, we’ll build it with you, end to end.

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