The overarching layer

Use natural language. Get profit math back.

Orbit AI sits across the platform as the overarching analytics layer. Ask the question; get the answer in seconds — without a data-pipeline project, a data engineer, or a dashboard that ships nine months late.

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How the query surface works.

Step 1 of 4

You ask in natural language

"Top 10 customers by margin this quarter." "Which customers had the most adjustments last month?" "Carrier-cost trend on USPS Priority over the last 90 days?"

Step 2 of 4

Orbit translates against the schema it already knows

No schema-training step. Orbit ships pre-trained on RocketFuel's model — customers, shipments, carriers, services, markups, adjustments — and maps your question to the right query.

Step 3 of 4

You get the answer in seconds, with the rows

The result includes the numeric answer plus the underlying rows — so you can verify the math or copy it straight into your own reporting.

Step 4 of 4

You decide, with your team

Bring the answer into the conversation and make the call together — then save the questions you return to, so you are not rebuilding them next time.

What Orbit can answer.

  • Natural-language queries Natural language. No SQL, no DSL, no chart-builder. The conversation surface is incidental — the answer is the point.
  • No data pipeline Orbit runs over the same metering data that drives billing. No separate data warehouse, no data-engineering project, no nine-month build.
  • Pre-trained schema Ships knowing customers, shipments, carriers, services, markups, adjustments. You don't train it on your data — it already knows the model.
  • Verifiable answers Every result includes the underlying rows so you can audit the math. For ambiguous queries, Orbit asks clarifying questions before returning.
  • Decide as a team Share the answer and the rows behind it with whoever needs it — so your team makes an informed decision together, not after the next report.
  • Latest AI models Built on the latest AI models, tuned to the language of 3PL billing — domain knowledge baked in, not bolted on.

More across the platform

Flagship Recharge Meter

The metering pipeline produces the data Orbit queries — same source of truth.

 Flagship Adjustment Reconciliation

Orbit answers questions about which clients drive the most adjustments and where margin leaks.

 Sibling Claims & Disputes

Eligible-claims data answers Orbit queries about recovery trends and adjustment categories.

Frequently asked questions

Is Orbit AI a chatbot?

It's a natural-language query interface, not a chatbot. You ask a question; it returns numbers and the rows it ran against. The conversation surface is incidental — the answer is the point.

Does it use my data to train an external model?

No. Orbit's schema knowledge ships pre-trained on RocketFuel's billing model. Your customer-specific data is queried at runtime and never sent outside our infrastructure for training.

How accurate are the answers?

Orbit returns the rows it queried alongside the numeric answer — verify the math directly. For ambiguous queries, Orbit asks clarifying questions before returning a result.

Why most 3PL analytics projects stall.

The standard playbook: pull data out of the WMS and the carrier-billing system, load into a warehouse, build dashboards, train the team. Costs six figures, takes nine months, and the dashboards usually answer questions nobody is asking by the time they ship. The questions 3PL operators actually ask — which customers are priced too thin, what's the margin trend on USPS this quarter — are answerable in real time if the data lives in one place. The metering pipeline is that one place. Orbit is the query surface.

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