BlueBear case studies

Representative workflows and how a pilot measures them

Explore representative AI agent workflows for healthcare, finance, connected tools, and white-label platforms, and how a pilot would measure each one.

How BlueBear handles the work

About these workflows

These scenarios are representative of the workflows BlueBear supports. They carry no outcome or improvement figures and no customer quotes, because we publish a result only after measuring it with a customer against their own baseline. Each entry names the measure a pilot would use instead. The DME entry gives only workload context from a connected DME supplier, which we do not name, and is not a result.

Evidence: No outcome figures; supporting captures use demo and synthetic data

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Client reporting for a growth agency

An agency pulling campaign and CRM data by hand for weekly client reports moves that collection, assembly, and delivery onto agents with scoped connections to the ad platforms and CRM.

Evidence: How a pilot measures it: analyst hours per weekly report before and after, from retained run records

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White-label platform for a reseller

A provider serving many subsidiaries gives each one a branded, isolated tenant with its own integrations, model access, and budgets rather than standing up a separate stack per business unit.

Evidence: How a pilot measures it: time to launch a branded tenant, and a tested check that no data crosses tenants

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DME intake and prior authorization

A connected DME supplier working across missing documents, prescriber signatures, prior-authorization tracking, and NikoHealth sync exceptions gets one operational queue around the systems already in place.

Evidence: Workload context, not a result: a connected DME supplier receives about 1,900 new orders in a 30-day window

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Finance reconciliation

A finance team reconciling accounting data against bank statements and invoices by hand moves matching and discrepancy flagging onto agents, with exceptions routed to a person.

Evidence: How a pilot measures it: manual reconciliation hours per month and discrepancies caught, against your baseline

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From request to inspectable outcome

  1. Find the closest scenario

    Use these as a shape for the work, not as a promise of a number, and identify which one resembles your workflow.

  2. Bring your own baseline

    Measure what the workflow costs today in hours, rework, and spend, because an estimate modelled elsewhere cannot substitute for it.

  3. Run one workflow

    Put a single workflow into production with its systems, boundary, and acceptance criteria defined.

  4. Measure your own result

    Compare against your baseline using retained session evidence, so the number you quote afterwards is yours.

Frequently asked questions

Why are there no outcome figures on this page?
Because we have not yet measured an outcome with a customer that we can publish, and a modelled number reads like a result. Each entry names the measure a pilot would use instead. The DME entry gives only workload context from a connected DME supplier, which we do not name. Supporting screen captures are produced from demo and synthetic data.
Will you publish results later?
Only once a pilot has measured them against the customer baseline and the customer has agreed to publication. Until then the page shows the shape of the work, not a number.
What evidence can we see during an evaluation?
A working workflow in your own environment, with retained session evidence covering model routes, tool calls, approvals, retries, failures, and cost, so results are measured against your baseline instead of someone else scenario.
How would we measure a result in our own environment?
Establish a baseline first: what the workflow costs today in hours, rework, and spend. Then compare cost per accepted outcome after the workflow runs, using the retained evidence rather than an impression of improvement.