Case Studies

Proof from AI systems that made it into operations

Selected Avicenna engagements across claims automation, reporting, and operator tooling. These are shipped systems with measurable business outcomes, not prototype theatre.

Portfolio Signal

Measured gains across speed, volume, and visibility

Each engagement began with a specific operating bottleneck, then moved into a production workflow the client could actually run day to day.

$50M+

processed through a production claims workflow

~10m to <10s

reduction in a key claims processing step

5x

efficiency improvement in claims operations

Featured Case Study

Claimo: AI-first claims processing

Avicenna built a production-grade claims workflow that converted slow manual review into a governed AI pipeline, helping the team process $50M+ in claims while cutting key handling steps from around 10 minutes to under 10 seconds.

Read the full case study

$50M+

claims processed

<10s

for a key processing step

Selected Engagements

Current case study

Financial Services

Claimo: AI-first claims workflow

A governed claims pipeline that transformed manual review into a production AI workflow aligned to operational and compliance requirements.

$50M+

processed through the workflow

5x

efficiency improvement

~10m to <10s

key task reduction

Workflow automation LLM extraction Operator review

What Carries Across

Shared patterns in the work

01

Start with a bottleneck, not a demo

Each project begins with a process that is already costing time, money, or visibility.

02

Ship systems operators can actually use

The output is an owned workflow, dashboard, or tool embedded into how the team works day to day.

03

Measure outcomes that matter

We anchor success in throughput, reporting load, cycle time, or revenue visibility rather than vague AI adoption claims.

Next Step

Want your workflow to be the next proof point?

Share the bottleneck, reporting gap, or manual process slowing your team down. We will map where AI should go first and what a production rollout would look like.