Case Studies

Results from production AI systems

Every engagement is different. What's consistent is the standard: working software, measured outcomes, and systems that hold up under real operational pressure.

Details anonymized to protect client confidentiality.

Financial Services Mid-Market

Document Processing Pipeline

The Problem

A mid-market financial services firm was processing 2,000+ compliance documents monthly using a team of analysts. Each document required extraction, classification, cross-referencing, and escalation routing. The manual process introduced inconsistencies and couldn't scale with growth.

The Approach

We built a multi-stage document intelligence pipeline: a classifier agent to categorize incoming documents, extraction agents for structured data capture, a cross-reference layer against internal policy databases, and a routing agent to escalate edge cases to human review. The pipeline included a cost optimization layer that identified repetitive extraction patterns and converted them to deterministic functions.

Outcomes

71%

Cost reduction vs. manual process
12x

Faster processing per document
99.2%

Classification accuracy

Timeline

4 weeks to production

Content Platform Growth-Stage

Multi-Agent Content Pipeline

The Problem

A growth-stage content platform was producing editorial content for dozens of clients across multiple verticals. Their editorial team was the bottleneck: human writers couldn't keep pace with demand and the quality of outsourced content was inconsistent. They needed throughput without sacrificing voice consistency.

The Approach

We designed a hierarchical multi-agent architecture: a research agent to gather and synthesize source material, specialist writing agents fine-tuned to each client's voice profile, a consistency agent to enforce style guidelines, and a quality review agent to flag deviations before human sign-off. Provider-agnostic routing allowed the system to select the optimal model per task based on cost and capability requirements.

Outcomes

5x

Content throughput increase
68%

Reduction in editorial revision cycles
43%

Lower AI cost per published piece

Timeline

6 weeks to production

Healthcare Operations Mid-Market

Clinical Workflow Automation

The Problem

A healthcare operations company was managing patient intake, appointment routing, prior authorization requests, and follow-up scheduling across multiple clinic locations. Administrative staff spent the majority of their day on repetitive coordination tasks, leaving limited capacity for cases that genuinely required human judgment.

The Approach

We implemented a workflow automation layer with strict human-in-the-loop controls for clinically sensitive decisions. Intake triage, scheduling optimization, and prior authorization form generation were fully automated. The system included a complete audit trail for every AI decision: what input it received, what it decided, and on what basis. HIPAA-compliant data handling was built into the architecture from the start, not retrofitted.

Outcomes

85%

Administrative tasks fully automated
2.4x

Increase in cases handled per coordinator
100%

Decision auditability maintained

Timeline

5 weeks to production

How we approach every engagement

Each case study above followed the same underlying discipline: understand the problem before proposing a solution, build incrementally so you can validate results early, and design for the operational reality of your team.

We don't build demos or proof-of-concepts that never get used. Every system we ship is built to run in production, with full observability so you can see exactly what it's doing and what it's costing.

Discovery Sprint

We map your workflows, identify AI leverage points, and define measurable success criteria before writing a line of code.

Incremental Build

We ship working components early so you can validate results and provide feedback before the full system is complete.

Observability by Default

Every system ships with dashboards showing cost, performance, and decision quality. You always know what your AI is doing.

Ready to see results like these?

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