Reference Implementations

How we approach
real problems.

Three systems in production. Different industries, different constraints — one standard throughout: ship something that actually works.

01 / 03Financial Services

Automated Compliance Reporting

The Challenge

A regional bank spent 40+ hours weekly on manual compliance report generation, with frequent errors and missed deadlines threatening regulatory standing.

Our Solution

We built a multi-agent system that pulls data from 5 different internal systems, validates against regulatory requirements, cross-references historical data, and generates audit-ready reports automatically.

Approach

  1. 01Mapped existing compliance workflows with stakeholders
  2. 02Built secure connectors to core banking, CRM, and document systems
  3. 03Developed validation agents that check regulatory requirements
  4. 04Created audit trail and version control for all generated reports
Engineering takeaway

Single-prompt approaches couldn't handle the cross-system validation. Multi-agent orchestration with a dedicated critic was essential — every report passes through a verification agent before it ever reaches a human reviewer.

What we delivered
Major

Reduction in analyst manual time

Audit-ready

Reports validated against regulator schema before human review

On-prem

Stack runs entirely inside the bank — no data egress

02 / 03Healthcare

Patient Intake Automation

The Challenge

A clinic network processed hundreds of patient intake forms daily, requiring manual data entry and verification across multiple systems, leading to patient delays and staff burnout.

Our Solution

We deployed an intelligent document processing agent that extracts patient information, validates insurance details, checks for data consistency, and updates the EHR system — all while maintaining HIPAA compliance.

Approach

  1. 01Implemented secure document processing pipeline
  2. 02Built insurance verification integration
  3. 03Created intelligent data extraction with validation
  4. 04Designed human-in-the-loop for edge cases
Engineering takeaway

Document AI alone wasn't enough — extraction accuracy lives or dies on validation. We layered a structured validator agent that flags low-confidence fields for human review instead of guessing.

What we delivered
Minutes

Average per-form processing time

HIPAA

Compliant pipeline, end to end

Hybrid

Sensitive data on-prem, models in private cloud

03 / 03Manufacturing

Predictive Quality Control

The Challenge

A manufacturing plant experienced high defect rates and couldn't identify root causes fast enough to prevent batch losses, costing significant scrapped product.

Our Solution

We implemented an analytics agent that monitors production sensor data in real-time, predicts quality issues before they occur, identifies root causes, and alerts operators with specific corrective actions.

Approach

  1. 01Integrated with existing SCADA and sensor systems
  2. 02Built real-time anomaly detection models
  3. 03Created causal analysis for root cause identification
  4. 04Developed operator dashboard with actionable alerts
Engineering takeaway

Latency was the constraint. Cloud round-trips meant defects shipped before alerts arrived — we pushed the inference layer to edge hardware so the operator gets a callout in seconds, not minutes.

What we delivered
Real-time

Anomaly detection on live sensor streams

Causal

Root-cause analysis, not just alerting

Edge

Inference runs on plant hardware — no cloud round-trip

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