AI Development - Custom LLM Applications and Automation | Detroit Computing
AI Development

Automate decisions with AImeasure the impact

We build production AI systems using large language models—document processing, conversational interfaces, and intelligent automation that integrates with your existing workflows and delivers measurable ROI.

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AI applications we build

Clinical Documentation

Transform physician-patient conversations into structured clinical notes in real-time. Extract ICD-10 codes, medication lists, and treatment plans from unstructured voice recordings while maintaining HIPAA compliance.

Legal Document Analysis

Review contracts, identify non-standard clauses, flag potential risks, and extract key terms across thousands of documents. Reduce contract review time from hours to minutes while maintaining accuracy standards.

Insurance Claims Processing

Automatically classify claims, extract relevant policy information, identify potential fraud patterns, and route to appropriate adjusters. Process claims 10x faster with consistent decision-making logic.

Customer Support Automation

Build AI assistants that understand product documentation, historical tickets, and company policies to resolve tier-1 support issues automatically. Escalate complex cases to humans with full context and suggested solutions.

Manufacturing Quality Control

Analyze inspection reports, defect images, and sensor data to classify quality issues, recommend root causes, and generate corrective action reports. Connect production data with quality outcomes to predict defects before they occur.

Financial Document Intelligence

Extract data from invoices, receipts, purchase orders, and financial statements across multiple formats. Reconcile transactions, flag anomalies, and maintain audit trails for compliance and reporting.

How we approach AI projects

We focus on economics, error rates, and integration—not hype

1

Validate unit economics first

Calculate per-query costs at expected volume and compare against manual process costs. If the economics don't work, we tell you before building anything.

2

Design for error tolerance

Build validation layers between AI steps to prevent error compounding. Implement human-in-the-loop workflows for high-stakes decisions.

3

Integrate with existing systems

Connect AI capabilities into your current workflows, databases, and tools. No need to replace working systems—augment them.

4

Monitor and iterate in production

Implement logging, monitoring, and evaluation frameworks to track performance over time and improve models based on real-world usage.

Let's talk about your project

Whether you have a clear vision or just a problem that needs solving, we'd love to hear about it.

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