Switching AI models is not like switching coffee brands. You don’t just swap it out and hope nobody notices. Entire pipelines break, outputs change tone, and suddenly your “stable” system behaves like it just discovered chaos.

This case study explores how a FinTech-style company migrated from Claude to 深度搜索, why they did it, what broke along the way, and what actually improved.

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Background: Why the Company Used Claude Initially

Claude was initially selected for three main reasons:

• Strong natural language reasoning
• Reliable long-context handling
• Safety-focused outputs

For early-stage systems, Claude worked well in:

• Customer support automation
• Document analysis
• Risk summarization

But as the company scaled, cracks began to show.

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The Problem: Scaling Limitations with Claude

1. Rising Costs

As API usage increased, costs scaled linearly—and painfully.

• High per-token pricing
• Expensive long-context usage
• Inefficient for high-frequency tasks

2. Latency Issues

Real-time systems began to suffer:

• Slower response times
• Bottlenecks in transaction pipelines

3. Limited Customization

Fine-tuning and control over outputs were limited compared to newer models.

4. Overkill for Structured Tasks

Claude performed well in general reasoning, but many tasks were repetitive and structured—making it inefficient.

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Why DeepSeek Was Chosen

The company evaluated multiple models before selecting DeepSeek.

Key Advantages

• Lower inference cost
• Strong performance in structured reasoning
• Better code and data processing capabilities
• Flexible deployment options

DeepSeek was particularly effective for:

• Risk scoring
• Fraud detection pipelines
• Data-heavy analysis tasks

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Migration Strategy

A direct switch would have been reckless. Instead, the company followed a phased migration.

Phase 1: Parallel Testing

• Claude and DeepSeek ran simultaneously
• Outputs were compared for accuracy
• Edge cases were identified

Phase 2: Task Segmentation

Tasks were categorized:

• Keep on Claude (creative, sensitive tasks)
• Migrate to DeepSeek (structured, repetitive tasks)

Phase 3: Gradual Rollout

• Low-risk systems migrated first
• High-risk systems followed after validation

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Technical Implementation

API Layer Changes

• Switched endpoints
• Updated request formats
• Adjusted token handling

Prompt Engineering Adjustments

Claude-style prompts did not translate directly.

DeepSeek required:

• More structured instructions
• Clear input-output expectations

Output Normalization

Differences in formatting required post-processing adjustments.

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Performance Comparison

Cost Reduction

• 40–70% lower inference costs

Latency Improvement

• Faster response times in real-time systems

Accuracy

• Comparable or better in structured tasks
• Slight differences in open-ended reasoning

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Impact on Risk Analysis Systems

DeepSeek significantly improved:

• Fraud detection speed
• Risk scoring accuracy
• Data processing efficiency

The system could now handle:

• Higher transaction volumes
• More complex data inputs

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Challenges During Migration

Output Variability

Different models produced slightly different answers.

Prompt Rewriting Overhead

Existing prompts had to be redesigned.

Team Adaptation

Engineers needed time to understand new model behavior.

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Lessons Learned

1. Don’t migrate everything at once
2. Test extensively with real data
3. Optimize prompts for each model
4. Monitor performance continuously

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Final Results

After full migration:

• Operational costs decreased significantly
• System performance improved
• AI infrastructure became more scalable

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Conclusion

Migrating from Claude to DeepSeek wasn’t just a cost-saving decision—it was a strategic shift toward efficiency and scalability.

For companies dealing with high-volume, data-heavy workflows, DeepSeek proved to be a strong alternative.

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