AI ML

🎭 Anthropic Claude

Enterprise-grade AI with Claude's long context and safety features

2+ Years Experience
10+ Projects Delivered
βœ“ Available for new projects

$ cat services.json

Claude Integration

Integrate Claude into your applications with best practices.

Deliverables:
  • API integration
  • Model selection strategy
  • Streaming implementation
  • Error handling
  • Cost optimization

Long Document Processing

Leverage Claude's 200K context for document-heavy applications.

Deliverables:
  • Document chunking strategy
  • Context window management
  • Multi-document analysis
  • Summarization pipelines
  • Citation tracking

Claude for Enterprise

Deploy Claude with enterprise-grade security and compliance.

Deliverables:
  • Data privacy compliance
  • Audit logging
  • Rate limiting
  • Fallback strategies
  • Cost monitoring

$ man anthropic-claude

Claude Model Selection

Claude 3 Opus

  • Best for: Complex reasoning, analysis
  • Context: 200K tokens
  • Trade-off: Highest quality, higher cost

Claude 3.5 Sonnet

  • Best for: Balanced quality/speed
  • Context: 200K tokens
  • Trade-off: Great for most use cases

Claude 3 Haiku

  • Best for: Fast, simple tasks
  • Context: 200K tokens
  • Trade-off: Lowest cost, simpler tasks

Why Claude for Enterprise

Claude excels in enterprise settings:

  • Long Context: 200K tokens for large documents
  • Safety: Constitutional AI training
  • XML Output: Structured, parseable responses
  • Reliability: Consistent behavior
  • Privacy: Enterprise-focused data handling

$ cat README.md

Claude Integration Example

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from anthropic import Anthropic

client = Anthropic()

def analyze_document(document: str) -> dict:
    """Analyze long document using Claude's 200K context."""
    
    response = client.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=4096,
        messages=[{
            "role": "user",
            "content": f"""
Analyze this document and provide structured output.

<document>
{document}
</document>

Respond in XML format:
<analysis>
  <summary>Brief summary</summary>
  <key_points>
    <point>Key point 1</point>
    <point>Key point 2</point>
  </key_points>
  <entities>
    <entity type="person">Name</entity>
    <entity type="org">Organization</entity>
  </entities>
  <sentiment>positive/negative/neutral</sentiment>
</analysis>
"""
        }]
    )
    
    return parse_xml_response(response.content[0].text)

Claude vs OpenAI Comparison

FeatureClaude 3.5 SonnetGPT-4 Turbo
Context200K tokens128K tokens
Structured OutputXML preferredJSON mode
SafetyConstitutional AIRLHF
SpeedVery fastFast
Best ForDocuments, analysisCode, general

Experience:

Case Studies: Enterprise RAG System | Agentic AI Knowledge Systems | Multi-LLM Orchestration

Related Technologies: OpenAI, LangChain, MCP, RAG Systems, AI Agents

$ ls -la projects/

Legal Document Analysis

@ Anaqua
Challenge:

Analyze multi-page patent documents that exceed typical context limits.

Solution:

Claude 3 Opus with 200K context for full document analysis, XML structured output for reliable parsing.

Result:

Complete document analysis without chunking, higher accuracy.

Enterprise Knowledge System

@ Sparrow Intelligence
Challenge:

Build Q&A system requiring nuanced, careful responses.

Solution:

Claude Sonnet for balanced speed/quality, structured prompts with safety guardrails.

Result:

Reliable, helpful AI assistant for enterprise users.

$ diff me competitors/

+ Production Claude experience in enterprise settings
+ Long context expertiseβ€”200K token workflows
+ Multi-model strategyβ€”Claude + OpenAI + others
+ XML/structured output patterns
+ Cost optimization across Claude models

Build with Claude

Within 24 hours