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AI Integration

Add intelligence to your existing software with Claude, GPT, Gemini — practical, measurable AI features with ROI.

AI integration means your existing software gains intelligent features: automatic answer to customer questions, document summarization, content generation, categorization, anomaly detection, personalized recommendations. We develop integrated solutions with Claude (Anthropic), GPT (OpenAI), Gemini (Google), Mistral and open source models (Llama, Qwen).

Which model to use for which scenario is a critical decision: criteria like cost, latency, accuracy, privacy determine the right model.

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Which Model for Which Scenario?

Complex reasoning: Claude Opus / GPT-4 class. Fast/cheap: Claude Haiku / GPT-4 mini. Code: Claude. Visual: Gemini, GPT-4o. Multimodal: Gemini, Claude. Open source (privacy-critical): Llama 3, Qwen.

RAG (Retrieval-Augmented Generation)

Querying over your company documents. Vector DB (pgvector, Pinecone, Weaviate) + embedding (OpenAI, Cohere, Voyage) + reranking + LLM. Critical for accuracy.

Cost and Latency Optimization

Prompt caching, batch API, model fallback, result caching, streaming response. Practices that can reduce monthly AI cost 5-10x.

Security and Privacy

Where does data go? Anthropic/OpenAI don't train models with data (enterprise plan). EU/regional data centers preferred for compliance; on-premise model when privacy critical.

Use Cases

Customer Support Assistant

Answers from company docs via RAG.

Content / Marketing Generation

Blog, product description, social media post.

Document Summarization & Extraction

Contract, report, email summarization.

Category & Tag Automation

Product, content, ticket classification.

Personalized Recommendation

User-specific product/content recommendation.

Technology Stack

Claude API (Anthropic) OpenAI API Gemini API pgvector Pinecone LangChain / LlamaIndex OpenAI Embeddings Cohere

How We Work

  1. Use Case Definition — Which problem are we solving?
  2. Model Selection — Performance/cost/latency.
  3. Prompt Engineering — System prompt, few-shot examples.
  4. RAG / Tool Use — Data source connection.
  5. Test & Eval — LLM eval set, quality measurement.
  6. Launch & Cost Monitoring — Production, observability.

Pricing Approach

Pilot AI feature: $3K - $7K. Production-ready RAG: $7K - $25K. Monthly API costs (Claude/GPT) separate; $50-2.500/month range in practical use.

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Frequently Asked Questions

Claude for reasoning and long context. GPT for open ecosystem and plugin needs. We use both with fallback in most projects.

No, Anthropic and OpenAI don't use API data for training (default). Enterprise/Workspace plans have contractual guarantees.

RAG (answers from real docs), low temperature, validation with tool use, "I don't know" trigger prompt. Human-in-the-loop when hallucination critical.

Prompt + RAG is enough for most use cases. Fine-tune adds value if very specific domain and large data exist.

$5-25/month for small scale of 1K queries. $250-2.5K/month for customer support bot. Contract discounts and 50-80% reduction with cache possible at high volume.

Read the Pillar Guide

For an in-depth article on this topic with cost breakdowns, real examples and 2026 trends, see our pillar guide.

Read Guide →

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