Generative AI

GenAI & LLM Integration

Add generative AI capabilities to your existing product. RAG pipelines, fine-tuning, prompt engineering, and custom AI features that drive real business value.

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AI-First Engineering
NVIDIA / Google Partners
Enterprise-Ready
Services

Integration Services

RAG Pipelines
Connect LLMs to your proprietary data. We build retrieval pipelines that ground AI responses in your documents, databases, and knowledge bases — eliminating hallucinations.
Fine-Tuning
Customize foundation models for your specific domain. We handle data preparation, training, evaluation, and deployment of fine-tuned models that speak your language.
AI-Powered Features
Add intelligent search, summarization, content generation, classification, and extraction capabilities directly into your existing product workflow.
Prompt Engineering
Optimize prompts for accuracy, consistency, and cost. We design prompt architectures with evaluation frameworks that ensure reliable, production-quality outputs.
Applications

Use Cases

Document Q&A
Automated Content Generation
Intelligent Search
Code Assistance
Customer Insights
Personalization Engines
Approach

Our Approach

1
AI Readiness Audit
Week 1
Assess your data, infrastructure, and use cases. Identify the highest-impact opportunities for AI integration.
2
Proof of Concept
Weeks 2–4
Build a working prototype with your real data. Measure accuracy, latency, and cost to validate the approach before committing.
3
Production Integration
Weeks 5+
Scale from POC to production with proper error handling, monitoring, caching, and cost optimization built in.
Enterprise

Enterprise Considerations

Data privacy & security
Your data never trains third-party models. We use enterprise API agreements, data encryption, and can deploy on-premise when required.
Cost optimization
Smart caching, model routing, and prompt optimization to keep your AI costs predictable and sustainable at scale.
Latency & performance
Streaming responses, async processing, and edge caching ensure your AI features feel instant, not sluggish.
Model evaluation & selection
Systematic benchmarking across providers and models to find the best accuracy-cost-speed tradeoff for your specific use case.
FAQ

Frequently Asked Questions

01.
What is RAG?

RAG (Retrieval-Augmented Generation) connects a large language model to your proprietary data. Instead of relying solely on training data, RAG retrieves relevant documents from your knowledge base and uses them to generate accurate, grounded responses specific to your business.

02.
Should I fine-tune or use RAG?

RAG is best when you need the model to reference specific, frequently updated data. Fine-tuning is better when you need the model to adopt a specific tone or domain expertise. Many production systems use both. We help you evaluate which approach fits your use case and budget.

03.
How much does LLM integration cost?

LLM integration costs range from $10,000 for a basic RAG pipeline to $100,000+ for complex enterprise deployments with fine-tuning and multi-system integration. A proof of concept typically runs $10K-$25K and takes 2-4 weeks.

04.
Will my data be used to train the model?

No. We use enterprise API agreements that explicitly prohibit training on your data. For maximum control, we can deploy open-source models on your own infrastructure.

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Your data is your competitive advantage. Let us help you unlock it with AI that’s accurate, secure, and production-ready.

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