Add AI capabilities to your applications
Integrate large language models into your products with proper architecture, prompt engineering, and production-grade reliability.
Why LLM integration?
- Add AI capabilities to existing applications
- Choose the right model for cost, latency, and quality
- Build robust error handling and fallback strategies
- Implement proper prompt engineering from day one
- Set up evaluation pipelines to measure quality
- Design for scale: caching, batching, rate limiting
Deliverables
API Integration
Connect your applications to OpenAI, Anthropic, Azure OpenAI, or self-hosted models. Proper error handling, retries, and monitoring included.
Prompt Engineering
Design prompt templates that produce consistent, high-quality outputs. Structured outputs, few-shot examples, and chain-of-thought patterns.
Production Deployment
Deploy LLM-powered features with caching, rate limiting, cost monitoring, and quality evaluation. Built for reliability at scale.
Models supported
OpenAI GPT-4
Best overall quality, function calling
Anthropic Claude
Long context, safety, reasoning
GPT-4o Mini
Cost-effective for high volume
Llama 3
Self-hosted, data privacy
Mistral
European hosting, good performance
Azure OpenAI
Enterprise compliance, SLAs
When to talk
You added OpenAI or Claude, but quality is inconsistent
The problem is often missing evals, weak output contracts, no trace review, or no fallback path when a model changes behavior.
API costs are rising without a clear owner
Model routing, caching, batching, prompt size, and observability usually matter more than switching providers blindly.
You need to integrate LLMs into a product, not a demo
Production integration means permissions, data boundaries, user feedback, monitoring, incident paths, and a way to measure quality over time.
Common questions
Which LLM should I use for my project?
It depends on priorities. GPT-4 for quality, Claude for long documents and reasoning, GPT-4o-mini for cost optimization, Llama/Mistral for data privacy. Trade-offs are evaluated and the right model chosen for each use case.
How do you handle API costs?
Cost optimization is built into every integration: response caching, prompt optimization, model selection by task complexity, and batching where possible. Monitoring is set up so spend can be tracked by feature and user.
Can you help with existing LLM implementations that aren't working well?
Yes. Existing implementations are audited, issues identified (usually prompt design, lack of evaluation, or architectural problems), and fixed. Small changes to prompts and architecture often lead to significant quality improvements.
Do you work with open-source models?
Yes. For data privacy requirements or high-volume use cases, solutions are implemented using Llama, Mistral, or other open-source models. Self-hosted or via providers like Together, Groq, or Fireworks.