AI/ML Engineering
LLM integration, RAG pipelines, and MLOps infrastructure for production AI.
The problem
You want to integrate AI into your product, but don't know where to start. LLMs are expensive, and production deployment is risky.
Our approach
Discovery: Define use cases, evaluate models (OpenAI, Anthropic, open-source), and estimate costs.
Prototyping: Build POC with RAG (Retrieval-Augmented Generation), vector databases, and prompt engineering.
Deployment: MLOps pipeline with model versioning, monitoring, and cost tracking.
Optimization: Fine-tuning, caching, and rate limiting to reduce latency and cost.
Outcomes
Production LLM integration with monitoring
RAG pipelines with vector databases (Pinecone, Weaviate)
Cost optimization (caching, prompt compression)
Model deployment with CI/CD
Sample deliverables
- LLM integration (API or self-hosted models)
- RAG pipeline with embeddings and retrieval
- MLOps infrastructure (model registry, monitoring)
- Cost tracking dashboard
- Documentation and handover
Timeline
6–12 weeks for initial deployment; ongoing optimization.
Tech stack
FAQs
Do you work with open-source models?
Yes. We deploy LLaMA, Mistral, and others on your infrastructure.
How do you control costs?
Caching, prompt optimization, and rate limiting. We monitor usage in real-time.
Can you fine-tune models?
Absolutely. We handle data prep, training, and evaluation.
Ready to get started?
Book a thirty minute technical scope call. We will review your requirements and respond with a timeframe and estimate.
Request a scope call