AI/ML Engineering
LLM integration, RAG pipelines, and MLOps infrastructure for production AI.
Diagnosis
What we fix first
You want to integrate AI into your product, but don't know where to start. LLMs are expensive, and production deployment is risky.
Platform blueprint
From messy inputs to trusted decisions
Frame
Define the use case, success metrics, and data readiness up front.
Retrieve
Build vector search and grounded retrieval pipelines over your data.
Deploy
Serve models with caching, fallbacks, and safety guardrails.
Evaluate
Offline evals, monitoring, and per-request cost and latency tracking.
Delivery plan
How the work moves
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.
Results
Outcomes your team can measure
Production LLM integration with monitoring
RAG pipelines with vector databases (Pinecone, Weaviate)
Cost optimization (caching, prompt compression)
Model deployment with CI/CD
Deliverables
What you receive
- LLM integration (API or self-hosted models)
- RAG pipeline with embeddings and retrieval
- MLOps infrastructure (model registry, monitoring)
- Cost tracking dashboard
- Documentation and handover
FAQs
Practical questions
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.