Take your AI from prototype to production.

AI engineering covers everything needed to run AI features reliably in production beyond the model itself: infrastructure, data and retrieval pipelines, deployment, monitoring, security and cost control. VA2PT focuses on this production side, helping teams turn working prototypes into GenAI features that are scalable, observable, secure and affordable on AWS, Azure or Google Cloud.

Retrieval-augmented generation (RAG) lets a language model answer using your own documents and data rather than only its training. A pipeline ingests content, splits it into chunks, turns them into embeddings and stores them in a vector index. At query time, the most relevant chunks are retrieved and passed to the model, which improves accuracy and allows answers to cite sources.

We test your LLM application against the OWASP Top 10 for LLM Applications. That includes prompt injection, sensitive information disclosure, insecure output handling, excessive agency in tools and agents, data and model poisoning risks, and unbounded consumption. You receive findings with severity and practical fixes, similar to our VAPT reports, and we re-test once fixes are in place.

It depends on your requirements. Amazon Bedrock, Azure OpenAI and Vertex AI give managed access to foundation models with no GPU management, which suits most teams, and the choice between them usually follows the cloud you already run on. Self-hosting on SageMaker, Azure Machine Learning, Vertex AI or Kubernetes can make sense for specific open models, strict data residency or very high, steady volumes. We compare cost, latency, control and operational effort for your case.

We measure token and GPU usage per feature and team, then apply budgets and alerts. Common savings come from caching repeated requests, routing simple tasks to smaller models, trimming prompts and retrieved context, batching, and right-sizing or scheduling GPU capacity. Cost dashboards make spend visible alongside quality metrics so trade-offs are clear.