AI & AIOps
Take your AI from prototype to production.
We build the infrastructure, retrieval pipelines, deployment and security controls that let generative AI features run reliably, safely and at a sensible cost on AWS, Azure or Google Cloud.
- AWS Advanced Tier Services Partner
- ISO 27001 certified
In short
VA2PT's AI Engineering Services help companies run generative AI in production on AWS, Microsoft Azure and Google Cloud. We build LLM application infrastructure on Amazon Bedrock, Azure OpenAI or Vertex AI, retrieval-augmented generation (RAG) pipelines, deploy and scale models, assess LLM applications against the OWASP Top 10 for LLM Applications, and put cost governance in place for tokens and GPUs.
Sound familiar?
Demos that do not survive production
A prototype that works in a notebook often fails on latency, scale, monitoring and reliability once real users arrive.
New attack surface
Prompt injection, data leakage and insecure tool use are real risks that traditional security testing does not cover.
AI costs that nobody forecast
Token usage and GPU capacity can grow faster than revenue without clear limits and visibility.
What we do
The work inside AI Engineering Services, run by senior engineers.
LLM application infrastructure
We design and build the serving, API, caching, observability and scaling layers for LLM features, on Amazon Bedrock, Azure OpenAI, Vertex AI or self-hosted models.
RAG pipelines
We build ingestion, chunking, embedding and vector search pipelines that ground model answers in your own data with access controls.
Model deployment and MLOps
We set up model deployment on Amazon SageMaker, Azure Machine Learning, Vertex AI or Kubernetes with versioning, evaluation, rollout and monitoring.
AI security assessment
We test LLM applications against the OWASP Top 10 for LLM Applications, including prompt injection, sensitive data disclosure and excessive agency.
Guardrails and data protection
We add input and output filtering, PII handling and policy guardrails so AI features behave within agreed limits.
AI cost governance
We track token and GPU spend per feature and team, set budgets and apply caching, model routing and right-sizing.
How the engagement runs
- 1
Discover the use case
We review your AI feature, data sources, users, risks and success criteria.
- 2
Architect
We design the model, retrieval, serving, security and cost approach and agree on evaluation metrics.
- 3
Build and secure
We implement infrastructure and pipelines as code, and run security testing before launch.
- 4
Operate and improve
We monitor quality, latency, cost and security in production and iterate with your team.
What you get
- GenAI reference architecture for your use case
- RAG ingestion and retrieval pipeline
- Model deployment pipeline with evaluation
- OWASP LLM Top 10 security assessment report
- Guardrail and data-handling configuration
- AI cost dashboard with budgets and alerts
Tools we work with
- Amazon Bedrock
- Azure OpenAI
- Vertex AI
- Amazon SageMaker
- Amazon OpenSearch Service
- pgvector
- LangChain
- LlamaIndex
- Kubernetes
- vLLM
- Terraform
- OpenTelemetry
Questions buyers ask
What are AI engineering services?
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.
What is a RAG pipeline?
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.
What does an LLM security assessment cover?
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.
Should we use a managed model service or host our own models?
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.
How do you keep AI costs under control?
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.
Talk to us about AI Engineering Services.
A 30-minute call with a senior engineer. We look at your setup, name the biggest risks and outline what we would do first.