Years Enterprise Experience
Average Cloud Cost Reduction
Audit & Roadmap Delivery
AI/ML Development
We Build the AI — Not Just the Strategy for It
Important distinction: Our AI Transformation Consulting advisory service produces your AI roadmap — the forensic assessment of where AI will deliver the highest ROI in your business, the 90-day implementation plan, the vendor-neutral platform recommendations and the board-ready investment case. Our AI/ML Development service executes that roadmap. If you already know what you want to build, we build it. If you need both the strategy and the build, we deliver both — with the advisory team and the engineering team working from the same assessment.
Vrintra Labs engineers applied AI and ML products — working software that integrates language models, computer vision, predictive models and recommendation systems into your product, your workflows and your data infrastructure. We build LLM-powered features using OpenAI, Anthropic, Google Gemini and open-source models. We build retrieval-augmented generation (RAG) systems that ground model outputs in your proprietary data. We fine-tune foundation models on your domain-specific datasets. We build ML pipelines that ingest, process and serve model predictions at production scale — with monitoring for drift and performance degradation built in from day one.
Every AI system we build is production-grade, not a demo. It has logging, monitoring, fallback handling, rate limit management and latency optimisation. It integrates with your existing application stack via clean APIs. It has the observability infrastructure your engineering team needs to understand what the model is doing in production. And where your business operates in a regulated industry, it is designed to meet the governance requirements of our AI Governance & Compliance advisory service.
What You Get
- LLM integration and feature development — OpenAI GPT-4o, Anthropic Claude, Google Gemini, Mistral and open-source models via Hugging Face and Ollama
- RAG system development — vector database design (Pinecone, Weaviate, pgvector), embedding pipelines, retrieval optimisation and hallucination reduction
- Custom model fine-tuning — domain adaptation, instruction tuning and RLHF on your proprietary datasets with evaluation framework
- Agentic AI implementation — building the agents designed in our Agentic AI Architecture advisory engagement using LangChain, CrewAI and AutoGen
- ML pipeline engineering — data ingestion, feature engineering, model training orchestration (Airflow, Prefect), model registry and serving infrastructure
- Computer vision and NLP models — object detection, document processing, classification, entity extraction and multimodal AI applications
- AI-powered product features — intelligent search, personalisation engines, predictive analytics, anomaly detection and automated decision systems
- Model monitoring and MLOps — drift detection, performance alerting, A/B testing infrastructure and automated retraining pipelines
Who This Is For
Product teams that have a defined AI feature to build and need senior ML engineers to build it. Businesses that have completed our AI Transformation Consulting advisory and now need the engineering team to execute the roadmap. Companies that have tried to build AI features internally but have hit the wall on production reliability, latency, cost and hallucination rate. Engineering teams that need senior ML engineering capacity augmented into their sprint cadence.
Frequently Asked Questions
Everything you need to know about our AI/ML Development service.
AI Transformation Consulting is the advisory engagement — we assess your business, identify where AI will deliver the highest ROI, design the implementation roadmap and produce the board-ready investment case. AI/ML Development is the engineering engagement — we build the AI systems identified in that roadmap, or any other AI feature your team has already scoped. Some clients do both: the advisory engagement first to prioritise correctly, then the development engagement to execute. Others come to us with a defined AI feature already scoped and engage directly for development.
Retrieval-augmented generation (RAG) is an architecture that grounds LLM responses in your specific proprietary data rather than relying solely on the model's training knowledge. You need RAG when your AI feature must answer questions from your internal documentation, product data, customer records or domain knowledge base — and the answers must be accurate and traceable to specific sources, not generated from general model knowledge which may be incorrect or outdated. We design and build complete RAG systems including the document processing pipeline, embedding generation, vector store selection and retrieval optimisation.
Yes. We handle the complete fine-tuning pipeline: dataset preparation and cleaning, training configuration, fine-tuning execution (LoRA/QLoRA for cost-efficient fine-tuning of large models), evaluation against your specific use case and deployment to your private inference endpoint. All fine-tuning happens in your private environment — your proprietary data never leaves your sovereign infrastructure and is never used to train any public model.
We engineer reliability at multiple layers. Retrieval layer: structured retrieval with source attribution forces the model to ground answers in retrieved documents. Prompt layer: system prompt engineering and output schema enforcement reduce off-task generation. Validation layer: deterministic validation checks on model outputs before they reach your application. Monitoring layer: production logging of all model inputs and outputs with human review sampling to catch systematic failures early. No AI system is hallucination-free, but these layers reduce unreliable outputs to a manageable and measurable rate.
Data sovereignty is non-negotiable. All development and fine-tuning uses private API endpoints with zero-retention data processing agreements. We configure all LLM integrations with your data to use enterprise-grade endpoints (Azure OpenAI, Amazon Bedrock, Google Vertex AI) that process your data without retaining it for training. For maximum sovereignty, we can deploy open-source models to your own private cloud infrastructure with zero third-party data transmission at all.