Back to Services
Enterprise Practice

Custom AI Development

Train domain-specific private models, build high-performance vector retrieval architectures, and deploy secure inference API microservices. This is true engineering-led product delivery — not prototyping.

Discuss Your Custom AI Build

Key Outcomes

  • Launch proprietary AI products
  • Maintain complete IP ownership and data privacy
  • Deploy scalable, production-grade microservices
  • Outperform generic models with fine-tuned accuracy

Deliverables

  • Fine-tuned LLMs (PEFT/LoRA)
  • Enterprise RAG Systems
  • Secure Inference APIs
  • Agent Frameworks (LangGraph)
  • Dockerised AI Microservices

Engagement Methodology

1

Scoping

Architecture design, data requirements, and feasibility review.

2

Data Prep

Dataset curation, preprocessing, and expert annotation.

3

Engineering

Model training, RAG pipeline construction, and rigorous evaluation.

4

Deployment

Kubernetes/Docker deployment, API wrapping, and 90-day SLA.

Engagement Profile

Typical Timeline12 - 24 Weeks
TeamFull-Stack AI Team
Best ForProduct companies needing proprietary AI features or enterprises requiring absolute data privacy.