Build AI That Ships to Production

AI Agent Development

From prompts to production: build agents with tool use, MCP integrations, and evaluation—deployed with guardrails, human-in-the-loop controls, and observability.

• —LLM APIs, tool calling, and the Model Context Protocol (MCP)
• —LangGraph, CrewAI, and custom orchestration patterns
• —Evaluation harnesses, guardrails, and safety testing
• —Integration patterns for CRM, booking, and helpdesk systems

RAG Pipeline Development

Build retrieval pipelines that are accurate and auditable—ingestion, hybrid search, re-ranking, and evaluation against real question sets.

• —Layout-aware ingestion, chunking, and metadata design
• —Hybrid (vector + keyword) search and re-ranking
• —Citations, grounding checks, and access control
• —Evaluation with Ragas/DeepEval-style golden question sets

Conversational AI Development

Ship WhatsApp, web, and voice assistants that transact through real APIs and hand off cleanly to a human—in English, Hindi, and Hinglish.

• —WhatsApp Business Platform (Meta Cloud API) integration
• —Voice AI: speech-to-text, text-to-speech, and call transfer
• —Multilingual NLU including Hindi and Hinglish
• —Human hand-off design and containment-rate analytics

Forward Deployed Engineering

Learn to work the way our FDEs do: immersion in a real workflow, a working prototype fast, and production hardening inside a client’s own environment.

• —Workflow shadowing and use-case discovery
• —Shipping inside client repos, CI/CD, and security policy
• —Integration with legacy systems that have no clean API
• —Weekly-demo delivery cadence and adoption tracking

Custom & Specialized AI Models

Fine-tune and distil models for a real task—from feasibility benchmarking through evaluation and serving optimisation.

• —LoRA/QLoRA fine-tuning and DPO preference tuning
• —Distillation for smaller, faster, cheaper models
• —Held-out evaluation, bias checks, and payback modelling
• —Serving with vLLM/TGI and post-launch drift monitoring

AI Models on Cloud

Deploy generative AI on AWS Bedrock, Microsoft Foundry, and Vertex AI—with infrastructure as code, guardrails, and cost control from day one.

• —Bedrock, Foundry, and Vertex AI architecture patterns
• —Terraform/IaC and CI/CD for prompts and models
• —Guardrails, PII redaction, and responsible-AI controls
• —Cost optimisation: caching, batch inference, model routing

Engineering

Build production web applications and AI chat interfaces with React, Node.js, and real-time streaming—wired to agent and RAG backends.

• —React + TypeScript UI patterns and state management
• —Node.js or FastAPI backends with REST and GraphQL APIs
• —WebSocket and SSE for real-time AI chat streaming
• —Deploying to cloud with Docker and CI/CD pipelines