AI Deployment Company • NVIDIA Inception Member

Start Your AI Project With Us

Tell us about your AI project — the data, workflows, or business problems you need AI to solve. We'll design and build a custom AI project — RAG pipeline, agentic workflow, or multi-LLM system — and deploy it on AWS.

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In a hurry? Email us directly at info@ondevtra.com

AI Systems We Build
  • RAG Pipelines — Document ingestion, chunking, embedding, pgvector indexing, hybrid search (BM25 + RRF), RAGAS evaluation
  • Agentic AI Workflows — LangGraph state machine agents with tool use, function calling, multi-step reasoning, and autonomous execution
  • Multi-LLM Orchestration — Intelligent routing across OpenAI GPT-4o, Claude (Bedrock), Gemini — semantic caching, token budgets, automatic fallback
  • MCP Servers & A2A — Model Context Protocol servers exposing domain tools to AI agents, Agent-to-Agent integration
  • AI Guardrails & Safety — Hallucination detection, domain accuracy validation, output format checking, cost routing
  • Document AI & Search — Turn manuals, specs, reports into intelligent Q&A systems with cited, accurate answers
  • AI Chatbots & Assistants — Conversational agents grounded in your data — customer support, internal knowledge, technical docs
  • LangSmith Observability — Tracing, evaluation, monitoring for every agent run in production
  • AWS AI Deployment — Docker, Terraform IaC, ECS Fargate, RDS + pgvector, CloudWatch dashboards, zero-downtime deployment
  • iOS & Web Apps — AI-powered apps with production multi-provider LLM backends (1M+ users shipped)
Shipped: Multi-LLM AI Platform
1M+ Users — 5-Provider LLM Orchestration

A production SaaS platform routing across OpenAI, Azure Speech, Google Cloud TTS, AWS Polly, and ElevenLabs. Intelligent provider selection, automatic fallback, 200+ AI voices in 45+ languages — deployed on AWS ECS Fargate with Terraform.

  • Multi-LLM routing with automatic fallback & cost tracking
  • Async render pipeline on ECS Fargate (zero cold starts)
  • React 19 / TypeScript / Supabase / AWS infrastructure
  • 1M+ users, 4.6★ rating, #1 in Europe

Stack: React 19, TypeScript, Supabase, OpenAI, Azure, AWS ECS, Terraform · Live in production

Built: RAG Agent for Technical Docs
Agentic Q&A Over Engineering Documentation

A LangGraph agentic RAG system over technical documentation and timing reports. Hybrid search (pgvector + BM25 + RRF), cited answers, hallucination detection, and RAGAS evaluation — deployed with Docker on AWS.

  • LangGraph state machine with tool use & multi-step reasoning
  • Hybrid search: pgvector embeddings + BM25 keyword + RRF fusion
  • Three-layer guardrails: hallucination, domain accuracy, format
  • RAGAS evaluation suite + LangSmith observability

Stack: Python, LangGraph, LangChain, pgvector, FastAPI, Docker, Terraform · Demo available

Built: MCP Server & Agent Orchestration
Domain-Specific AI Tool Server

A FastMCP server exposing domain-specific search and analysis tools to AI agents. Includes A2A (Agent-to-Agent) integration, cost-optimised model routing (GPT-4o-mini for simple, GPT-4o for complex), and semantic caching.

  • FastMCP server with registered domain tools
  • A2A Agent Card for multi-agent discovery
  • Cost router: model selection + semantic cache (30-40% hit rate)
  • Token budget management & per-request cost tracking

Stack: FastMCP, FastAPI, LangGraph, OpenAI, Pydantic · Production-ready

Common Questions
Simple automation tools and order pages: 1–2 weeks. Mid-size web apps: 4–8 weeks. Larger systems with AI integrations: 2–3 months. We'll give a realistic estimate after reviewing your brief.

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info@ondevtra.com