AI Systems Architect

Your Team's Knowledge is Trapped in Thousands of Documents. I Build AI Systems That Unlock It.

Production-grade RAG systems for enterprises with sprawling documentation - internal wikis, compliance archives, technical manuals, and customer knowledge bases. No hallucinations. Full citations. Your data stays yours.

6+
Years Building
4
Products Launched
20+
AI Systems Shipped
5+
Enterprise Clients
What I Build

Expertise That Drives Real Outcomes

01

Enterprise RAG Pipelines

Hybrid search, smart chunking, reranking, and citation-backed answers across SharePoint, Confluence, Drive, databases, and file systems.

02

Multi-Agent Knowledge Workflows

LangGraph agents that plan, retrieve, reason, and self-correct across multi-step questions - not just single-shot lookups.

03

Production Deployment & Security

Full-stack implementation on AWS with role-based access, audit logs, monitoring, and data that never leaves your cloud.

04

RAG System Audits

Deep evaluation of existing RAG or LLM systems. Identify hallucination sources, retrieval failures, and architecture gaps.

Products

Built, Launched & Scaling

NirixAI

Production RAG System

Production RAG system converting unstructured video and document content into searchable, cited Q&A with real-time streaming responses. Built on Pinecone with hybrid search, multi-LLM routing, and SSE streaming. Demonstrates the full ingestion -> embedding -> retrieval -> generation pipeline applied to messy, multi-format content at interactive latency.

Serves streaming answers from a 20K+ content corpus with sub-2-second first-token latency.

LangGraph RAGHybrid SearchPineconeSSE Streaming

PiperQL

Natural-Language Database Agent

Natural-language database agent that lets non-technical teams (ops, finance, HR) self-serve complex database queries without writing SQL. Handles multi-turn conversation context, auto-generates appropriate visualizations, and runs in three deployment modes. The same architecture pattern applied inside an enterprise eliminates the analyst bottleneck for routine reporting queries.

Open-sourced under AGPL v3. Architecture scales to enterprise-grade query workloads.

LangGraph + OpenAINatural Language to SQLOpen Source

Tim Edge

High-Concurrency Trading Platform

Full-stack trading platform with live market data streaming, automated trade logic, journaling, and backtesting. Architected with MetaAPI integration, WebSocket streaming, Celery async workers, and AWS infrastructure. Demonstrates high-concurrency, low-latency system design - the same patterns required for real-time enterprise retrieval systems.

MetaAPIWebSocketCelery WorkersAWS

Tim Academy

End-to-End Product Delivery

Full-stack education platform with live streaming (100ms), authentication, payments, and structured learning paths. Built on Firebase with a premium landing experience. Demonstrates end-to-end product delivery - design, implementation, infrastructure, and launch.

FirebaseLive StreamingPaymentsAuth
About Me

Engineering the
Agentic Shift

I'm an AI Systems Architect. My work focuses on helping organizations unlock the knowledge trapped in their sprawling documentation - wikis, compliance archives, technical manuals, customer knowledge bases - with retrieval systems that cite their sources and don't hallucinate.

I provide architectural leadership for production-grade RAG: hybrid search, reranking, citation-backed answers, LangGraph agents for multi-step retrieval, and AWS deployments with role-based access and audit logs.

Currently working as a Generative AI Engineer at AION Soft.

Philosophy
Architecture-first over prompt hacking
Stateful, self-correcting agent loops
Prototype to Production to Scale
Enterprise-grade vector intelligence
High-concurrency production systems
Validate before you execute
How I Engage

Standards I Ship To

Six non-negotiables every engagement holds to - whether it's a 2-week audit or a 4-month platform build.

01

Architecture-first over prompt hacking

Every engagement starts with a retrieval architecture, not a prompt. Prompts are the last mile, not the first one.

02

Hybrid retrieval, never pure vector

BM25 + dense vectors + reranking, tuned to your corpus. Pure vector search ships demo-grade answers, not production ones.

03

Citations on every answer

Every response links back to the exact document and section. "I don’t know" is a valid answer. Hallucinations are a bug, not a quirk.

04

Production infrastructure, not notebooks

Monitoring, logging, alerting, auto-scaling, audit trails, and runbooks. Every deliverable is something your on-call can operate.

05

Your data stays in your cloud

Self-hosted, enterprise-API with zero data retention, or hybrid - chosen during the audit based on your compliance posture.

06

Handover, not dependency

Every engagement ends with your team able to operate and extend the system without me. No seat-licensing, no vendor lock.

Core stack
LangGraph·Pinecone·FastAPI·AWS·Claude·OpenAI·Next.js
Voices

What Clients Say

Ajay architected our agent workflow in 3 weeks - what our internal team had stalled on for 3 months. He thinks in systems, not prompts.
- CTO, B2B SaaS · 40 employees
We needed a RAG system that wouldn’t hallucinate on regulatory content. Ajay delivered one with full citations and 94% accuracy. Our compliance team finally trusts AI output.
- Head of Compliance, Fintech
Clear communicator. Ships production code, not demos. The architecture decisions he made early saved us from a 6-month rewrite later.
- Engineering Manager, Enterprise SaaS

Ready to Build Your
AI System?

Let's discuss your architecture, identify bottlenecks, and create a concrete plan. No sales pitch - just honest, actionable direction.