Hey, I'm Ajayaditya Lokchandra.
Coimbatore, India
I build AI systems that survive real users, real traffic, and real constraints. Enterprise RAG platforms · hybrid retrieval · reranking · LLM guardrails · ingestion pipelines · observability — designed end-to-end, deployed, and running in production. Not prototypes. Not demos. Systems that hold under load.
I think in reliability, scale envelopes, and latency budgets — so your AI product works today and still works at 10× growth.
Nucleus AI | Enterprise Knowledge RAG Platform
Nucleus AI is a production-grade, multi-service RAG architecture engineered for answer trust, cost efficiency, and long-term scale.
• Intent routing (KB vs Web) — reduces unnecessary vector search and retrieval overhead
• Hybrid retrieval + cross-encoder reranking — broad recall, precise ranking
• NLI contradiction filtering — blocks unreliable answers before they reach users
• Delta-based reingestion via state table + cron — batched updates, isolated failures, consistent pipelines
• Stage-level telemetry across services — observable latency and quality chain
• ~10s latency by design — prioritizes correctness and verification over raw speed
Built as isolated services, not chained prompts. Designed for real data, real users, and real operational constraints.
Neuronote | Your Second Brain, Reimagined
LIVE | Try Now!
Neuronote is an AI-native structured knowledge system that converts raw thought into typed, queryable data without manual organization or excess compute.
• JSON-constrained extraction pipeline — converts raw input into typed, atomic knowledge objects with structured metadata
• Embedding-backed semantic indexing — enables vector recall across notes while preserving structured fields
• Client-side hybrid retrieval (vector + lexical boosts) — combines cosine similarity with tag/type matching for precision
• Local-first architecture + process-triggered sync — chunks processed in-browser and synced only after structured updates, reducing API calls by ~50%
Built as a modular AI pipeline inside a full-stack system balancing semantic intelligence, cost control, and offline-aware continuity.
Quirk | GitHub Automation for Product Teams
LIVE | Try Now!
Quirk is a graph-based automation engine that turns GitHub events into structured execution across Slack and Asana — with traceable, extensible workflow orchestration.
• Graph-based execution engine — workflows modeled as node/edge graphs with condition gating and dynamic variable interpolation
• Webhook-driven orchestration — GitHub events mapped to published workflows with ownership + credit enforcement
• Channel adapters (Slack / Asana) — isolated integration handlers for extensibility across providers
• Structured execution logging — per-stage log records for debugging, analytics, and reliability monitoring
• Extensible architecture — orchestration layer separated from adapters and persistence, enabling new trigger/action providers
Designed as an automation engine with clear pathways for signature verification, idempotent retries, and async job queues for enterprise hardening.
Nucleus AI | Enterprise Knowledge RAG Platform
Nucleus AI is a production-grade, multi-service RAG architecture engineered for answer trust, cost efficiency, and long-term scale.
• Intent routing (KB vs Web) — reduces unnecessary vector search and retrieval overhead
• Hybrid retrieval + cross-encoder reranking — broad recall, precise ranking
• NLI contradiction filtering — blocks unreliable answers before they reach users
• Delta-based reingestion via state table + cron — batched updates, isolated failures, consistent pipelines
• Stage-level telemetry across services — observable latency and quality chain
• ~10s latency by design — prioritizes correctness and verification over raw speed
Built as isolated services, not chained prompts. Designed for real data, real users, and real operational constraints.
Neuronote | Your Second Brain, Reimagined
LIVE | Try Now!
Neuronote is an AI-native structured knowledge system that converts raw thought into typed, queryable data without manual organization or excess compute.
• JSON-constrained extraction pipeline — converts raw input into typed, atomic knowledge objects with structured metadata
• Embedding-backed semantic indexing — enables vector recall across notes while preserving structured fields
• Client-side hybrid retrieval (vector + lexical boosts) — combines cosine similarity with tag/type matching for precision
• Local-first architecture + process-triggered sync — chunks processed in-browser and synced only after structured updates, reducing API calls by ~50%
Built as a modular AI pipeline inside a full-stack system balancing semantic intelligence, cost control, and offline-aware continuity.
Quirk | GitHub Automation for Product Teams
LIVE | Try Now!
Quirk is a graph-based automation engine that turns GitHub events into structured execution across Slack and Asana — with traceable, extensible workflow orchestration.
• Graph-based execution engine — workflows modeled as node/edge graphs with condition gating and dynamic variable interpolation
• Webhook-driven orchestration — GitHub events mapped to published workflows with ownership + credit enforcement
• Channel adapters (Slack / Asana) — isolated integration handlers for extensibility across providers
• Structured execution logging — per-stage log records for debugging, analytics, and reliability monitoring
• Extensible architecture — orchestration layer separated from adapters and persistence, enabling new trigger/action providers
Designed as an automation engine with clear pathways for signature verification, idempotent retries, async job queues, and encrypted token policies for enterprise hardening.