Dr. Rajasekar Venkatesan
Architect of enterprise GenAI at scale. Translating frontier AI into production systems.
Enterprise GenAI, in production.
What it looks like to run enterprise GenAI as a program, not a project.
GenAI Service Layer
When every team needs LLMs, you need infrastructure that turns chaos into reliability. Multi-cloud, multi-vendor, model-agnostic, with built-in routing, caching, observability, cost governance, and policy enforcement across 100+ production use cases.
Agent Service Layer
Agents aren't a feature you ship once. They're a class of system that has to be built, evaluated, validated, and operated. The framework standardises every step. Cuts the full pipeline (development, evals, human validation, load testing, deployment) from 16 weeks to 2 weeks or less.
Multi-agent customer chatbot
Customer-facing production AI handling 85K+ queries per week. CSAT lifted from ~50% to 80%+. A flagship example of what the Agent Service Layer can produce.
- Multi-agent orchestration with specialised sub-agents
- Parallel tool use and agentic delegation
- Human-in-the-loop escalation for high-stakes flows
- Persistent interaction + knowledge memory across turns
- Multi-step transactional workflows
Industry partnerships
Active engagement with industry-leading AI organizations: co-shaping enterprise GenAI roadmaps, evaluating frontier capabilities pre-release, and translating research into production.
Internal engagements
Building enterprise-wide AI fluency through training, upskilling, coaching, and mentoring across peers, engineering teams, business users, and senior leadership including C-suite stakeholders.
DocIQ
Multi-modal, VectorDB-less, agentic RAG for high-accuracy enterprise document Q&A.
Tacit
Local-first knowledge digitisation system capturing tacit expertise into queryable structured knowledge.
Across two eras of AI.
Fourteen years of building. Today's GenAI revolution, anchored in the deep ML foundations it stands on.
Enterprise GenAI architecture
Service layers, multi-cloud routing, model-agnostic design.
Agentic AI systems
Harnesses, tool use, evaluation, idea-to-production velocity.
Cost governance at scale
Caching, batching, tiering across providers.
Voice & multimodal
Beyond legacy STT-text-TTS pipelines.
Transformer training & fine-tuning
End-to-end model training, transfer learning, and parameter-efficient fine-tuning across modalities.
NLP & computer vision
Production NLP and CV pipelines. Classification, extraction, detection, segmentation, and OCR at scale.
Recommender systems
Hybrid recommendation combining collaborative filtering, content-based, and neural approaches.
Traditional ML & more
Tabular learning, time series, clustering, anomaly detection, ensembles, and graph representation learning.
Currently working on.
The initiatives shaping the next chapter of enterprise GenAI at scale.
Agentic systems at scale
Pushing the Agent Service Layer into more departments. New eval harness, new tool primitives, faster idea-to-prod cycles.
Voice AI for internal ops
Native end-to-end streaming voice replacing legacy STT-text-TTS for internal staff workflows. Real-time, low-latency, prosody-aware.
Frontier model partnerships
Pre-release evaluations and roadmap co-shaping with OpenAI, Anthropic, Google, AWS, and Salesforce.
DocIQ + Tacit research
Multi-modal agentic RAG and local-first knowledge digitisation. Both private builds.
Reading & thinking.
A short list of what's shaping how I think this season.
Co-Intelligence
Ethan Mollick on living and working with AI. The clearest framing of human + agent collaboration I've read.
Anthropic Building Effective Agents
The architecture patterns that hold up in production. Routine reading for anyone shipping agents.
Claude Code & Codex
The current state of AI coding agents. Reshaping how software gets written, not just how it's typed.
Open to collaboration, advisory, and speaking.
For partnerships, technical advisory, conference invites, or to compare notes on enterprise AI.