About
Sivaram A
AI Product Strategy & Solution Architecture · ML · DL · GenAI · Vision · Responsible AI & AI Security
Founder, Phygitalytics · 0 to 1 Product Builder · 2 Patents · Ex-Microsoft · Ex-Amazon · IIT Hyderabad
AI product and solution architecture leader with 23+ years of experience turning data and domain insight into production AI. Blends hands-on product management (0 to 1 discovery, MVP, roadmap, and pitch) with end-to-end solution architecture across GenAI, machine learning, deep learning, and computer vision. Has built 5+ GenAI products from zero to one across FinTech, Retail, ESG, Real Estate, Fashion, and Industrial domains, with roughly 85% reaching production.
Partners with founders, CTOs, and product teams to align data readiness, AI capability, and business outcomes, governed by Responsible AI and AI security. Two granted patents in retail computer vision, originator of the CAL (Consistency, Accuracy, Latency) delivery framework, and faculty to 2,000+ practitioners across 64+ countries.
“Growth is not measured by how far ahead you are of others, but by how deeply you understand yourself and how intentionally you shape your journey. Keep going, keep learning.”
Education
- M.S., Data Science, Indian Institute of Technology Hyderabad (2015 to 2017)
- B.E., Computer Science & Engineering, Bharathiar University (1999 to 2003)
Contact
- Greater Bengaluru Area
- sivaram [at] phygitalytics [dot] com
- linkedin.com/in/siva-ak
- github.com/siva2k16
Awards & recognition
- Phygitalytics input published in the UN Global Dialogue on AI Governance (Asia & Pacific, Private Sector, Respondent 665)
- Microsoft Gold Star award (top 1% across all divisions)
- Tyco Living the Values (2012) and Spot Award (2011)
- Data science hackathon winner: TRS Global (2018) and Affine Analytics (2016)
- Top 5% on Stack Exchange for Software Quality Assurance
Selected highlights
Measurable outcomes, not just titles
- Served as Fractional AI Product Manager and Solution Architect for a GenAI procurement co-pilot, owning discovery, MVP, custom LLM fine-tuning and NER, market analysis, and VC and customer pitch.
- Drove a 15% lift on product detail pages through GenAI content enrichment (The Container Store) and a 100% match on multipack bundle recommendations (The Children’s Place).
- Raised ESG metric-extraction accuracy from 30% to 80%+ and shipped a full ESG platform with human-in-the-loop validation, automated insights, and NL2SQL chat (SAM Corporate).
- Co-inventor on 2 granted patents in retail computer vision, with additional filings across video analytics, IoT alerting, and shopper behavior analytics.
- One of two key architects of the data-driven warranty engine for 220M Xbox consoles, cutting warranty errors for 90% of consoles and improving SQL performance by 200% (Microsoft).
Want outcomes like these on your own product or platform?
Book a free call →Core competencies
AI Product Management & Strategy
0 to 1 discovery, MVP, roadmap, prioritization, pitch, and build-versus-buy decisions.
Solution Architecture
Enterprise GenAI and agentic systems, data and AI pipelines, RAG, NL2SQL and Txt2SQL.
Machine Learning & Deep Learning
Forecasting, recommendations, NLP, NER, model fine-tuning, and MLOps.
Computer Vision
Detection, tracking, classification, re-identification, OCR, and multimodal vision.
Responsible AI & AI Security
Guardrails, red-teaming, bias and hallucination audits, NIST AI RMF, and HIPAA alignment.
Domain Depth
Retail, Supply Chain, FMCG, E-commerce, Fashion, ESG, Real Estate, FinTech, and Insurance.
Technical expertise
GenAI & LLMs
OpenAI / GPT-4, Claude, Gemini multimodal, RAG, vector databases, Azure AI Search, NL2SQL and Txt2SQL, agentic frameworks, prompt and evaluation design.
ML & Deep Learning
TensorFlow, Keras, PyTorch, scikit-learn; forecasting, recommendations (user and item, Apriori, clustering), NER, fine-tuning.
Computer Vision
YOLO, OpenCV, OpenVINO, Dlib; object detection and tracking, multi-label classification, landmark detection, face re-identification, OCR, emotion analytics.
Responsible AI Tooling
Giskard, PyRIT, Promptfoo, Guardrails-AI, LLM Guard.
Cloud & Data
Azure, AWS, GCP; Docker, Kubernetes; SQL Server, MongoDB, Neo4j; Spark, Hadoop; Python, SQL; PowerBI, Tableau.
Track record
Customer accounts Sivaram has delivered for
Recognize your industry above? Let's discuss what a similar engagement could look like for you.
Book a free call →Teaching & thought leadership
Executive-education and edtech faculty and SME reaching 2,000+ learners across 64 countries through 178+ sessions and 350+ training hours.
The 23+ year journey
From enterprise foundations to the GenAI frontier
In their own words
Perspectives shared publicly on AI, data, and learning
Building a 360-degree perspective
A product person needs range across API design, data science, and data analytics; a read on consumer and market trends; the judgment to spot what blocks a working version one; and coding skills sharp enough to apply best practices at scale. No function works in isolation, so working directly across product, dev, support, and PM is what actually builds collaboration.
Historical data is the essence of ML
A decade ago, an archival job quietly deleted transaction records older than a year to keep database performance up. That same historical data is now the foundation ML models are built on. Historical data matters as much as live transactional data: every record is useful once you apply the right lens, real-time, BI, AI, or customer.
Getting the data right the first time
Data skills for BI and ML mean finding relevant data outside your regular transactional systems, and finding ways to map that data into features that represent the underlying variable, trend, or pattern. Putting all relevant data in on the first pass beats iteratively patching every missing field later.
Connected learning across old and new tech
We often overrate what we don’t know: the fundamentals stay the same. Spark keeps frequently used objects in memory the same way SQL Server uses its buffer pool, cutting I/O and speeding up joins and aggregates, in contrast to a Hadoop-based architecture that writes to disk between every step. Every new concept maps to an advancement on, or a limitation of, something that already existed.
Domain first, then data
If you don’t understand your domain, you won’t understand your data, and you’ll miss the insights that matter. It’s easy to call .fit and .predict, but much harder to find the hidden feature variables before you build the model. Understand the data before you commit to an ML use case.
Want to work with Sivaram directly?
Whether it's an AI roadmap, a GenAI pilot, or a technology evaluation, get in touch.