About
I'm an engineer who became an AI CTO. I decide whether AI is worth it, then build it or lead the team that does — from forecasting systems worth ~$40M a yearto multi-agent systems in production today.
What I Do
An AI programme needs a clear decision, an accountable owner and a practical route into the workflow. I assess these foundations before recommending a build. Read how I approach AI delivery.
I help leadership teams create a ranked use-case map, define the operating logic and build a value model that Finance can evaluate.
Pattern
Case
We restructured decisions across the automotive lead-conversion process. Conversion increased from 4% to 11% (approximately 2.7×) using the existing infrastructure. This was a separate engagement from the 5× lead-conversion case.
Role
I connect operations, Finance and technical teams around a shared business objective. My architecture and delivery experience helps turn that objective into a system the team can build and operate.
Lens
I examine the system, its constraints and the decision that needs to improve. That perspective helps me assess how a technical change will affect the wider business.
Current Work
I am Founder and Chief AI Officer / CTO at Kriyalytics Solutions. I work directly with prospective clients to define needs and develop consulting and solution engagements.
Kriyalytics Solutions — AI strategy and production AI builds for mid-market companies. Five clients across materials science, industrial IoT, steel trading and agritech (animal health).
30+ clients served across my career. I am available as a Fractional CTO & Chief AI Officer, and for full-time remote Chief AI Officer / CTO / CIO roles.
I designed and built steel-trading agents now in daily use. My industrial IoT and digital-twin work includes equipment-health consulting and a blast-furnace demo using simulated data. A live version is in development with an industrial client.
I provided consulting and architecture for vendor rationalisation, with procurement teams retaining final decisions. Recent agritech work includes animal-health consulting.
Japanese automotive agents show how I approach lead capture, matching and deployment. AI in Operations for Managers remains available while I prepare its revised five-day curriculum.
Background
I bring 15+ years of experience in data science, AI architecture and delivery leadership. I managed a $90M+ AI portfolio.
At Nagarro, I architected Forcastra AI and led a six-person team. Explore the platform on Nagarro's website.
My technical experience spans AWS, GCP, Azure and Databricks.
Education
I am an ISB CTO Fellow with a master's in Data Science and Engineering from BITS Pilani. My foundation in mechanical engineering helps me examine a system's internal workings and its wider business context.
Breadth
My work spans customer decisions, operational planning and internal coordination. Each industry brings a different set of constraints.
| Industry | Functions | Decisions and use cases |
|---|---|---|
| Retail | Supply chain, marketing, HR and administration | Demand and replenishment planning, promotions and markdowns, inventory working capital, planning coordination and approvals |
| Telecom | Marketing and maintenance | Retention interventions, network maintenance and field-crew dispatch |
| Automotive | Sales, marketing, supply chain, administration and finance | Sales guidance and customer financing, warranty-claim assessment, service-document retrieval, equipment health and quality inspection |
| Gaming | Sales, marketing and operational controls | Rule application, fatigue and legal requirements |
| Media | Marketing and moderation | Marketing decisions and content moderation |
| Manufacturing | Supply chain and operations | IoT, equipment health, process mapping and optimization, inventory, scheduling, resource planning |
| Professional services | HR, administration and finance | Staffing coordination, resource allocation, project estimation, budgeting and utilisation planning |
In professional-services work, I was responsible for project estimates, budgets, resource plans and financial figures. Programme managers retained final accountability.
See the retail replenishment and automotive GenAI cases for two examples of the decisions behind this work.
Leadership
My mentoring helps teams extend their capabilities and solve demanding problems. I teach practical problem-solving alongside technical skills.
At Nagarro, I mentored 30+ junior and senior data scientists. During more than four years on the AI hiring team, I evaluated 200+ candidates and helped hire 50 people across seniority levels.
I typically led project teams of four to six people. On the forecasting engagement, I led a 25-person team.
Recent work
Early pilot · full outcome pending
7,000 → 5,000vendors, with ~$100K/month saved and 15 analyst hours/week freed
Five agents normalise, research, validate and optimise vendor options across 3M+ SKUs. Procurement keeps the final decision.
Read the caseFinancial impact being measured
93.2% → 97.8%service level across 4,800 SKUs
Demand, inventory and replenishment forecasting built from scratch for a US firm.
Read the caseIn daily use
1–2 hrs/daysaved for the owner, across ~30 leads and 70–80 plates a day
I designed and coded both agents. Owner approvals govern lead changes, and people review flagged plate exceptions.
Read the caseShowcase on simulated data
Twin demoblast-furnace showcase on simulated data
Equipment-health consulting plus a digital twin that combines descriptive, predictive and prescriptive views. A live version is in development with an industrial client.
Teaching
Alongside building, I teach: six sessions for engineering students, corporate AI training for manufacturing, trading and civil-works teams, and the course AI in Operations for Managers. Its beta cohort rated it 5/5.
“Without first understanding whether AI is truly needed for your use case and what ROI it can deliver, it's difficult to create real value. The Quickscan gives you exactly that: a clear answer before you commit a dollar.”
Method
It starts with the AI Audit: one week, on your data, that reviews your AI, data and system maturity and finds where profit is leaking, then sets the next best steps and a roadmap for growth and savings.
Fit
Recommendations
Jitin played a pivotal role not only in experimenting with and developing the AI-driven forecasting solution but also in serving as an AI coach for end users, helping them understand and trust the forecasts produced by the model(s).”
In a space full of tech hype, he is refreshingly grounded, honest about what the technology should and shouldn’t do, and always focused on what will actually work.”
Jitin also has a remarkable ability to simplify complex AI and engineering concepts for business leaders, enabling informed decision-making.”
He is a master class coding hand and can decompose any hard problem in to simple logic and quickly build a scalable and ready to implement solution.”
He learns quickly and I would have no hesitation in working with Jitin once again in the future.”
Next
Read the field notes, join the weekly brief, or book a 45-minute diagnostic if there is a live business decision behind the AI work.