We have run these functions, not just advised on them.
Three partners, each senior in one domain: brand and demand, sales and deals, pricing and retention. Between them: thirty years of brand strategy, two decades of AI product and go-to-market, and seven years deploying industrial AI into the field.
A senior specialist in the domain you need.
Raining Code is three senior partners, each deep in one part of the commercial practice. Rohit on pricing and retention, Adi on deals and go-to-market, Sushobhan on brand and demand. You work with the one whose domain you need, backed by the others when the problem crosses lines, which it usually does.
Each of us is senior in one domain, and has run it inside an operating company with AI brought in where it earned its place. Sushobhan owns how you are seen and found. Adi owns how you win. Rohit owns how you keep what you win. Different domains. One method. No handoff to anyone junior.
Rohit Chikballapur
Founder · Pricing, Retention & Delivery · Basel
Basel 🇨🇭 · Riedisheim 🇫🇷 · Freiburg 🇩🇪
Rohit owns how you keep what you win. Seven years building industrial AI at Facterra: IoT and asset intelligence deployed into manufacturing plants across the DACH region and France. He built the commercial motion that sold it, then lived the part most firms never see: deployment, calibration, and the after-sales relationship that decides whether an account renews and grows or quietly leaves. His advisory work includes NettWorth — a cross-border wealth platform — where the engagement began with user research into why existing tools failed globally mobile professionals and concluded with the AI-native architecture that now underpins the product.
Before Facterra he incubated digital services inside Schneider Electric, watching one of the world's largest industrial companies pilot and quietly retire AI initiatives. The lesson stuck: the technology almost always worked; what failed was everything around it. The pricing, the adoption, the service motion.
He founded Raining Code to build the practice he would have hired. He advises in English, French, and German, covers European engagements, and takes on a small number a year. If you work with Raining Code in Europe, he is in the room.
- Pricing strategy, and the discipline to hold it in the deal
- After-sales, service, and retention that compound an account
- AI deployment and calibration that holds in the field
- EN / FR / DE — European engagements
Sushobhan Mukherjee
Partner · Brand & Demand · Basel
Basel 🇨🇭 · Singapore 🇸🇬 · Mumbai 🇮🇳
Sushobhan owns how a company is seen and found. Thirty years in brand strategy and digital design at enterprise scale, most recently as AVP Digital Design and Strategy at Infosys in Basel. He has run brand architecture and commercial positioning for global enterprises across Asia, Europe, and North America, and his benchmark has always been whether the work converts, not whether it reads well.
Grand Effie. Jay Chiat Award for Strategic Excellence. Multiple Asian Marketing Effectiveness Awards: prizes for brand strategy that changed commercial outcomes, not for design. Before Infosys he co-founded Dealstreetasia.com, a financial intelligence platform acquired by the Financial Times. IIM Lucknow. Three decades across Singapore, Mumbai, New Delhi, and Basel.
His sharpest work now is answer-engine positioning: making sure that when a buyer asks an AI assistant who to consider, the market and the machine both name you, and describe you the way you would describe yourself. If the question is whether your position will hold and your story will move buyers, that is where Sushobhan anchors.
- Brand architecture and market positioning
- Commercial narrative that moves a sceptical buyer
- Answer-engine positioning — getting named by AI assistants
- Enterprise brand strategy across Europe, Asia, and North America
Adi Sankaran
Partner · Sales & Deals · Houston
Houston 🇺🇸 · North America
Adi owns how you win. He is a product and engineering leader who understands sales from the inside, a rare combination. At Zinier he led product for an AI-native field service platform deployed to enterprise operators in telecom, utilities, and infrastructure globally: what to build, how it fit complex enterprise workflows, how the product earned its price. He has run discovery and design sprints with customers in the field, and has a precise sense of where AI genuinely changes a deal and where it is decoration.
Before Zinier, industrial digital transformation at Accenture's Industry X.0 practice and strategic portfolio work at EY. Engineering roots as a field engineer in oil and gas: the operational world where industrial AI eventually has to pay off. Twenty years across product, engineering, go-to-market, and consulting.
MBA from Rice, engineering from UT Austin. He covers North American and global engagements from Houston. His domain is the deal cycle: speed-to-lead, RFP and quote acceleration, deal intelligence, and the judgement of which AI to buy, build, or ignore.
- Go-to-market and the AI-assisted deal cycle
- RFP, quote, and speed-to-lead acceleration
- Buy-versus-build judgement on AI sales tooling
- North American and global engagements
A senior partner in your market. The full bench behind them.
You work with one partner: the one whose domain you need, in your market and your timezone. Behind them sits the rest of the practice: when the work crosses from the pitch into the deal, or from the price into the service motion, the right person joins. We run every engagement on one method, refined across the commercial problems we have each solved inside operating companies. What you buy is the practice's judgement, not one person's calendar. The person who scopes your engagement runs it.
We have built, sold, priced, and deployed AI inside operating companies, on both sides of the Atlantic.
That is the credibility plank. Not advisor decks, not best-practice frameworks. The actual experience of putting AI in front of real customers and staying long enough to know what holds. It is why we can tell you where AI moves the number, and where it doesn't earn its place.
Industrial and deal-driven B2B is where the pattern recognition runs deepest: manufacturing, chemicals, components, energy, infrastructure, B2B software. And regulated financial services, where the data constraints and governance stakes are equally unforgiving. The buyers are technical, the decisions are consequential, the forgiveness is low. If AI earns its place there, it is real.
"The revenue engine is where the money is made — and the last place anyone has properly engineered. That is the work."