From Reinvention to Reimagination: The Journey to ChainAlign
{/* truncate */}
In 2012, I made one of the biggest decisions of my career - I left my corporate job to start something of my own.
What followed was more than a decade of digital supply chain consulting, primarily for pharma and agrochemical companies. I had the chance to work on some fascinating transformation projects - helping large organizations reimagine how information, materials, and decisions flow across their networks.
But behind all those projects, one thing I realised is that irrespective of which I client I worked with: Even with all the data, systems, and expertise, decisions still took too long - and too often, the wrong decisions won.
⸻
2024: The Year of Pause
By mid-2024, consulting became harder. Projects slowed. The market tightened. Leads that would normally take a few weeks to close began taking months - or disappeared altogether.
By early 2025, I made the difficult call to wind down my consulting business.
It wasn’t an easy time. I was applying for roles, waiting for interviews, and trying to figure out what to do next. But I also knew I didn’t want to lose momentum - or curiosity.
So I decided to use the time for structured learning.
⸻
Rediscovering the Builder in Me
I started by using generative AI tools to help me with writing and documentation. Then, out of curiosity, I began experimenting with AI coding assistants - first with Gemini CLI when it was released.
That changed everything.
Coding was something I’d enjoyed in the early part of my career but never pursued deeply. Now, with AI as a partner, I could go much further. I began building small things - scripts, web apps, mobile prototypes - and realized I could actually create working systems that solved real problems.
It felt like rediscovering a part of myself I’d forgotten I enjoyed.
⸻
A Conversation About Decisions
Around that time, I had a long discussion with a friend about how senior managers make decisions - how some organizations value transparency while others prefer ambiguity, and how AI might shift that balance.
That conversation stayed with me.
I realized that most enterprise systems are designed for reporting or monitoring, not deciding. And that’s a deeper problem - not just a process gap, but a coordination gap.
That insight became the foundation for what is now ChainAlign.
⸻
The Core Idea
The first version of ChainAlign started as an experiment - could I build a platform that helps organizations align decisions with strategy in real time?
I used S&OP (Sales & Operations Planning) as the test case. It’s one of the few places in an enterprise where cross-functional decision-making is both structured and visible. It gave me a perfect sandbox to design, test, and iterate.
But from day one, the vision has been broader - to build the decision intelligence layer that connects the strategic intent at the top of an organization with the execution decisions at every level.
⸻
What ChainAlign Does
ChainAlign aims to help organizations make faster, more aligned decisions through three connected layers:
- Strategic Context Layer - Extracts corporate priorities from strategic documents, transforming them into living constraints that guide every decision.
- Decision Intelligence Layer - Synthesizes data from across systems, models scenarios, and recommends options that balance cost, service, and compliance.
- Coordinated Action Layer - Captures approvals, governance, and rationale - ensuring every decision is transparent and traceable.
The goal is not to automate human judgment, but to augment it - to bring structure, speed, and shared context to how decisions are made.
⸻
Building the MVP
Over the last several months, I’ve been teaching myself modern architecture and development - cloud-native systems, GraphRAG reasoning, multi-tenant design, and LLM-based interfaces.
The current MVP integrates: • A multi-tenant knowledge graph for contextual reasoning. • A dynamic UI layer generated by LLMs. • Compliance and governance guardrails baked into the architecture. • Integration with live data and transcription for real-time decision tracking.
It’s rough in places, but it works - and it’s already capable of running small-scale decision simulations across data streams.
⸻
What’s Next
Right now, I’m continuing to build while exploring early customer pilots with large organizations facing coordination challenges across operations, finance, and compliance.
The plan is to bring in a technical co-founder to scale the architecture and raise a pre-seed round to deliver the first enterprise pilot.
In parallel, I’m continuing to experiment - reading new AI research, integrating lessons from the latest papers, and testing how human-AI collaboration can create a new model of enterprise decision-making.
⸻
Why Document This
This post isn’t for marketing. It’s for context.
As others join this journey - co-founders, collaborators, advisors, or early hires - I want them to see how it started.
ChainAlign didn’t come from a perfect plan. It came from experience, curiosity, and necessity - from seeing how organizations struggle to decide and realizing that AI might finally give us a way to fix it.
This blog will serve as the living record of that journey - the experiments, the pivots, the learnings, and hopefully, the breakthroughs.
Because the real story of building something new isn’t just what you’re building. It’s why you couldn’t not build it.