The wealth management operating system.

Lithika built Avagance from the client's side of the desk, starting from one question: what does an adviser actually do that a machine cannot? Everything in the platform exists to protect that answer, not replace it.

He builds things and finishes them. He took a specialist clinic from zero to a clean exit at twice its annual net profit, running it remotely on systems and documentation alone while completing an MSc in Investments. That is where his instinct for Avagance comes from: keep the hard engineering underneath, where the client never sees it, and make the surface simple enough to trust.

An engineer before he was an investor, he pairs that build discipline with genuine quant depth. His MSc dissertation built a novel hybrid forecasting model that outperformed the standard statistical benchmark, then spent as much effort making its reasoning transparent, on the view that a forecast is only useful when an adviser can explain it to a client who just watched the news and is worried about their pension. That view runs right through Ava.

Dinuwan is the engineer behind the platform. He architected and built Avagance end to end, from the database schema to the API layer to the advisor portal to the AI agents sitting on top, without outsourcing a line of it.

His defining rule is a simple one: a wrong number is worse than no number. In software that advisers repeat to clients, a valuation that quietly shows zero because a data feed is down is not a UI bug, it is a compliance risk. So he built that principle into the system itself, where every calculation returns a clear result, an honest "unavailable", or an error with a reason, and no part of the platform is allowed to invent a fallback. That instinct for what to refuse to display is what makes him the right person to build software for a regulated profession.

A software engineering graduate from the University of Plymouth, he came to this through machine learning. His final year project built a credit-scoring model for corporate and retail lending, and the lesson that stuck was not the accuracy score but what happens when a model is confidently wrong. It shaped how he builds AI for financial services now: fairness, transparency and proper checks, from the start.

Maria is the adviser in the room. She runs her own advice firm, so every product decision gets tested against how advice is actually delivered and how a practice actually runs, not a theory of either.

She is Founder and Director of Iris Wealth Management, a Partner Practice of St. James's Place, advising high-net-worth individuals, couples and families on investment, retirement, protection and estate planning, and holds the CISI Level 6 Certificate in Advanced Financial Planning. Before advice, she spent years in the markets, starting in fixed income derivatives in 2010 and going on to head trading desks at JNF Capital, Prime Wealth Group and ZAI Capital Markets.

She is also well known beyond the desk: a monthly columnist for Master Investor Magazine, a presenter on Tip TV, the author of 18 Smart Ways to Improve Your Trading, and a familiar name in magazines like Entrepreneur UK and International Business Times. She knows how to make a complicated subject land, which is exactly what advice, and Avagance, has to do.

Mel has done almost every job in an advice firm. Administrator, paraplanner, financial planner, and finally operations leader. That full run through the profession is exactly why she is here: she can tell the difference between a feature that actually helps a practice and one that just looks good in a demo.

She spent a decade inside financial planning operations, most recently as Head of Operations at Emery Little and before that in the same role at Buckingham Gate Chartered Financial Planners, with earlier spells at St. James's Place, Smith & Williamson and Black Swan. She is DipPFS qualified with the Chartered Insurance Institute and holds a Certificate in ESG Investing from CFA UK. Few people understand what it takes to run a modern advice business from as many angles as she does.

Her job at Avagance is to keep the platform honest. She shapes the workflows so they make sense with a human in the loop, where the AI does the work and the adviser stays in control, and she holds every feature to one test: is this actually useful, or just nice to have?