Nine years, one stubborn idea, and several decisions that cost us money at the time. This is the honest version — including the years where the method was expensive to hold and the projects we rebuilt at our own cost to prove it worked.

A small engineering team in Kathmandu
Value model before architecture
Cumulative client value to date
Golden's first three projects were delivered on time, to specification, and to the genuine satisfaction of everyone involved. All three were, commercially, a waste of the client's money. Not because the software was bad — it worked exactly as agreed — but because in each case the agreement had described a system rather than an outcome, and the system turned out not to produce one.
We rebuilt all three at our own cost. It was not a marketing decision; the firm was very small and the founders were embarrassed. What it produced, though, was the observation that has governed everything since: in every one of those cases we could have predicted the failure in the first week, if anyone had asked what number was supposed to move.
Nobody had asked, because in our industry nobody asks. So we started asking, and we have not stopped.
Golden started with a small engineering team in Kathmandu and a single commitment: never hand a client a system they cannot make money with. The first three projects were rebuilt for free until the client's numbers moved.
After watching well-built software fail commercially, we inverted our process. Every engagement began with a value model — where revenue leaks, where cost hides, where time disappears — before a line of code was written.
Remote-first delivery took us into the United States, United Kingdom and Northern Europe. Clients stayed because the value model kept proving itself against their own finance reports.
We moved from analytics to applied AI: prediction, language, vision and decisioning. The rule stayed the same — the model only ships if it changes a business number someone owns.
Golden joined the Yashoda Group, an industrial house with 5-star hospitality, hospitals, hydropower and manufacturing. Suddenly our AI had heavy industry, healthcare and hospitality as living laboratories.
Voice agents, lending copilots and autonomous operations agents went live for clients in seven countries — answering, deciding and acting inside real workflows.
Cumulative client value crossed $1.7 billion across 120+ projects, delivered by 217 people and a 30-partner network. The next target is the businesses AI has not reached yet.

In 2019 we turned down four projects in a single quarter. Each was funded, each was scoped, and in each case the assessment concluded that the work would not pay for itself. Writing that down four times in three months, as a young firm with a payroll, was the most uncomfortable period in our history.
Two of those four clients came back within eighteen months with different problems, and both are still with us. One referred the engagement that became our largest programme. The arithmetic that felt suicidal at the time turned out to be the best marketing we ever did, because a firm that will decline your money is a firm whose advice you can actually use.
They refused a signed-off budget and told us to spend a quarter of it on something else. That is when we started trusting their advice.
We did not move into AI because it was fashionable. We moved because the value models started pointing there. Year after year, the largest unclaimed numbers in our clients' businesses were in decisions being made too slowly, demand going unanswered, documents being read by hand and patterns nobody had the capacity to notice. Those are AI problems.
By 2022 most of our engineering was applied AI, and by 2024 autonomous agents were answering calls, deciding collections priority and completing back-office work in production for clients in seven countries. Eighty-four of our 217 people now work in the AI practice.
The rule that governs it is the same rule from 2018. The model only ships if it changes a number someone owns.
Joining the Yashoda Group in 2023 gave us two things. The obvious one was a balance sheet — the ability to put fees at risk, to decline funded work, and to take multi-year positions that a firm dependent on its next invoice cannot take.
The less obvious one mattered more. The group operates five-star hotels, hospitals, hydropower plants and manufacturing businesses, and we work inside them. Our hospitality revenue AI was built against real occupancy problems. Our predictive maintenance came from a real nine-day outage during peak flow. Our healthcare work started in a clinic with a three-week waiting list and empty chairs.
Most technology firms learn an industry from their clients. We had the unusual luxury of learning four of them from the inside first.
Operated within the group
Golden became part of Yashoda
Freedom to decline and to be honest
From one room to seven countries
Each with a signed value model
The number the story is really about
Almost all of the value AI has created so far has accrued to organisations that were already sophisticated: large banks, global platforms, well-funded technology companies. The mid-sized lender, the regional hospital group, the hydropower operator and the property business have mostly been sold pilots and left with dashboards.
That gap is the whole of our ambition. Not to build the most advanced models — the frontier labs will do that far better than we can — but to be the firm that reliably converts those models into money for businesses that nobody else takes seriously enough to be honest with.
We think that is a bigger business than the frontier, and a considerably more useful one.
Everything on this page reduces to one question we will ask in the first meeting: which number needs to move, and who owns it?