Sugar 1.0 proved trust with 200k+ gold users. Sugar 2.0 turns that installed base, transaction context, and referral engine into a done-for-you AI spend optimisation business. This FAQ explains how the raise is being used to convert that wedge into the broader financial operating system.
Capital will be deployed to turn the gold app investors already know into Sugar 2.0's first real distribution wedge. Gold remains active and important on its own, but it also gives us trust, transaction context, and an existing customer base to launch optimisation from.
What this raise is really funding: not a brand-new product with no audience, but the expansion of Sugar from gold auto-investing into done-for-you financial optimisation for the same customer relationship.
A portion of funds will also cover legal and compliance costs inherent to operating as a licensed fintech across multiple jurisdictions.
We are currently live in New Zealand, the United Kingdom, and India, with US entry planned by Year 3. The first 12 months are about proving Sugar 2.0 with the trust and customer context we already have, not skipping ahead to broad paid acquisition.
Near-term focus: Sugar 1.0 is the wedge. Sugar 2.0 is what we are proving. The job now is to convert trust into optimisation customers, measurable savings, and repeatable unit economics.
Priority stack:
Our model is concierge + AI: human operators handle complex switching and negotiation today, while the AI model takes on an increasing share of analysis and execution over time. This lets us deliver value immediately while building the technology moat.
Sugar is backed by operators and builders who have scaled globally relevant companies:
Technical Advisor: Ian Wong -PhD Computer Science (University of Auckland), VP of Engineering at Heidi Health (Australia's fastest-growing startup 2025), Founder of Transactional.ai, Principal Data Scientist at Fonterra.
The cap table is available to prospective investors after initial discussions. The founder retains majority ownership.
Sugar 1.0 is gold auto-investing -a proven product with 200k+ users, 112k+ transacting users, $1M ARR, and 95% retention in New Zealand. Gold is durable on its own, but more importantly it gives Sugar 2.0 three assets on day one: trust, distribution, and transaction data.
Sugar 2.0 is AI Spend Optimisation -where the venture-scale opportunity lives. We are not moving away from gold; we are using gold as the entry point. Customers who already trust Sugar with savings are the first audience for optimisation, and the savings freed up by the optimiser can flow back into gold, creating a compounding loop.
Why the gold base matters now:
Trust wedge: gold is a simple, culturally legible starting point for household finance.
Distribution: low-friction onboarding let Sugar reach 200k+ users before launching the optimiser.
Data: open banking and transaction context feed the optimisation engine.
The core insight: People know they're overpaying -but the cost of inaction is invisible, while the cost of switching is immediate: research, phone calls, paperwork, risk of getting it wrong. So they never do it. The reality is that 10–30% of household income can be freed up across insurance, energy, broadband, subscriptions, mortgages, and more. Globally, that adds up to $1.5–2 trillion per year lost to this "inaction tax."
Real example -Eva: £41,000 income, £3 in savings, living pay-cheque to pay-cheque. A sudden roof leak and a cat needing surgery -with no savings to cover either. Sugar cut her bills, consolidated debt, and cancelled waste, freeing up £6,800 per year in two weeks. Result: roof fixed, cat got surgery, went on holiday -and she's still saving and investing today.
Sugar acts as a buyer's agent for your entire financial life. We don't just find the cheapest option -we find what's most valuable for each household based on their priorities, life stage, and context. Then we execute the switches for them. Not advice. Not a dashboard. Done for you. We only make money when our customers save money -10–15% of the savings we find.
This alignment is structural and cannot be easily replicated by incumbents. Revolut profits from its own products. NerdWallet is lead-gen. ChatGPT can advise but cannot act. Sugar does all three: analyse, recommend, and execute -and every household we serve deepens our data, our provider integrations, and the trust that compounds over time.
The vision:
Investors receive quarterly snapshot updates covering key metrics, progress against milestones, and strategic priorities. We send longer, more detailed annual updates reflecting on the year and outlining the path ahead.
Major decisions -fundraising, market launches, pivots -are communicated promptly as they arise, not held for scheduled updates.
We value transparency and treat our investors as long-term partners in building Sugar.
We are raising on a SAFE (Simple Agreement for Future Equity) -the industry standard for early-stage venture investment. The SAFE converts to preferred shares upon a priced round or qualifying event.
The valuation cap is $25 million USD. Discounts are available for investments above $20,000 NZD.
Target outcomes before Series A/B:
This is an angel-stage investment and carries meaningful risk. As with any early-stage venture, your investment can go to zero. You should only invest capital you can afford to lose entirely.
The risks include:
The case for reward:
Angel investments can multiply significantly. The best venture outcomes come from backing exceptional founders tackling enormous markets at the right time -and being patient.
Devrath Soni, Founder & CEO. Before Sugar, Devrath worked in KPMG Advisory, exited a bespoke suit shop (clients included BMW, the All Blacks, the Blackcaps, and Warnerbros Discovery), and scaled a flower shop to $100k per month. He then built Sugar's licensed gold investing app to 200,000+ users with no prior fintech experience.
The pattern across every venture has been the same: enter an unfamiliar industry, learn fast, build systems, and scale. Sugar is the biggest version of that pattern -applied to the $1.5–2 trillion inaction tax across global household finance.
Technical Advisor: Ian Wong -PhD Computer Science (University of Auckland), VP of Engineering at Heidi Health (Australia's fastest-growing startup 2025), Founder of Transactional.ai, Principal Data Scientist at Fonterra. Ian advises on AI architecture and engineering strategy.
Sugar is a solo-founder company today, supported by a technical advisor and a concierge team. Mitigating key-person risk is a priority: the immediate hires funded by this raise -Growth Lead, Ops Lead, and engineers -are designed to distribute execution across a core team.
Four secular tailwinds are converging simultaneously -creating a window that didn't exist even two years ago:
AI cost decline (50x per year): White-glove financial service at mass-market prices is now economically viable for the first time. What cost $100 per customer interaction in 2023 costs pennies today.
Open banking mandates: US open banking goes live April 2026. UK Variable Recurring Payments (VRP) are already live. Data silos are dissolving -Sugar can access transaction data programmatically rather than asking customers to upload statements.
Household financial stress: 57% of the global workforce lives pay-cheque to pay-cheque (ADP Research, 2025). UK household debt is exceeding pre-2008 levels. The need has never been greater.
Gold macro tailwind: Gold prices rose 60% in 2025 -the best calendar year since the 1970s. Central banks are buying ~1,000 tonnes annually. Sugar's gold product rides this wave, bringing in users who then convert to the optimiser.
Any one of these trends would be helpful. Together, they create a once-in-a-generation window: the technology is cheap enough, the data is accessible enough, and the demand is urgent enough for Sugar to work at scale.
Sugar combines AI with human accountability -and actually executes. Here's what happens when a customer signs up:
Today: Human concierges handle most of the execution. AI handles analysis, pattern recognition, and nudging.
Over time: The AI share increases -target 40–70% of analysis and execution handled by AI by Phase 2 (2029–2032). Every case the concierge handles trains the model.
Why this matters: We deliver value from day one with humans, while every case trains the AI. This avoids the cold-start problem that kills pure-tech approaches. The human layer isn't a cost to eliminate -it's accountability. Customers trust a person who's accountable for outcomes, backed by AI that handles the scale.
The key distinction: Sugar 1.0 and Sugar 2.0 do not share the same acquisition profile today. Gold proves trust and distribution. Optimisation has much higher revenue per customer and a separate CAC curve that we are proving now.
Sugar 1.0 - proven trust and top-of-funnel
Sugar 2.0 - economics being proven now
Sugar 1.0 metrics should not be read as Sugar 2.0 CAC. What Sugar 1.0 gives us is a lower-friction starting audience, trust, and transaction context. The next 12-24 months are about measuring Sugar 2.0 conversion, cost-to-serve, and payback separately, then scaling once those numbers are repeatable.
The real story is how Sugar 2.0 economics evolve. AI reduces cost-to-serve faster than price compresses, so the model transitions through three margin stages:
This is the key insight for a service-heavy model: the humans aren't overhead -they're training data. Every case handled today makes the AI more capable tomorrow, and the margin structure shifts from services to platform to infrastructure over time.