Quant on Demand
Quantitative analytics, models and validation for every kind of investment process — discretionary and systematic, buy-side and sell-side. Built around your data, your infrastructure and your decisions.
Discuss your requirements →Quant that is designed for how you actually invest
QuantDesigned is an independent, senior-led quantitative finance consultancy. Quant is not just for black-box systematic funds — it sharpens any investment or finance process. Whether you run a discretionary, fundamental, or fully systematic book, we design, build, validate, and operationalise quantitative solutions using the data and infrastructure best suited to the problem.
We don't supply data — we make your data work harder. Our partnerships with leading data providers mean we understand institutional-grade datasets deeply, so we can build analytics that extract maximum value from the sources you already have or help you identify the right ones.
Who we work with — and what we do for them
Switch between the kind of organisation you are and the kind of work you need — the panel updates to match. The lists are illustrative, not exhaustive.
Across every asset class and every stage
Whatever you trade and however far along you are, the engagement is shaped around your problem.
Asset Classes
Rates, FX, credit, equities, commodities, OTC and structured products, and digital assets.
The Full Lifecycle
Research, build, validate, deploy, operate, and train — we can join at any stage and leave the capability with you.
However You Engage
A one-off diagnostic, a time-boxed build, an embedded capability, or training. See how we work →
The money — and the mandate — are moving to the quants
+16% quant/CTA fund assets grew faster than any other strategy in 2025; AQR's AUM rose 89% to $109bn as global hedge-fund AUM hit a record ~$4.74tn. — With Intelligence / Hedgeweek
Regional regulators — HKMA, MAS, ASIC/APRA — are actively pushing governance, validation and human-in-the-loop controls: demand for exactly the independent oversight QD provides. — HKMA
What runs through every engagement
Regardless of client type, three things are constant.
- Independent validation & governance — model validation, code review, and remediation you can stand behind
- Quantitative due diligence — on models, funds, data products, and analytics platforms. See examples →
- Knowledge transfer & training — so the capability stays in-house, not locked to us
We use machine learning and AI where they genuinely help — applied with the same rigour as any other model, with overfitting control and validation built in, not as a buzzword.
How an engagement works
There is no standard package. Most engagements move through some or all of these stages.
Assess
Diagnostics, gap analysis, and a prioritised roadmap.
Build
Targeted analytics delivered in time-boxed sprints with documented milestones.
Embed
Ongoing quant capability, knowledge transfer, and build–operate–transfer.
Train
Practitioner-led upskilling on real market data, with CPD/CPT evidence.
Every stage has a cost of getting it wrong
80.3% of enterprise AI projects deliver no business value — the root causes are organisational (scope, ownership, sponsorship), rarely the algorithms. A structured Assess stage is what catches that early. — RAND 2025 / Gartner
20–30% salary premium commanded by APAC quant / AI / ML / data roles, after a decade-long supply gap — exactly why an Embed engagement can be faster than building a full in-house team. — Selby Jennings