We design and build data systems that convert raw data into reliable, structured outputs — continuously, automatically, and ready for whatever your teams need to act on, including AI.
Automated analytics systems that ingest raw data, apply auditable business logic, and deliver decision-ready outputs on a recurring basis — without manual intervention.
Advanced analytics and statistical modelling for research and insights teams — conjoint, segmentation, key driver analysis, TURF, and more, delivered at scale.
Embedded with finance, commercial, and insights teams. Analytics partner for strategy and consulting firms. White-label delivery for global research firms — invisibly, under their brand.
Data sits across systems, relies on manual effort, and becomes unreliable exactly when decisions matter most. This shows up the same way every time.
Different teams working from different versions of the same numbers
Analysts spending more time fixing spreadsheets than interpreting outcomes
Reporting cycles that slow or break under deadline pressure
Dashboards that exist but get bypassed when it matters most
Research delivery that doesn't scale across markets without adding headcount
AI initiatives stalling because the underlying data isn't clean or structured enough
A data factory is an operational analytics system designed to run as part of routine business activity. It automates data ingestion, encodes and audits business logic, and produces up-to-date outputs on a recurring schedule — without manual intervention at each cycle.
Once implemented, it becomes infrastructure: part of how the organisation runs, not a workflow that requires ongoing upkeep.
The AI is reliable. The data isn't. In building these systems across finance, research, and commercial operations, we see the same pattern when organisations try to layer AI agents on top of existing analytics: the agents fail in production. Not because the AI models are wrong — because the data they're reading is inconsistently structured, manually assembled, or governed by logic buried in spreadsheets no one maintains. The data factory is the prerequisite — not the afterthought.
Built for finance teams running closing, forecasting, and review processes under real deadline pressure.
Built for commercial teams that need consistent, comparable data across distributed markets and sales operations.
Built for research operations where volume is the constraint and consistency across markets is non-negotiable.
Built for teams receiving third-party data files on a recurring basis — converting raw vendor feeds into structured, queryable analytics layers without manual processing each cycle.
Advanced analytics and statistical modelling for research and insights teams. We handle the analytical workload — design, modelling, and delivery — so your team focuses on the strategy and the client relationship.
We work directly for research directors and as embedded analytics partners for agencies and global research firms, delivering under their brand on a white-label basis.
Typical engagements include conjoint studies for FMCG and fintech pricing decisions, multi-market segmentation for travel and healthcare platforms, and shopper decision trees for retail and packaged goods categories.
Choice-based conjoint with Hierarchical Bayes modelling. Simulators, WTP estimation, and design support included.
Clustering, profiling, typing tools. Multi-market with consistent segment definitions across countries.
Best-worst scaling for feature and message prioritisation. Anchor-scaled and market-comparable outputs.
Relative weight analysis (RWA) and regression-based driver modelling. Actionable priority outputs.
Total Unduplicated Reach and Frequency for portfolio optimisation and assortment decisions.
Van Westendorp PSM and price architecture modelling. Acceptable range and optimal price point outputs.
We run the quantitative analytics behind the scenes for agencies and research firms. Outputs are delivered in your format, under your brand. Clients see consistent, polished work — we handle the modelling, QC, and delivery machinery.
Multi-country studies with segment definitions, model parameters, and output formats held constant across markets. Comparable country-level outputs with a single consolidated view — no post-hoc reconciliation required.
We build simulators, profiling tools, and automated segment dashboards that client teams can continue using after the project closes. The analysis doesn't end at the debrief — it becomes part of how the team operates.
High-volume research programmes — NPS tracking, wave studies, multi-market brand health — delivered through automated pipelines. Same rigour at study 40 as at study 1, without proportional analyst time.
The moment this becomes relevant is usually one of three.
Month-end takes two weeks. Quarterly reviews start late because the data isn't ready. Analysts are the constraint — not because they're slow, but because the system is manual and every cycle requires the same manual effort.
The CFO's view doesn't match the commercial team's. Markets report differently. You've stopped fully trusting the dashboard because you know reconciliation gaps exist somewhere — you just don't know where.
Whether the board is asking when AI will be deployed, or pilots are underperforming, the barrier is usually the same: data that isn't clean, structured, or consistently governed enough for AI to act on reliably. That's the gap we close.
We work with finance and commercial teams at mid-to-large companies, and market research firms delivering analytics at scale across Asia-Pacific, the US, and Europe. Our longest client relationships are with firms that came in for a single study and stayed for the infrastructure.
We build operational analytics systems and deliver research at scale — not one-off dashboards, not advisory decks, not pilots that require internal teams to productionise.
We design and build complete data factories — from data ingestion through to dashboard and output layer. Typically engaged when reporting processes are under strain, or an organisation needs infrastructure in place before expanding analytics or AI capability.
Most project engagements run 8 to 16 weeks — shorter for contained systems, longer when multiple data sources, markets, or entities are involved. Delivery is iterative; working components are handed over throughout the build, not only at completion.
We work alongside internal teams on specific components — pipelines, modelling, business logic encoding, or automation. This model fits teams with existing analysts who need additional depth, structure, or capacity without a full outsourced engagement.
Common in marketing science: a client team runs the project; we run the analytics.
We provide analytics systems and research delivery behind the scenes for consulting and research firms. Partners deliver to their clients; we build and maintain the infrastructure and analytics that make delivery possible at scale — without the cost of expanding internal analytics teams.
The model used by global research and consulting firms to scale delivery without growing internal teams.
For project-based engagements, every handover includes full documentation and a logic transfer session. Most clients retain us on a support or iteration basis after initial delivery — system requirements change, and the easiest way to evolve a system is with the people who built it. This is discussed and scoped before any build begins.
We came in with a manual reporting process that took a week every month. That's been replaced by a system that runs automatically. The team is dependable — deadlines are always met, quality is consistently high.
Epitome automated our reporting pipeline. What took three days of analyst time now runs overnight. They've been a long-term partner for analytics, modelling, and automation — and the quality hasn't dropped once.
Select the one that fits closest. We'll show you where we can help.
Most analytics projects fail not because of technology, but because they are built around tools rather than decisions. We start with the decisions that need to be made, the operating constraints that exist, and the workflows that need to fit — then work backward to system design and execution.
The people doing this work come from analytics, finance, and data engineering backgrounds — practitioners who have worked inside the types of organisations we build for, not only consultants who have advised them.
Analytics infrastructure built without understanding business workflows rarely survives contact with real operating conditions. We design systems for how organisations actually work — with the constraints, exceptions, and pressures that exist in practice.
Platform-agnostic by design. We work within existing technology ecosystems and select tools based on fit. Experience spans modern BI platforms, cloud data infrastructure, databases, scripting languages, and advanced analytics and statistical tools.
If reporting is manual, fragile, or difficult to scale — or if AI initiatives are on the roadmap and you need the data infrastructure to support them — the first step is a scoping conversation.
A 30 to 45-minute call where we understand what you're currently running, where the friction is, and what a data factory or analytics engagement would need to solve. You don't need to prepare anything — we'll ask the right questions.
By the end, you'll have a clear picture of whether this is the right fit and what an engagement would realistically involve. Engagements are scoped and priced transparently before any build begins.