Useful architecture for health & insurance problems that are still taking shape.
Strategy defines intent. Execution delivers outcomes. Between them sits design. CueRatio works in that space — helping organisations turn ideas into products, systems and operating models that can actually work in the real world.
CueRatio is a young practice founded on close to two decades of insurance, actuarial, health, product, and strategy experience. We take on a small number of serious problems where technical judgment, data design, and practical delivery need to meet.
This website is meant to represent us honestly, at the size we actually are. We want it to be clear about what we are good at: structuring ambiguous insurance problems, designing the logic behind products and decisions, and helping teams move from intent to something buildable.
A young practice with early engagements underway — some advisory, some design-led, a few beginning to convert into longer working relationships. Building deliberately.
Quietly, deeply, and practically. We always believe in building the foundation right — with structured data — and would rather be precise about a small useful contribution than over-claim a large transformation.
The disciplines.
Across every domain.
These are the architecture disciplines we bring to every engagement — independent of product type or market. They apply whether the problem sits in health, life, parametric, or general insurance.
Designing insurance propositions that are commercially sensible, explainable to channels, and grounded in how customers actually make decisions — including the documents and specifications needed to move from idea to reviewable product material.
Helping teams define the canonical data, semantic layers, rules, controls, and governance needed before AI can be responsibly used in insurance decisions — and the separation between model signals and governed choices.
Working through triggers, basis risk, payout logic, operational workflows, and fiscal boundaries for climate-linked protection schemes — with ground-truth validation at the design stage.
Building the bridge between product design and agent confidence — the minimum a channel needs to understand, believe, and say before a product can be sold responsibly and persistently.
Our first named
vertical.
Health and life protection is where our current engagements are deepest. Health challenges rarely exist in isolation — they interact with income, longevity, caregiving, financial resilience, and life-stage risk. Our work spans data foundations, protection product design, customer journeys, operational workflows, and AI-enabled decision systems across both health and life.
We help organisations create a trusted health data foundation that can support analytics, automation and AI. Our work spans data models, semantic layers, document intelligence and decision-ready health information architecture — so that AI deployment is auditable and defensible when it happens.
We work on the design of health and life protection solutions — from traditional insurance products to embedded and digital protection models. This spans critical illness, cancer, hospital cash, loan-linked protection, term life, whole life propositions, and parametric health concepts. Our focus is on making protection understandable, scalable, and operationally viable.
We help organisations understand health needs beyond the policy contract — exploring how people navigate diagnosis, treatment, caregiving, recovery and financial stress. Most consultancies focus on the product. We focus on the person carrying the risk: the caregiver, the chronic disease patient, the aging breadwinner, the informal worker.
We design AI-enabled workflows for health and insurance operations — helping organisations move from document-heavy processes to structured, explainable decision systems. This includes medical document ingestion, claims extraction, underwriting evidence workflows, and health data quality governance.
A small body of work,
honestly described.
We are not presenting a long case library because we do not have one yet. What we can share is the type of problem we have started to earn trust on, and the nature of our contribution so far.
Contributed to the design thinking for parametric heat protection for informal outdoor workers: trigger logic, enrolment and payout construct, operational workflow, and the importance of keeping public-sector exposure predictable and capped.
Helped reframe inclusive insurance from a broad intention into a more decision-oriented agenda: which segments deserve exploration, what uncertainty needs to be resolved, and when a pilot should integrate, pause, or stop. Built a governance structure that forces decisions rather than accumulates endorsements. Beyond the reframe, also providing execution depth — structuring the analytical work, building the commercial case for priority segments, and giving the team the confidence to move from strategy to action.
Supporting the development of new insurance products from concept to reviewable material — proposition logic, customer and agent positioning, benefit construct, product specifications, and the documentation a team needs to move from intent to something that can be assessed, priced, and taken to market.
Working on the bridge between product design and agency confidence: how agents understand the product, how they explain the need, what objections they face, and what minimum scaffolding helps them reach the first credible customer conversation.
Worked on the structure behind AI readiness: consistent definitions, semantic layers, data quality governance, and the separation between model signals and governed decisions — so that AI deployment is auditable and defensible when it happens.
Small practice.
Serious standards.
Our credibility will be earned over time. Until then, the clearest thing we can offer is transparency about how we work.
Insurance problems often involve imperfect data, unclear ownership, and competing incentives. We name the uncertainty rather than concealing it inside confident language.
A good architecture should be usable by product, actuarial, data, technology, and operations teams — not just persuasive in a presentation. If it cannot be built and owned, it is not finished.
Budgets, regulation, data quality, channel behaviour, reinsurance appetite, and operational capacity are not obstacles. They are design inputs — and the architecture has to work within them, not around them.
Our role is to help structure the problem and design the system. The organisation must be able to own, challenge, and adapt what we build — after we are gone. Dependency is not an outcome we design for.
The architecture challenge
depends on where you sit.
Each of the audiences below reads the same engagement differently. That is by design — the problem is different, and so is the conversation that starts it.
CXOs and senior leaders where strategy, data, technology and distribution need to work as one system. You have the ambition and the regulatory pressure. What is often still being designed is the architecture layer that makes all of it coherent — the canonical schema, the decision governance, the explainability model.
State governments, development finance institutions, World Bank programmes. You are deploying protection at scale and need the instrument to be actuarially sound, operationally scalable, and fiscally predictable — simultaneously.
Gig economy operators, employer health programmes, embedded insurance platforms. You are embedding protection into a product not originally designed to carry it. The architecture of the data flows, trigger design, and partner hand-offs determines whether it works at scale.
Technology companies entering insurance. You have the build capability. What is still being developed is the actuarial depth, regulatory fluency, and data architecture discipline to deploy responsibly. Getting this right at design stage costs far less than correcting it later.
Appointed Actuary at three insurers — SBI General, Bharti AXA, and Prudential Health India — and previously a senior consultant at PwC, spanning P&C, health, and life across India and emerging Asia.
The practice is intentionally small. For now, that is a strength: fewer layers, more direct thinking, and more care in choosing the work we take on.
CueRatio is the architecture practice. MeanRev Technology handles execution and build. Both are early-stage and deliberately focused.
Three ways
to begin.
Because CueRatio is still early, we prefer bounded work with clear outputs. That protects both sides: useful progress for you, and focus for us.
A short, structured review of your product, data, AI, or insurance architecture question. We identify the layer where the constraint actually sits — not where it appears to.
Output: a practical note on what needs to be resolved and what choices need to be made. Most clients find the problem that surfaces is meaningfully different from the one they described.
A fixed-scope engagement to design the schema, decision logic, product construct, trigger design, or operating model needed to move forward.
Suitable for: canonical data layer, parametric trigger system, AI governance framework, product configuration, health data semantic layer. Your team builds from it — no dependency on us to deliver.
Senior support while a team is building, testing, or navigating a difficult insurance-data-product-process-AI decision. We ensure that what gets built is worth building.
We are currently working with a small number of clients on this basis. If the problem is genuinely hard, we want to hear about it.
Have an insurance problem that is important,
but not fully formed?
That is usually the right starting point. Send a note with the context, the decision you are trying to make, and where things feel stuck.