Case study

DISTRIQ AI

Service: AI Development & Integration
Year: 2026
Live: distriq.ai

01The work

Overview

A relocation intelligence platform that answers the question nobody can answer from a listing: what is this neighbourhood actually like to live in. It brings together neighbourhood data, school ratings, safety metrics, commute analysis and cost-of-living comparisons, with AI-assisted research on top, and it serves three different audiences from a single codebase — individuals researching a move, employers relocating staff, and employees browsing the neighbourhoods their employer has already approved. It is live in Dubai, Singapore and London.

Challenges

Three audiences that need the same data and completely different products. A person choosing where to rent wants to compare four neighbourhoods on their own terms and take as long as they like. An HR team moving forty people wants dashboards, service-level alerts, relocation kits and reports, and needs the whole thing to be defensible to a finance director. A relocating employee wants the consumer experience, but constrained to what their employer will actually pay for — and must not be shown options that will be refused. Build those as three products and the data drifts apart within a quarter; build them as one and every screen ends up serving nobody in particular.

Solution

One data layer, three shells over it. The neighbourhood intelligence — schools, safety, commute, cost of living — is gathered and modelled once, and the three personas are routing and presentation decisions on top of it rather than separate applications, wired up through a single router. The employee portal is the consumer view with the employer's approved set applied as a constraint, which is why it feels like the consumer product rather than a cut-down version of it. The enterprise surface adds what only an organisation needs — HR kits, SLA alerts, organisational dashboards, intelligence reports — without forking the thing underneath.

Conclusion

The structural bet is that relocation is one problem seen from three distances, not three problems. Holding the data in one place and varying only the surface is what lets a consumer feature reach the enterprise product the week it ships, and it is why adding a city is a data exercise rather than a release.

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