AI product · Full-stack architecture · Jun 2023 — Sep 2025
Taking an AI spatial-discovery product from concept to a production-ready MVP
I owned the full-stack architecture and key product flows across a location-based interface, realtime social mechanics, AI workflows and cloud delivery.
CASE / 02 SYSTEM ONLINEIMAGE PLACEHOLDER · Replace through public/images/cases and the case-study data file.
01 · Executive summary
The shortest honest version.
An early-stage product needed to combine spatial exploration, external location data, social interaction and AI behavior in one coherent experience.
I designed a service-oriented full-stack architecture and shipped the main product loops across Next.js, Python, PostgreSQL and third-party APIs.
The concept became a production-ready MVP with maintainable boundaries between UI, services, integrations and data processing.
02 · Context
Why the engineering work mattered.
The product centered on map-based exploration and geosearch, then layered in realtime social behavior and AI-assisted workflows. Each capability had a different latency and data profile.
Because the company and product remain under NDA, this case focuses on verifiable responsibilities, system boundaries and shipped capability rather than confidential growth metrics.
03 · System design
A clear path from signal to production.
Next.js interfaces for map exploration and user workflows.
Typed handlers separated clients from service behavior.
Python and Node.js modules held business logic and orchestration.
Google Places, AI providers and WebSocket communication.
PostgreSQL workflows deployed through Docker and AWS.
Constraints that shaped the solution
- Requirements evolved while the MVP was being validated.
- Map, realtime and AI interactions had different latency patterns.
- External APIs required explicit error and fallback boundaries.
- The architecture needed to remain testable without slowing product learning.
04 · Decision ledger
The trade-offs behind the implementation.
External APIs changed independently from core business rules.
More modules and explicit mapping code.
Integrations could evolve without rewriting product logic.
Not every screen required persistent WebSocket coordination.
A mixed request/realtime data model.
Realtime complexity stayed concentrated in the interactions that needed it.
Rapid iteration still required confidence in core workflows.
Deliberate service boundaries during fast MVP delivery.
Python services could be tested independently of transport and UI.
05 · Change
Before and after.
BeforeProduct concept and evolving requirements
AfterProduction-ready full-stack MVP
BeforeIndependent map, AI and social ideas
AfterOne integrated product experience
BeforeDirect third-party coupling
AfterBounded integration clients and services
BeforeBusiness logic tied to transport
AfterUnit-testable service modules
06 · Outcomes
Technical change translated into team value.
- Delivered core user flows across frontend, backend, database and cloud infrastructure.
- Integrated geosearch, realtime communication and AI-agent workflows.
- Established service boundaries suited to continued production development.
- Converted product ambiguity into estimable technical work.
- Maintained a shared delivery path across product and business stakeholders.
- Balanced speed of learning with maintainability and testing.
07 · Evidence boundary
Precise claims build more trust than inflated ones.
- Product name, screenshots, user counts and commercial data remain confidential.
- The case describes shipped capabilities and direct ownership; no unverified growth claims are included.
- Architecture is simplified to communicate responsibility without exposing proprietary design.
08 · Reflection
What the project changed in my engineering judgment.
Explicit boundaries around integrations and business logic kept a fast-moving product understandable as its capability surface expanded.
I would introduce formal event contracts and deeper production observability earlier in the product lifecycle.
End-to-end product engineering under uncertainty, from UX decisions to cloud delivery.