Inside Eloovor: Principles Behind Our Architecture

When we started building Eloovor, we were not optimizing for buzzwords. We were optimizing for a calm, reliable experience for people doing high stakes work: finding their next job. That goal shaped every technical decision we made.

Early on, we felt the tension between moving fast and building safely. Users were trusting us with their career story, and AI workflows introduced new kinds of failure. We needed an architecture that was fast, predictable, and easy to evolve.

This is a look at the principles behind our architecture and the reasons we chose the stack we did.

One language, fewer surprises

We build the product in TypeScript end to end. That gives us strong types across the UI and the API, reduces handoffs, and makes it easier to move fast without breaking things.

In practice, this means the same data structures that power the backend also shape what the frontend expects. When a response changes, the build catches it before a user does. That kind of safety is not a luxury. It is a trust requirement.

It also makes collaboration easier. Engineers can move between product surfaces without context switching, and shared utilities stay consistent across the stack.

Modular by design

Eloovor is built as a set of modules that share types and utilities but keep responsibilities clear. That includes the core web app, background workflows, and feature specific logic like resume generation or interview prep.

A modular structure matters because the product is evolving quickly. We want to improve a feature without rewriting the foundation. Clear boundaries let us move in small, safe increments.

A thin API, heavy work in the background

AI workflows are not typical request and response calls. They can take time, pull in multiple sources, and require retries. Early on, we learned that running these tasks inside the API made everything feel slow and fragile. A single long running analysis could lock up resources and degrade the experience for everyone else.

So we separated concerns. The API stays fast and predictable. The heavy work happens in background jobs where it can run safely and recover from interruptions. That shift made the system more stable and the product more trustworthy.

Data organized around the user

We designed our data model to be user scoped by default. Profiles, opportunities, and analyses live in user specific collections. This keeps access boundaries clear and supports features like per opportunity analysis without risk of cross user mixing.

It also makes it easier for us to add new modules because every feature has a clear place to store and retrieve data.

Reliable building blocks

We lean on proven infrastructure for authentication, data storage, and background processing. That includes Firebase for authentication and data, and background job infrastructure for long running AI tasks.

These choices let us focus on product quality while keeping reliability high. Instead of reinventing the wheel, we invest our energy in the parts that matter most to users.

Observability and graceful failure

AI workflows are complex and sometimes fragile. We treat observability as a first class concern so we can see where things fail and fix them quickly. We also design for graceful failure so users always understand what is happening and what to do next.

Reliability is not just uptime. It is the confidence that a user will get what they asked for, even if a background job needs a retry.

Performance that feels invisible

Performance is not only about benchmarks. It is about perceived speed. We optimize for fast initial loads, responsive interactions, and a UI that never feels blocked by long running work. That is why heavy analysis runs outside the request path and why we invest in type safety and consistent data shapes.

We avoid architecture that is impressive but brittle. That means no unnecessary complexity, no premature optimization, and no tech choices that make hiring or maintenance harder than it needs to be. Boring in the best sense is often the most reliable option.

Architecture is only useful if it helps the product evolve. A clear, modular structure means we can ship improvements without creating regressions across the system. When the Resume Builder changes, it should not break Interview Prep. When we add a new analysis, it should fit naturally into the existing data model. That is why we keep boundaries clear and rely on shared types instead of duplicated logic.

It also helps the team move quickly. Designers and engineers can iterate on experiences knowing the backend contracts are stable. PMs can plan work with confidence because the system supports incremental delivery instead of big risky rewrites.

Privacy as a design constraint

Because we handle career data, we design with privacy and safety in mind. User scoped data, clear access boundaries, and grounded AI outputs are architectural choices as much as product choices. This helps keep trust high as we scale.

The quiet goal: make the tech invisible

The best architecture is the kind you never notice. A user should not care about our stack. They should feel that the product is fast, consistent, and dependable when they need it most.

That is the standard we build toward every day. Every architectural choice is a promise to users that their time and trust will be respected.