AI, Developer PlatformFragmented AI Stack20+ APIs → One Interface

    How A Developer Team Unified 20+ LLMs Behind A Single API

    Every AI provider has its own API, dashboard, and quirks. We built the single surface that makes all of it disappear.

    Creative AI Chat

    01 — Context

    The Situation

    Creative AI Chat's users were building on top of many LLM providers at once. Each provider had its own API, dashboard, pricing model and quirks, and switching between them was slow, expensive and error-prone.

    02 — Diagnosis

    The Problem Was Not Missing Models, It Was Missing Integration

    Before RapidCode:

    • Every provider had a different API contract, forcing per-model integration work
    • Comparing model output required juggling separate dashboards
    • Cost and latency varied wildly with no easy way to route intelligently
    • Enterprise teams could not standardise on one contract for procurement

    03 — Strategy

    Our Approach

    We did not build another model. We built the layer that makes every other model interchangeable, so teams stop integrating and start shipping.

    Move 01

    Designed one unified API that abstracts every model behind the same interface

    Move 02

    Built an intelligent routing layer that picks the fastest, cheapest fit per request

    Move 03

    Shipped a single dashboard to compare, switch and monitor models side by side

    Move 04

    Layered analytics and prompt tooling so builders can iterate without leaving the platform

    04 — Judgement

    Why We Made These Decisions

    Why we built a routing layer, not just a proxy

    Passing calls through unchanged solved integration pain but not cost or speed. Intelligent routing turned the platform into a real optimisation surface, not just plumbing.

    Why the dashboard mattered as much as the API

    Developers pick the API; teams evaluate on the dashboard. A serious side-by-side comparison view was the difference between hobbyist adoption and enterprise adoption.

    05 — Shift

    Before vs After

    Before
    • Separate integration per LLM provider
    • No unified way to compare model output
    • Manual, per-provider cost and latency tracking
    • Fragmented dashboards for every model in use
    After
    • Single unified API for 20+ LLMs including GPT-4o, Claude and Gemini
    • Intelligent routing for cost and latency optimisation
    • One dashboard to compare, switch and monitor models
    • Analytics and prompt tooling built into the platform

    06 — Outcome

    Business Impact

    01

    One integration replaces 20+ separate provider integrations

    02

    Teams switch models in seconds instead of engineering sprints

    03

    Cost and latency optimised automatically per request

    04

    A single procurement contract for enterprise AI teams

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