TIVONIX

Integrating OpenAI and other AI providers

We integrate OpenAI and other AI providers with a secure server-side architecture, cost control and fallbacks for when a provider fails.

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What’s included

The parts the work is made of — and where to read more about related formats.

01

Server-side integration

Keys and model calls stay on the server, never in the browser. We spell this out so the result is usable for an estimate, build or handover without guesswork.

  • 01Keys on the server
  • 02Proxy layer
  • 03Request queues
  • 04Logs
Related service:Backend & API

Who it’s for

  • 01

    Products calling AI straight from the front end and risking their keys

  • 02

    Teams whose AI spend is growing without control

  • 03

    Services that need to stay up when one provider fails

Tell us the task — we’ll outline scope, timeline and cost

The brief takes a few minutes. We reply with the next step, risks, timeline and a cost range.

Plans

How the work goes

  1. Stage 1

    Scenario and data

    What task AI solves and on which data.

    • Task and quality criteria
    • Data sources and access
    • Cost limits
    • What stays with a human
  2. Stage 2

    Prototype and validation

    The approach is tested on real data before integration.

    • Model and approach
    • Prompts and tools
    • Tests on real examples
    • Quality and cost assessment
  3. Stage 3

    Integration into the product

    AI is built into the system with permissions and control.

    • Server-side integration, keys on the server
    • Access rights
    • Fallback path on failure
    • Action log
  4. Stage 4

    Launch and control

    Quality and costs are watched after launch.

    • Quality and cost monitoring
    • Improvements from real requests
    • Documentation
    • Access stays with you

Why TIVONIX

  • 01

    One owner of the result

    The work is led by the TIVONIX founder: responsibility isn’t spread across contractors and decisions are made quickly.

  • 02

    AI as part of the product

    Keys on the server, checks, a fallback path and cost control — AI works inside the system, not as a separate demo.

  • 03

    Code and access stay with you

    Source code, access, environment settings and instructions are handed to the client. The project isn’t locked to us.

  • 04

    Built in working parts

    We show working parts of the system, not pictures: interface, server side, admin panel and integrations.

FAQ

Can AI be added to a live product?

Yes. We add a server layer for working with models and plug it into the right flows.

Why not call the model straight from the browser?

That exposes the key to any user. Server-side integration protects keys and lets you control costs.

Which AI models do you work with?

We choose for the task: OpenAI, Anthropic Claude, Google Gemini and other providers. Keys and calls stay on the server, with a fallback if a provider fails.

How is the cost formed?

It depends on scope: how many roles, screens and scenarios, which integrations and launch requirements, and what already exists. Scope and price are fixed in writing after reviewing the task — before work starts.

How long does it take?

It depends on scope and on what already exists. We name the timeline after reviewing the task and split the work into stages with intermediate results, so you see progress instead of waiting for the end.

What do you need to get started?

A short description of the product and users, the task, and whatever exists already: designs, code, documents, access. That’s enough for a brief — we reply with the next step, risks, timeline and a cost range.

Which industries do you work with?

SaaS, fintech, e-commerce and internal systems. Launched projects include a booking marketplace, an AI commerce platform, a music fintech platform and a partner dashboard.

Who keeps the code and access?

You do. Source code, access and clear documentation are handed over to the client.

Tell us what system you need to launch

Describe the product, users, workflow and integrations. We’ll reply with the next step, risks, timeline and cost range.

See product proof