Availability

Hire an AI SaaS developer

There is a large gap between developers who have called an AI API and developers who have run AI features in front of real users. I built Creatiz.ai — an AI content platform — from scratch and later rejoined the team to scale it, so I have dealt with the cost, latency, and reliability problems that only appear at real usage.

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AI content platform built from scratch: Creatiz.aiAI content platform built from scratch
creators on an AI-assisted analytics product: 500+creators on an AI-assisted analytics product
typical time to ship an AI feature live: Daystypical time to ship an AI feature live

Where teams get stuck

AI demos are easy and AI products are not

The prototype takes an afternoon. Streaming, retries, cost control, evaluation, and sensible failure behaviour are the other 90% — and they are what determine whether the feature survives launch.

Nobody on your team owns AI quality

Without evaluation, prompt changes are guesswork and regressions ship silently. AI features need a quality harness the same way code needs tests.

AI spend has no ceiling by default

Unbounded token usage turns a flat-rate subscription into a loss on your heaviest users. Budgets, caching, and model routing have to be designed in, not retrofitted.

What you get

AI features built to survive production traffic, not just a demo
Model routing across providers for cost and reliability, with fallbacks
RAG pipelines over your own data, with a real vector store and chunking strategy
Streaming interfaces with proper loading, cancellation, and error states
Evaluation harnesses so prompt changes are measured rather than guessed
Cost instrumentation per feature and per user, visible from day one
Full product surrounding the AI — auth, billing, dashboards, the whole thing

How it works

01
Feasibility and honest scoping
I will tell you which AI features are worth building and which will disappoint your users. That conversation saves more money than any implementation detail.
02
One feature end to end
A narrow slice in production quickly, so you learn from real usage rather than from a mockup.
03
Instrument and harden
Cost tracking, caching, evaluation, and failure handling before expanding scope.
04
Scale the surface
Additional AI features on a proven foundation, which is dramatically faster than the first one.

Frequently asked questions

Which AI providers and models do you work with?

Claude, GPT, and open models through hosted inference. I generally route by task — cheaper models for extraction and classification, stronger ones for generation and reasoning — which usually cuts cost substantially without a quality drop.

Can you build a full AI SaaS product, not just the AI part?

Yes. Auth, billing, dashboards, and infrastructure are what I do anyway. AI features are one layer of a product that still has to work as a product.

How do you stop AI costs from spiralling?

Per-user token budgets, response caching, prompt compression, and routing by task complexity. Cost per request is instrumented from the first deployment so it is visible rather than discovered.

Do you do AI agents and automation workflows?

Yes — tool-calling agents, multi-step workflows, and integrations with your existing systems. I keep them as simple as the task allows, since agent complexity is where reliability usually breaks down.

Building something with AI in it?

Tell me what you want it to do. I will tell you what is realistic, what it costs to run, and how fast it can ship.

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