AI Integration Services
I add AI to real products — OpenAI/LLM features, retrieval (RAG), and agentic workflows wired into your SaaS with cost control, evals and guardrails. I've used AI integration to cut support volume by 30%.
Beyond the demo: AI features your users can trust, engineered for cost, latency, and reliability — grounded in your data, evaluated, and safe.
What's included
- OpenAI / LLM API integration into existing products
- Retrieval-augmented generation (RAG) over your own data
- Agentic workflows and AI-assisted automation
- Cost control, caching, streaming and right-sized models
- Evals, guardrails and prompt-injection defense
How I work
We target one feature where AI clearly earns its cost, not AI for its own sake.
Retrieval over your data, schemas/tools to constrain output, and evals to measure quality.
Caching, token budgets and monitoring so the feature is reliable and the bill is predictable.
At Algotix AI I integrated OpenAI APIs into a production SaaS and reduced support tickets by 30%. I build with agentic tooling in my own delivery workflow and design AI features with explicit cost and quality controls.
Related work
FAQ
Will adding AI blow up my costs?
Not if it's designed for cost: narrow use case, retrieval grounding, caching, the smallest model that passes evals, and per-feature token budgets. Cost control is a design choice.
RAG or fine-tuning?
RAG for answers that must reflect your current data (most cases) — cheaper, updatable, citable. Fine-tuning only for fixed style/format needs.