OpenAI / GPT

A GPT demo is easy. A GPT system in production is a different job.

GPT will impress anyone in a sandbox. Putting it in front of customers or employees, where it has to be reliable, affordable, and safe on inputs nobody anticipated, is a software engineering problem that happens to involve a model. The teams that stall keep reaching for a better prompt when the thing blocking them is evaluation, observability, data quality, or a plan for when the model gets something wrong. That gap is where our OpenAI work lives.

What we actually do

We build production systems on the GPT family: direct API integration, Azure OpenAI for regulated and enterprise deployments, the Assistants and Realtime APIs, and fine-tuning in the rare cases it is actually justified. Around the model we build the parts that decide success, evaluation so you know whether output is right, observability so you can see what is happening in production, and cost management so the bill does not quietly outrun the value.

Where GPT projects go wrong

The predictable failures: a model grounded in nothing, so it invents answers; no evaluation, so quality is a matter of opinion; runaway token costs no one is watching; and a pilot that works on the happy path and falls apart on the inputs real users send. We design for grounding, measurement, and cost from the start, because those are the parts that separate a feature that ships from a demo that does not.

Why Abstrakt Solutions

We build production AI for a living, with the engineering discipline that turns a capable model into a dependable system. As a Salesforce and AI consulting firm, we integrate GPT where it does real work inside your stack, and we are honest about when a different model, or no model, is the better answer.