Production AI hardening, evals, fallbacks, cost and latency
The gap between a prototype that demos well and a system a business depends on is not model quality. It is retries, evaluation, cost control, observability, and a handover somebody else can maintain. This is the engineering layer around the model. What happens when the API is slow. How you find out behaviour changed before a customer does. What the thing costs per month at real volume. Who owns it after I leave. Building software and automation systems since 2011, with enterprise delivery, across banking, insurance, telecommunications, public sector and manufacturing.
What's included
Five to six sessions across a month. We take one prototype you already have and work through reliability, then evaluation, then cost, then the handover documentation. That order is deliberate, because it is the order these things break in.
- Retry, timeout and fallback handling around the model calls that affect users or business-critical flows
- Evaluation and regression tests that surface behaviour drift before a release ships
Meet your expert
Mirza I.
Transform AI Strategy with Proven Framework
Writing software full time since 2011, alongside a BSc and then a Master's at the University of Passau. Enterprise delivery in Germany and the UK since 2018. No shortcuts, no straight line. A genuine obsession with technology, how it works, and what it can do for people when it is set up properly. That path led through enterprise consulting, AI systems, and eventually Frankfurt, where the focus now is on what is coming next. Agents, large language models, and the infrastructure that makes them run in production rather than in a demo. Certified n8n Expert Partner, licence 18628384, valid through February 2028, with code merged into n8n core. I deliver automation and integration work across banking, telecommunications, public sector and manufacturing in the DACH region. The kind that still runs after the engagement ends, rather than a half-finished workflow.
