AI System Architecture
Model selection, inference pipeline design, and production-grade deployment — built to your load profile.
Most enterprise AI projects fail not at the model, but at the system around it — latency budgets missed, cost overruns, no clear ownership between model and application. You need a system, not a demo.
We design the full inference architecture — model choice, serving layer, caching, evaluation harness, cost controls — against your actual load profile, then ship it into production behind proper monitoring and rollback.
- Choosing and benchmarking a model provider against your actual latency, cost, and quality requirements
- Designing a serving layer that survives traffic spikes without over-provisioning
- Migrating from a single-vendor prototype to a resilient, multi-region inference topology
- Building the cost-control layer so inference spend doesn't scale linearly with usage
Typically starts as a Discovery engagement mapping your current latency and cost baseline, then a scoped Build to the target architecture.
This is the discipline behind PegBit Core, our own production layer — a vendor-neutral inference architecture, battle-tested inside our own products, with product-isolated knowledge bases and per-user personalisation.
Tell us what you’re building and we’ll scope where AI System Architecture fits.