Saabsa · Austin HQ, US delivery
AI implementation for companies stuck after the demo
You already have a prototype, a vendor demo, or a pilot that never reached the workflow. Saabsa implements the system: retrieval, agents, APIs, evaluation, security, and the operating change. The first purchase is a two-week sprint with a written go/no-go.
Implementation is the work after the proof of concept
Search for “AI implementation services” and you will find firms that still sell a strategy workshop. The buyers we take on have already crossed that line. Someone built a RAG demo, an agent, or a copilot. It works on a laptop. It does not survive permissions, Monday-morning volume, or a security review.
That is a delivery problem. The model is rarely the blocker. The blocker is the path from prototype to a system an operator will trust: data access, tool boundaries, evaluation, identity, cost caps, and a person who owns the metric.
Score the use case
Week one of the sprint kills weak ideas. If the workflow has no owner and no baseline, we write that down and stop.
Wire it to real systems
APIs, documents, and the system of record. Cloud is the substrate, not a separate migration practice.
Prove it before launch
An evaluation set, a security note, and a cost ceiling your finance partner can read.
Ship or decline
A production build only if the sprint survives. A retainer only after the first win.
What “AI implementation consulting” means here
Consulting that ends in a slide deck is a different purchase. Implementation means the system runs in your environment against a number you already track: hours, cycle time, error rate, or revenue protected. We will not invent a new KPI to make a demo look successful.
The sequence is sprint, architecture, build, production, then an optional operate squad. Prototype to production is the core of that sequence. Enterprise deployment is how it lands inside identity and change control. Agents and RAG are the two patterns we implement most often.
Questions buyers ask
Before you book the sprint
How is this different from hiring an AI consulting firm?
The first deliverable is a go/no-go package and, if it passes, working software in your stack. Advice that is not tied to a workflow, a metric, and a security boundary is out of scope.
Who is a fit?
Usually a US company of about 50 to 400 people with a champion, a real workflow, and either a prototype or a painful manual process worth instrumenting.
Do you staff a general “AI practice”?
No. We productionize stalled AI, implement playbooks we already run, or take on healthcare operations. We do not bid six equal service lines.
What if you recommend not building?
You keep the memo, the architecture notes, and the quote you did not accept. That is a successful sprint.
Related
The rest of the implementation map
Prototype to production
Part of the same prototype-to-production path.
Enterprise deployment
Part of the same prototype-to-production path.
Production readiness
Part of the same prototype-to-production path.
AI agents
Part of the same prototype-to-production path.
RAG development
Part of the same prototype-to-production path.
Enterprise generative AI
Part of the same prototype-to-production path.
Evaluation
Part of the same prototype-to-production path.
Security and governance
Part of the same prototype-to-production path.
Healthcare AI
Part of the same prototype-to-production path.
AI Production Sprint
Part of the same prototype-to-production path.
Bring the prototype, not a wishlist.
The AI Production Sprint is two weeks and fixed fee. You leave with a go/no-go, architecture and security notes, and a next-step quote you can decline.