Saabsa · Austin HQ, US delivery
From AI prototype to production
The prototype proved the idea. Production is a different job: permissions, evaluation, integration, cost, and a workflow someone owns. Saabsa does that job, starting with a two-week sprint instead of another proof of concept.
Pilots stall for ordinary reasons
A proof of concept is allowed to ignore the messy parts. Production is not. Retrieval has to respect who can see which document. An agent has to stop before it writes back to a system of record. A copilot has to be cheap enough to leave on. Someone has to know what “wrong” looks like after the engineer who built the demo moves on.
Teams searching for AI pilot to production, AI proof of concept to production, or AI productionization are usually describing that gap. More model testing will not close it.
Name the workflow
One path a real user takes. Not a platform, not a lab, not five use cases at once.
Keep the prototype’s lesson
We reuse what the demo already taught you, then replace the parts that cannot leave a laptop.
Add the production pieces
Evaluation harness, identity, logging, CI, and a rollback. AI production engineering, not a rewrite for its own sake.
Decide in two weeks
The sprint returns a go/no-go and a fixed quote for the build, or a recommendation to stop.
Production AI development, scoped to the stall
We modernize the path around the prototype you have: RAG, agents, APIs, document intelligence. We do not sell a greenfield “AI platform” when a thin vertical slice will answer the question. If eighty percent of the job matches a playbook we already operate—Patientree, DataXPipe, ForgeMeter, or Lease Exit—we will say so.
Readiness is a separate question from enthusiasm. The production readiness assessment is the sprint’s job. Evaluation and security are what make the go decision defensible to a technical buyer.
Questions buyers ask
Before you book the sprint
Do we have to throw away the prototype?
No. The sprint starts from the demo, the repo, or the vendor pilot and lists what can stay.
Is this AI modernization?
Only where the existing workflow or system of record is the constraint. We do not run a generic modernization program.
How long is production after the sprint?
The sprint quotes the next step. A thin slice is often the following build. A multi-quarter platform is a reason to pause, not a reason to start.
What do we keep if we stop?
The use-case score, architecture and security notes, ROI estimate, and the written recommendation.
Related
The rest of the implementation map
AI implementation
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.