Saabsa · Austin HQ, US delivery
Enterprise generative AI, attached to work you already do
Generative AI is a technique. The purchase is an application: a knowledge assistant, a document workflow, or a copilot inside a system your team already opens. Saabsa implements that application with your data, your identity, and a limit on cost.
“Generative AI” is too broad to buy
A company can spend a year piloting prompts and still have no system. Enterprise LLM development, done properly, is an application with users, a corpus or a set of tools, an evaluation set, and an owner. Private enterprise AI means the corpus and the identity stay inside a boundary you can describe to security.
Saabsa does not try to rank for the word “generative AI” by itself. We implement the slice that has a buyer: enterprise knowledge AI, internal copilots, and document generation with a human in the loop.
Knowledge assistants
Answers from your corpus, with citations and a refusal when retrieval is weak. See RAG development.
Document workflows
Extraction, ranking, and a generated artifact someone reviews before it is sent. Lease Exit is the pattern.
Workflow copilots
Drafts and suggestions inside an existing queue, not a new destination employees must remember to open.
Cost and model choice
A ceiling, a default model, and a reason to escalate to a more expensive one.
Implementation, not a model tour
LLM application development here includes the interface, the retrieval or tools, the logging, and the rollout. Fine-tuning is uncommon as a first step. RAG and agents cover the two shapes we see most. Deployment is how the application gets out of a side environment.
If you cannot name the user and the weekly task, the readiness sprint will probably return a no-go. That is the point of buying it first.
Questions buyers ask
Before you book the sprint
Do you build customer-facing chatbots?
When the task is bounded and the failure mode is acceptable. An open-ended bot on a marketing site is usually the wrong first system.
Can this stay private to our tenant?
Yes. The sprint writes the data boundary before implementation is scoped.
Is ChatGPT integration the project?
Sometimes a thin integration is enough. We will say that. Most enterprise work is retrieval, permissions, and the workflow, not the chat window.
How do you measure success?
A baseline on the task: time, error rate, or containment. “People said they liked it” is not the metric.
Related
The rest of the implementation map
AI implementation
Part of the same prototype-to-production path.
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.
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.