Build Generative AI Solutions Around Trusted Enterprise Information

Generative AI can help people search, summarize, draft, and explain complex information, but enterprise deployment requires trustworthy sources, access rules, evaluation, and human review. GBSI designs and builds bounded generative AI applications for the workflows your teams actually use. We define the user task, connect approved content and systems, test quality and safety, and establish ownership for the solution after launch. The service can work with SAP and relevant non-SAP information.

Why Choose GBSI

The goal is a reliable application in a real workflow, not an isolated prompt demonstration.

Enterprise context

We map the task to approved documents, data, business definitions, and user permissions.

Quality by design

We establish evaluation scenarios, source citations where appropriate, human review, and escalation.

Production ownership

We plan monitoring, content updates, support, and cost before rollout.

Common Generative AI Engineering Challenges

Untrusted answers

A fluent response can still be wrong. We test factual accuracy and make source and uncertainty handling part of the design.

Sensitive information

Users need role-appropriate access. We define data boundaries and inspect leakage risks.

Scattered content

Documents can be outdated, duplicated, or poorly tagged. We assess ownership and content preparation.

Undefined user task

A general chatbot rarely solves a specific job. We define a task, user journey, and acceptance measure.

Missing human review

Drafts and recommendations may need an accountable reviewer. We place approval in the workflow.

Operational cost and drift

Usage, content changes, and model behavior need monitoring. We set ownership and periodic evaluation.

From Prototype to a Maintained Business Application

GBSI selects a use case such as knowledge retrieval, document summarization, or assisted drafting and defines its approved sources and users. We design retrieval and interaction, build a contained prototype, evaluate it with representative tasks, then prepare the integration, access, support, and monitoring required for a controlled launch. We do not claim that one model or vendor is suitable for every organization.

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Generative AI Engineering Services

Use-case design

Define the user job, source material, value hypothesis, and success criteria.

Content assessment

Review quality, freshness, ownership, and permissions of source information.

Architecture

Select model, retrieval, integration, and hosting approach appropriate to requirements.

Experience design

Specify prompts, source display, feedback, and review flow.

Prototype

Build a bounded application for representative tasks.

Evaluation

Test accuracy, groundedness, access, safety, and edge cases.

Deployment

Implement approved integration, logging, support, and release practices.

Optimization

Review usage, quality, content changes, and operating cost.

A Four-Step Engineering Approach

Define

Define the task.

Prepare

Prepare approved information.

Build and evaluate.

Deploy

Deploy with monitoring and ownership.

Find Your Ideal Deployment Model

Findings from the team

⁠⁠The latest industry insights, technology advancements and findings form the team.

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Frequently Asked Questions

How does this differ from the existing Generative AI page?

The existing page describes the broad service. This page specifies engineering of a defined enterprise application with evaluation and ongoing ownership.

Can it use SAP information?

Yes, where the appropriate approved connections and permissions are available.

Do answers cite their source?

Source display can be designed where suitable for the use case and information architecture.

How is quality evaluated?

We use representative user questions and agreed measures for correctness, relevance, access, and failure.

Who maintains the solution?

The operating model assigns business content ownership and technical support responsibilities.