tecnovy AI Suite

Building Enterprise AI Apps

In this training you build an internal AI assistant that meets four requirements before anyone in the company uses it. Every person signs in and works within their role. The application reads only data that person may see. Checks on inputs and outputs, called guardrails, are meant to catch injected instructions and data leaving the system. The cost per request has to be predictable. You check each requirement with tests.

Attendance certificate4.8/5 on ProvenExpert

What this module delivers.

What your team takes away: a pattern for AI applications with roles, permissions, guardrails and tests, built on an internal assistant. tecnovy's Building Enterprise AI Apps is for full-stack and back-end developers who have worked with LLM APIs.

How the training runs

Method: One continuous project: an internal AI assistant from design to deployment
Basis: tecnovy curriculum, tied to no cloud and no model provider
Outcome: tecnovy certificate of participation, no examination

Dates & booking

Choose a date that fits

No public dates are scheduled yet.

New dates are published regularly. Join the waitlist or plan an in-house course for your team.

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Fit

Who this module is designed for

Typical roles

You develop full-stack or back-end applications and are asked to build AI features into them.
You are asked to turn an AI prototype into an application that colleagues across the company use.
You lead an AI product team as tech lead and decide on architecture and providers.

Prerequisites

No prerequisites

You can start right away. What you need: experience in web development, experience with LLM APIs.

Consider instead LLM Engineering Without LLM API experience: you build model APIs into Python code, with prompt templates, JSON schemas, single tool calls and first tests.

Curriculum

Building Enterprise AI Apps in Detail

Five parts follow one internal AI assistant that you extend across every course day: from the architecture through sign-in, permissions and data, guardrails and the interface to tests and deployment. The course assumes experience with LLM APIs; evaluation in depth and ongoing operation have tecnovy courses of their own.

01Architecture of AI applications

You settle which building blocks the assistant consists of and which ones you build yourselves.

Building blocks: interface, orchestration, models, data, tools
Orchestration: the layer that routes requests to model, data and tools
Build vs. buy: purchase or develop, block by block
Keeping the model provider replaceable

02Identity, permissions and data

This is where you decide what the application may do and read, and for whom.

Passing sign-in and roles on to AI features
Connecting company data to the application
Access only within the user's permissions

03Guardrails

Guardrails are checks and filters that inputs and outputs pass through before they reach the model or the user.

Checking and filtering inputs
Checking outputs before they are shown
Prompt injection: instructions smuggled in through inputs or documents
Data leakage: confidential data that ends up in answers

04User experience

Here you design the interface so that employees can judge how far to trust an answer.

Streaming: the answer appears while the model generates it
Source references that show where an answer comes from
Feedback and clear error messages
Designing for trust and transparency

05Tests and deployment

Finally you set what gets checked before deployment and move the assistant into its target environment.

Automated tests and evaluations for the whole application
Deployment and scaling
Cost per request

Outcome

What you will be able to do afterwards

01
You design the architecture of an AI application from interface, orchestration, models, data and tools.
02
You decide for each building block whether to buy or build it, and keep the model provider replaceable.
03
You pass the signed-in person's identity and role on to every AI feature of the application.
04
You connect company data so that the application reads only what the signed-in person may see.
05
You build in guardrails that check and filter inputs and outputs, and test them against injected instructions (prompt injection) and data leakage.
06
You design the interface with streamed answers, source references, feedback and clear error messages.
07
You write automated tests that check permissions, guardrails and answers across the whole application before deployment.
08
You deploy the application, plan its scaling and work out the cost per request.

tecnovy Teilnahmezertifikat

Confirms your attendance and the curriculum covered in the training. Issued by tecnovy, shareable on LinkedIn.

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Offer scope

What is included

Included

Project repository with a reference applicationThe reference application mirrors the course project, and you can start from it for your own applications after the training.

Why tecnovy

What you get on top with us

No Slideshow, Hands-On!

Promised: no PowerPoint marathon. We work in groups, tie theory to practice, and you get real project examples from our experienced trainers plus the exchange with like-minded people.

Certificate Showroom

Get your certificate of participation and, if you have one, add your exam certificate from E-Learning. Fully automated, beautifully designed. Just for you, only at tecnovy.

Flexible Date Change

If you are not able to attend the course, you can rebook your training free of charge up to one week before the start of the training.

Attend Twice, Pay Once

You are welcome to attend the training online again within a year as a refresher.

Learn from Experts

We always guarantee you the use of didactically and methodically first-class qualified trainers who draw their knowledge from training experience as well as professional practical and project experience.

FAQs

Frequently asked questions

01Which tech stack does the course use?

tecnovy's Building Enterprise AI Apps uses a common stack based on Python or TypeScript. You can then carry the assistant's architecture over to other languages and frameworks.

02Is the course tied to a particular cloud?

No, tecnovy's Building Enterprise AI Apps is not cloud-specific and shows how the architecture maps onto AWS, Microsoft Azure or Google Cloud. All three offer a managed service that provides language models from several vendors through an API (as of November 2026). You compare those services in detail in the tecnovy course Cloud AI Architecture.

03Do I need to take LLM Engineering first?

No, for tecnovy's Building Enterprise AI Apps experience in web development and with LLM APIs is enough, wherever it comes from. If you have not yet built a model API into your own code, start with LLM Engineering: API calls, prompts, JSON schemas, single tool calls and first tests in Python.

04How does the course handle access rights to company data?

In tecnovy's Building Enterprise AI Apps every AI feature queries data with the identity and permissions of the person currently using the assistant. You check whether that boundary holds with automated tests for different roles.

05Do guardrails reliably prevent prompt injection?

No, and tecnovy's Building Enterprise AI Apps does not promise that. In its 2025 Top 10 for LLM applications, the OWASP GenAI Security Project states it is unclear whether fool-proof methods against prompt injection, meaning injected instructions, exist, and recommends input and output filtering and least privilege among other measures. In the course you therefore combine guardrails with access rights and test both.

06Can our team roll out the course assistant in the company straight afterwards?

Whether an AI application is approved is decided by your IT, your security team and the business unit against their own criteria, not by a training. In tecnovy's Building Enterprise AI Apps you learn to implement and document permissions, guardrails, tests and cost so that you can give those teams concrete answers.

07What access do the exercises need?

The exercises in tecnovy's Building Enterprise AI Apps call a language model through its API and deploy the application. That requires API access to a model provider and an environment to deploy to.

08What comes after Building Enterprise AI Apps?

The tecnovy LLMOps training teaches you to run AI applications over time, from versioning and releases to monitoring and cost control. Evaluation datasets and quality measurement in depth are covered in AI Evaluation & Observability; Building Enterprise AI Apps only tests its own application before deployment.

09Does Building Enterprise AI Apps end with an examination?

No, Building Enterprise AI Apps is a tecnovy course with no examination and no certification from a cloud or model provider. With at least 80% attendance you receive the tecnovy certificate of participation.

What does your training at tecnovy look like?

Enterprise AI AppsBuilding Enterprise AI Apps

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