Service · AI Development · Integration
Develop and Integrate Custom AI Systems for Companies.
This service page describes the specific engagement: Trixner digital solutions designs, develops and integrates internal business applications that connect company data, rules, roles and suitable AI components, from a clearly scoped system concept to a production-ready version.
Tasks, status, sources and approvals in one interface.
DMS, CRM, ERP, Files
Validate, Search, Prepare
Service Approach
AI System Development Is Software Engineering With Clearly Bounded AI.
The language model is only one component. A dependable application needs a clear interface, business logic, data access, integrations, permissions, error handling and accountable operations.
We therefore develop from the business process outward. For each workflow step, we decide whether fixed rules, retrieval, AI or human approval provide the most reliable solution.
When is it a good idea?
Custom Development Pays Off When the Specialist Process Makes the Difference.
Business Logic
Proprietary Rules Define the Process
Review criteria, roles, exceptions or decision paths are company-specific and can be represented in off-the-shelf software only through workarounds.
Integration
Multiple Systems Need to Work Together
Documents, forms, DMS, CRM, ERP or databases need to be connected in one workflow without breaks between systems.
Control
Outputs Require Approvals and Evidence
Sources, changes, system suggestions and accountable decisions must remain traceable.
Further development
The Process Continues to Evolve
Functions, data sources and AI models need to be extendable or replaceable in a controlled manner.
Solution Types
What We Develop as a Custom AI Application.
The type of system follows from the business task. The result is often a combination of conventional application development, integration, workflow and clearly bounded AI functions.
Internal Specialist Application
Validate, Document and Approve
Dedicated workspaces for recurring specialist processes, cases, roles, status and traceable decisions.
RAG & Knowledge
Research With Source Grounding
Search approved company knowledge, support answers with sources and integrate them into the actual workflow.
Workflow & Assistance
Prepare Cases in a Structured Way
Classify inputs, extract information, generate drafts and route unclear cases to the appropriate person.
AI Function
Extend Existing Software Selectively
Integrate a clearly defined AI function via API into an existing portal, specialist system or internal product.
Scope
From the System Boundary to a Controlled Handover.
The specific project scope is documented in writing. This keeps functions, client input, quality standards and operational ownership transparent for both sides.
Concept & Architecture
Define user roles, the core process, data sources, specialist rules, AI tasks, integrations and deliberate non-goals.
Development & Integration
Implement the interface, backend, workflows, permissions, APIs and suitable AI components as a usable application.
Testing & Handover
Document test cases, acceptance criteria, documentation, monitoring, operational boundaries and the next expansion stage.
Project Process
From a Clearly Scoped Specialist Problem to a Production-Ready Version.
01
Define Scope and Success
Define the user group, core task, baseline, target values, systems and acceptance criteria together.
02
Validate the Architecture and Prototype
Test data flows, roles, the interface and critical AI tasks early with representative cases.
03
Develop the Core Version
Implement the agreed functions, integrations, permissions, logging and error paths reliably.
04
Accept the System and Prepare Operations
Evaluate quality against the test cases, involve users and define responsibility for monitoring and maintenance.
Quality & Security
Production AI Needs Verifiable Boundaries.
Data Access
Preserve Permissions
The application receives only technically and professionally approved access; user permissions are accounted for in the system concept.
Quality
Evaluate With Real Test Cases
Expected results, acceptable deviations, source grounding and stop conditions are defined before acceptance.
Human in the Loop
Approve Critical Outputs
Uncertain or commercially important cases remain with a responsible person and are not automated blindly.
Operation
Make Errors and Costs Visible
Monitoring, logging, model costs, outages and provider dependencies inform the operating decision.
What You Receive
A Usable Application, Not Merely a Technical Prototype.
The components delivered depend on the agreed scope. The first production-ready version deliberately focuses on the core process.
Software
Agreed Functionality
A tested application with the defined roles, core functions, data flows and integrations.
Transparency
Documented System Boundaries
Architecture, dependencies, quality criteria, known limitations and required human approvals.
Further development
Prioritised Expansion Path
Insights from usage and test cases, plus a dependable basis for the next investment decision.
Client Involvement
Specialist Ownership and System Access Are Part of the Project.
Dependable delivery requires someone who knows the process, can assess rules and test cases, and can make timely specialist decisions. IT or system owners support data access, integrations and permissions.
If the objective, data landscape or process is not yet sufficiently clear, an initial assessment is the more appropriate starting point.
Technical Foundation
Long-Standing Software and Integration Development Meets Modern AI Integration.
Trixner digital solutions has developed web, software and integration solutions since 2002. AI system development extends this foundation with RAG, language models and controlled AI workflows; it does not replace the principles of sound software engineering.
How this can become an internal specialist application is illustrated by the solution scenario for internal AI systems. The scenario is a professional illustration, not a client reference.
Next Step
Let Us Define the First Sensible Version Together.
In the discovery call, we clarify the specialist problem, user group, systems, available data and the appropriate starting point without prematurely prescribing an AI solution.