AI automation / AI integration for applications
Integration with AI platforms
AI functionalities technically correctly integrated, with cost limits, clear prompts, optional RAG and monitoring.
Background
When this service makes sense
We integrate AI platforms and models into websites, applications, dashboards and internal flows. We choose the right solution for the project requirements.
- Main needAPI integrations for applications
- Role in the projectAI agents, document processing and automation connected to real processes, with rules, logbooks and human verification.
- Result pursuedAI functionalities technically correctly integrated, with cost limits, clear prompts, optional RAG and monitoring.
Deliverables
What usually comes into the project
The exact list shall be adjusted after the audit, but these modules frequently appear in integration projects with platforms.
01API integration
02Prompts structure
03Optional RAG
04Limited rates
05Cost monitoring
AI integration
AI integrations for actual processes
We connect applications to AI platforms and models for summarization, classification, generation, assisted search, chat or document processing. We choose the supplier according to the quality of the results, costs, confidentiality and technical requirements. Integration includes usage limits, monitoring and rules for sensitive data.
- Concrete Usage CaseWe choose a task force where the Aion saves time or improves experience: support, documents, content, classification or search.
- Cost controlWe use limits, appropriate models, cache and monitoring that the use does not become unpredictable.
- Prompts and controlled outputWe define the response format, tuna rules, refusals and quality criteria.
- Integration with the systemAIO must enter the application, CRM, portal, dashboard or website with logs and fallback.
Choice
What we can integrate with the help of AI
Not every AI integration has to be chatbot. Sometimes the value is in classification, summarization, data extraction or internal assistance.
Chatbot AI
Best suited when: website answers, leads, support or frequently asked questions.
Keep in mind: He needs sources and rules so he doesn't get the wrong answer.
Document processing
Best suited when: extract, classification, summary or validation assisted for repetitive documents.
Keep in mind: Critical data must be checked by man.
Internal assistant
Best suited when: search procedures, summaries, onboarding and support for the team.
Keep in mind: access to information must be segmented.
RAG
Best suited when: responses based on documents and controlled sources.
Keep in mind: sources must be cleaned and updated.
Quality signals
What we're looking at in a serious AI integration
The integration of AI shall be technically and commercially predictable. We are checking data, costs, quality of responses and scenarios in which the system must refuse or escalate.
- Choice of model and architecture after task, cost and risk.
- Prompts, validations and documented response format.
- Usage limits, cost monitoring and logbooks.
- Rules for sensitive data and access.
- Testing with real examples prior to launch.
Connection
Service works better when it's connected to the rest of the project
Any service works better when connected to the rest of the ecosystem: content, SEO, data, operation, hosting and maintenance.
- AI agents
- Document processing
- Sales automation
- Integration with AI platforms
Recommended pages
Pages related to this type of service: technologies, fields of activity, guides and complementary services.
Frequently Asked Questions
Before we begin the project
Choice, stages and support for integration with AI platforms.
01 / Choice
What's useful to know
Can you control the costs of AI?
Yeah, through limits, cache, matching models on task and usage monitoring.
Can we integrate AI into an existing application?
Yes, after a technical audit of the application and the flow into which the AIA enters.
02 / Planning
Purpose, budget and stages
What can the project include?
For AI platform integration, the starting points are: API integration; prompts structure; optional RAG; Limited rates; cost monitoring. In the offer we specify deliverables, limits and responsibilities; the list adapts to your situation.
How do we set the budget and the timetable?
We separate data preparation and integration from the cost of use. The volume, models, external services and the level of human verification influence the estimation; we start with a limited and measurable flow.
03 / Continuity
Verification and support
How do you verify the result before handover?
We use representative examples, including difficult cases, and compare results with validated answers or rules. We track errors, costs and required interventions: a fluent response is not proof of accuracy.
What happens after handover?
Updates of sources, adjustments of rules and reassessments of results may be required after launch. We set cost monitoring, usage limits and escalation to a person for sensitive situations.