LLM integration
An LLM built into the applicationsyou already have.
LLM integration means adding language-model features (summarising, drafting, classifying, filling in fields) at exactly the step in your CRM, ERP, helpdesk or in-house application where your team loses time, without changing the application or the way people work.
Your team copies text between the application and an AI chat
In many companies AI is already in use, but alongside the systems: someone copies an email into a public chat, gets a summary and pastes it back. It is unclear what data leaves the company, what it costs and what happens when the answer is wrong.
- Customer data ends up in uncontrolled AI tools, with no rule about what is allowed
- Every colleague writes different instructions, so results vary from person to person
- Nobody knows what usage costs or whether a smaller model would be enough
- When the model does not respond or gets it wrong, the process stalls because there is no fallback
Who it is for
- Companies that already have a CRM, ERP, helpdesk or internal application and want a specific step to be assisted by AI
- Product teams that want to add AI features to their own customer-facing application
- Managers who have seen colleagues using a public AI chat and want the same help inside the company's tools, with clear rules
- Companies with sensitive data that need a reasoned decision about which model they use and where it runs
What LLM features your application can get
- 01
In-context summary
A button that summarises a ticket, a conversation or a customer record right on the screen your colleague is working on.
- 02
Draft reply
Suggests an email or message based on the history, which your colleague reads, corrects and sends.
- 03
Classification and tagging
Suggests the category, priority or department for a new request, which can be corrected in one click.
- 04
Field filling
Extracts from free text the data that needs to go into a form, and the user simply confirms it.
- 05
The right model for the task
Simple tasks go to a cheaper, faster model; complex ones go to a more capable model.
- 06
Fallback behaviour
If the model does not respond in time or the result fails the checks, the application reverts to the usual workflow.
How it works
How an integrated LLM feature works
The steps marked in yellow are approved by a person.
- Input
- Action
- AI
- Human in the loop
- Result
- 01
Input
Action in the application
The user clicks a button or a new request enters the system.
- 02
Action
Context from the system
The application gathers only the data the task needs, filtered by the user's permissions.
- 03
AI
Generation with the chosen model
The model suited to the task produces the summary, draft or classification in the required format.
- 04
Action
Automatic checks
The result is validated for format and content; if it fails, the fallback applies.
- 05
Human in the loop
User confirmation
Your colleague accepts, edits or rejects the suggestion before it is saved.
- 06
Result
Saved in the system
The approved result goes into the application, and the user's response is recorded.
What information LLM integration needs
- The data from the existing application that the task needs: tickets, customer records, orders
- Written instructions for each feature, covering tone, format and rules
- Real examples of good results, used to test the models
- The list of data that must not reach the model provider
- The usage budget and the acceptable response time
Possible integrations
- Salesforce
- HubSpot
- Pipedrive
- Zendesk
- Freshdesk
- Microsoft Dynamics 365
- Odoo
- In-house applications via REST API
- OpenAI API
- Anthropic API
- Salesforce
- HubSpot
- Pipedrive
- Zendesk
- Freshdesk
- Microsoft Dynamics 365
- Odoo
- In-house applications via REST API
- OpenAI API
- Anthropic API
Each integration's compatibility is checked during the analysis phase, for the systems and versions you use.
Control and transparency
What stays a human decision
- Any text sent to customers that started from a generated draft
- Correcting wrong classifications, which become examples for fine-tuning the setup
- Deciding which data may reach the model provider
- Switching an AI feature on for all users, after the trial period
Limitations and how we handle them
- The model can state wrong information convincingly; automatic checks catch some of these cases, which is why user confirmation remains the final step
- Cost depends on volume and text length, so the initial estimate is recalibrated after the first few weeks of use
- Quality in Romanian varies between models and is checked on your examples, not on public leaderboards
- Integration depends on what the existing application allows: an API, extensions or data access
- Providers update or retire model versions, which may require retesting
How we measure integrated LLM features
We agree the indicators together before launch and track them afterwards. The values depend on the process, the data and the volume.
- Suggestion acceptance rate
- How often users accept the result unchanged, edit it or reject it — recorded automatically in the application.
- Quality on the test set
- Model results compared with good examples chosen by your team, before any change of model or instructions.
- Cost per task
- Average usage and cost per action, tracked monthly against the agreed budget.
- Response time and fallback
- How long each request to the model takes and how often the fallback is triggered.
How we integrate an LLM into your application
- 01
Choosing the process step
We identify the point in the workflow where AI helps most and agree together on what a good result looks like.
- 02
Comparing models
We test several models on your examples, for quality in Romanian, cost, speed and data-processing terms.
- 03
Integration into the application
We build the feature, the checks and the fallback directly into the interface your team uses.
- 04
Pilot and roll-out
We start with a small group, track acceptance and costs, then expand gradually.
Use cases
These scenarios are conceptual examples showing what can be built. They are not delivered implementations or results achieved for clients.
- Building materials distribution
Customer summary in the ERP
The sales rep sees, in a panel, a summary of the customer's history and a draft reply to their latest request, which they edit before sending.
Conceptual example - Courier company
Complaint classification in the helpdesk
New complaints get a category and priority suggested by the model; the operator confirms or corrects them in one click.
Conceptual example - SaaS platform
AI feature in the product
The application's users get suggested descriptions, with a smaller model for short texts and a more capable one for long texts.
Conceptual example
Frequently asked questions
What does integrating an LLM actually mean?
It means a language model, for example via the OpenAI API or the Anthropic API, is called from your application for a specific task: summarising a ticket, suggesting a reply, classifying a request or filling in a form. The user sees the result on their usual screen and confirms it.
How is it different from AI API integration?
LLM integration starts from the process: which feature the application gets, what the result looks like and where a person confirms it. AI API integration is the technical layer underneath: keys kept on the server, usage limits, logs and switching providers. A project usually includes both, in different proportions.
How do you choose the model?
We test several models on real examples from your business and compare quality in Romanian, cost, speed and data-processing terms. We do not start from a preferred provider.
What data reaches the model provider?
Only the fields the task needs, defined explicitly. The rest can be excluded or masked before sending, and for sensitive data we also consider privately hosted models. The legal basis for processing personal data is agreed with you, in line with the GDPR.
What happens if the model is unsure or does not respond?
The result can be flagged as uncertain or, if it fails the checks or does not arrive in time, the application reverts to the usual workflow. The user can carry on working without AI.
Can we keep costs under control?
Yes, through usage limits, a smaller model for simple tasks and reuse of results already generated. The actual cost is tracked monthly and compared with the budget.
Do we have to change the application we use?
Not necessarily. If the application has an API or supports extensions, the feature is added to the existing interface. We check the options during the initial analysis.
Can’t find the answer? Write to us and we’ll reply directly.
Want AI right inside the application your team uses?
Tell us which application you use and where time is lost. We will suggest the feature, the right model and what the fallback looks like.
Related solutions
- AI API integrationConnecting AI providers' APIs to your systems, with keys kept on the server, limits, logs, cost control and a fallback provider.
- AI evaluation and monitoringTest sets, checks at every change, human spot-check reviews and cost tracking, so you know how your AI performs over time.
- AI agent for customer supportAnswers customers from approved information, checks order status in your systems and hands over to colleagues the cases that need a person.