LLM integration: ChatGPT, Claude and Gemini in your system.
LLM integration means using ChatGPT, Claude, Gemini or other models directly in your company’s current system.
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Chat on WhatsApp nowLLM integration in the most used steps
Steps where a language model takes over reading, writing or interpreting text inside a system already in use.
Summarization and classification
Summarize and classify texts, tickets and documents, so your team reads less and forwards faster.
Content generation
Generate descriptions, emails and content from system data, with review before publishing.
Structured extraction
Structured extraction of information from PDFs and images, in fields ready to save to the database.
Assistant in the dashboard
Assistants inside the system dashboard that answer questions and perform user actions.
Translation and adaptation
Translate and adapt content for other languages, audiences or channels, keeping the defined tone.
Natural language search
The user asks in free text and the system queries its own data to answer.
Suggested responses
Suggested replies for agents, based on customer history and company policies.
Review and validation
Records, texts and documents checked against defined rules, with anything out of standard flagged.
Any step with text
If a system step depends on reading, writing or interpreting text, it can use a language model.
Tell us yours
From the chosen step to production.
Integration only goes live after passing tests with real data.
- 01
Mapping
We identify where AI really makes a difference in your current system. The focus is on steps where the language model reduces rework, automates manual tasks or frees the team for more relevant decisions.
- 02
Proof of concept
We test the model and prompt using real system data. Only what solves the problem in practice moves forward.
- 03
Integration
We implement directly in the current system: code, cache, usage limits and cost tracking are integrated into the stack already in production.
- 04
Production
We launch in the system, with ongoing monitoring of quality and cost. Fast adjustments if anything goes off track.
Beyond the API call.
- Model selection by task, cost and latency
- Versioned and tested prompts
- Automated quality evaluation
- Cache, limits and fallback across providers
- Usage and cost tracking by feature
- Sensitive data handling
Frequently asked questions
Do we need to rewrite the system?
No. AI is integrated into the existing stack, only in steps where it improves the process.
Which model is used?
Whichever offers the best balance of cost, quality and latency for each task: OpenAI, Claude, Gemini or open models.
Will the data train the model?
Data is not used to train the models. The integration uses APIs and usage modes where the content sent does not feed the provider’s model. Sensitive data is filtered before sending.
How is cost controlled?
The system tracks usage and cost by feature, applies limits and uses cache to avoid unnecessary calls. Monitoring starts as soon as it goes live.
What if a provider goes offline?
If a provider goes offline, the system can automatically use another model. This way, the feature keeps working without relying on a single provider.
ChatGPT, Claude or Gemini: which to use?
It depends on the task, volume and acceptable cost per token. We test models with real system examples and choose the one that delivers the needed quality at the lowest cost, keeping the integration ready to switch providers. As prices and limits change often, we compare using the official tables from OpenAI, Anthropic and Google at the project date.
How much does it cost per month?
The monthly cost follows the volume of calls and the size of texts sent to the model. Before going live, we estimate the spend with real data and set usage limits.
Where does integration usually start?
In a well-defined step, like classifying tickets or extracting data from documents. When the answer depends on internal documents, the path is AI with company data; when AI needs to interact and act, an AI agent; and when it is the core of the product, an AI-powered system.
Which system step can AI take over?
Let’s talk to choose the model and estimate usage cost?