AI Implementation and Integration
Custom AI to automate your team’s work.
More than 100 projects. Developing software since 2012
Our unique system combined with AI delivers faster results.
Trusted by major brands, SMEs, and startups
Oriol Lanuza
Backend development and DevOpsThe true power of AI lies not in the model, but in the context. Our job is not to reinvent intelligence, but to build the necessary technical bridges so that this intelligence can understand your data and solve real business problems.
5-star rating on Google
Member of Tech Barcelona
AI connected to your business
Automate tasks within the tools your team already uses.
Implementing and integrating artificial intelligence makes it possible to automate repetitive work: reading documents even when each one arrives in a different format, preparing information and connecting with business applications. We start with a specific task and real examples to check whether it adds value.
We define what the model can read, what it can change and when a person needs to step in.
At Quiralis, we built document AI to extract information from insurers' authorizations, which the administrative team then reviews.
From the first test to daily use
We check what AI implementation brings before extending the integration. We define data, permissions and evaluation criteria with your team.
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We choose a specific task
We identify the work you want to reduce, how it's done today and whether automating it is technically feasible. We agree on what needs to improve and which examples will be used to verify it.
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We prepare data and access permissions
We review the available sources and the necessary permissions. We define what information the system can consult and what actions it can execute.
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We test with real cases
We compare the results with representative examples, including errors and incomplete data. We assess quality, time and cost before proceeding.
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We define human review
We decide what the system can do automatically and what needs validation. We prepare the handling of exceptions and the correction of results.
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We integrate the solution
We connect functionality with software and workflow. We manage permissions, connection errors and operation tracking.
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We support ongoing improvement
We agree on maintenance and monitoring of quality and costs. We review changes in models or suppliers before incorporating them.
Experience in software, applied to AI
Integration must work on a daily basis.
We have been building custom software since 2012, and that experience helps us integrate AI with the business's data, permissions and tools.
We set privacy measures according to the data and the provider, and review their terms of use, retention and training before going into production.
We choose the technique to fit the task: language models for natural language processing (NLP), machine learning when there's data to train a dedicated model, or AI agents to coordinate actions with clearly defined permissions.
Related projects
We create custom digital products to drive our clients growth.
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go to Over ten years building a consultancy's custom softwareBabià One
Over ten years building a consultancy's custom software
We answer your questions about AI integration
We understand that the adoption of Artificial Intelligence generates uncertainty about privacy, costs and technical feasibility.
A specific task, evaluated results and human oversight.
Many that could not be automated until now because they required reading, interpreting or summarising information with no fixed format. We have applied it to document processing, meeting summaries and product catalogue enrichment. The team stops spending time on repetitive work and keeps the review of the most sensitive tasks.
We select the service and configuration according to data sensitivity. Before integration, we review data processing, retention and training terms, along with access controls and encryption. These conditions depend on the product, contract and configuration.
RAG retrieves information from your documents and systems for the model to use in its responses. It is useful when the model needs company-specific knowledge. It can reduce unsupported responses, but evaluation and controls remain necessary because the model can still make mistakes.
It depends on the task, the available data, the integrations and the controls that need to be implemented. We separate the development from the recurring costs of models, infrastructure and maintenance. A limited pilot helps to estimate the consumption before expanding the solution. We tune the model and configuration so the cost of each task stays in proportion to the value it brings.
We use third-party models when they meet the project's requirements. We compare quality, cost, privacy and maintenance before choosing between integration, model adaptation or other approaches.
When there is a specific task, representative examples and a way to evaluate the result. A pilot lets you compare AI with the current process before investing further. If simple rules are enough or suitable data is unavailable, we consider other ways to automate.
Yes. We can agree on quality and cost monitoring, instruction review, and adaptation to changes in models or APIs. The scope of maintenance is defined according to the use and needs of the project.