We integrate AI into applications to offer new features
AI integration in custom applications and platforms
We add artificial intelligence to existing software and develop new products that incorporate it.
We integrate AI into web platforms and mobile apps
There are two ways to add AI to software: improving an existing application or developing a new product with services that incorporate it.
New products with AI services
We offer custom software development services, now integrating AI features that enhance both web applications and mobile apps.
AI features in existing tools
The platform or tool stays the same, and AI handles the points where the user has to intervene today: classifying, searching, generating text or graphic assets. Users gain new features without switching programs.
New AI-based products: what to decide before programming them
When AI is the product's main function, there are decisions that do not come up in a conventional application and that determine its cost and its viability.
The cost of each use, in the business model
Every generated image or processed text has a cost in the model. It is calculated from the start and reflected in the prices or in the usage limits, so that user growth does not make the product run at a loss.
Which model and which provider
The model chosen is the one that delivers the required quality at the lowest cost, for text or for images, and models are compared again when a better or cheaper one appears.
Quality measured with real cases
Before launch, the product is tested with real examples and what counts as an acceptable result is defined. A demo that works with three cases does not guarantee that it will work with a thousand.
Limits and abuse control
Usage limits per user, content filters, and a log of every request, to control the cost and prevent misuse.
Transparency and the EU AI Act
Depending on the type of application, the AI Act requires informing users that they are dealing with an AI or that a piece of content has been generated by an AI. This is taken into account in the design, not at the end.
A first phase that goes to market
A minimum viable product is defined with a fixed scope and price and then launched, and the next phase is decided with real usage data.
AI features that can be integrated into an application
These are the points where, until now, a tool needed a person to intervene. AI handles them without replacing the program, which still controls, validates, and stores the data.
Automatic classification and tagging
New information is sorted into categories and given its tags as it comes in, without anyone having to review it item by item.
Data extraction from documents
Dates, amounts, and other fields from invoices, emails, or PDFs go into the application's fields without being typed in.
Natural language search
Information is found by asking a question, without setting up filters or knowing the exact word.
In-app assistant
It answers questions and guides the user through the software, using the application's own documentation.
Writing, summaries, and reports
Descriptions based on the data in a record, draft replies that a person reviews, summaries of documents and conversations, and plain-language reports based on the data in the system.
Detection of errors, duplicates, and anomalies
Inconsistent data, repeated records even when they are not identical, or unusual behavior, flagged so that a person can decide.
Prioritization and next step
Tasks and incidents are ranked by urgency and impact, and the application suggests the next step based on the context.
Computer vision
Recognizing objects, reading the text in an image, or detecting defects in a photo.
Image generation
Product images, variants of a design, or illustrations created from a description or a reference photo.
Transcription and translation
Voice notes, calls, or meetings converted into text, and content translated into other languages.
Feedback analysis
Satisfaction, complaints, and recurring topics in comments, reviews, or surveys, identified automatically.
Prediction and recommendations
Forecasts of sales, bookings, or stock based on historical data, customers at risk of leaving, and product or content recommendations for each user.
A basic, useful example of how AI can be integrated into an application
Sorting a catalog with criteria written in one sentence
Before: one rule for each case
To decide where each product goes, it had to be written as a condition: if the product family is this one and the supplier is that one, then this category. Each exception added another rule. After two years, changing them was risky.
Now: the criteria are described
"Seasonal items go first, items low in stock are not shown on the home page, and own-brand items come before resale items." Written like that, in one sentence, by someone who knows the business but not how to program.
The system applies the criteria
It classifies each product and records why it did so. When the criteria change, the sentence is changed, not the code, and there is no need to wait for the next version. The same works for documents: with the rules of a document management system written down, the AI suggests the folder for each file when it is uploaded.
A person makes the final decision
The result is reviewed and corrected from within the tool itself. If a correction keeps recurring, the sentence with the criteria is adjusted. It is integrated into the company's catalog management system, for example Pimcore.
Integrating a chatbot lets the platform guide the user
A chatbot inside your application, with your data and your permissions
It does not reply with ready-made phrases
Traditional chatbots reply with predefined texts; if the question was not anticipated, they redirect to a form. This one looks for the answer in the manuals, the procedures, the incident history, and the application's own database.
It shows the source of each answer
Each answer links to the document or record it comes from, so that the person asking can check it. If it does not find any related document, it says so instead of answering: that way it does not make up the answer.
It queries the data and does what it is asked
It answers "which case files have had no activity for more than a month?" with the list of records, and turns "customers who have not bought anything in six months and have an open incident" into the application's filters. The person asking reviews the result before using it.
It takes the current screen into account
If the question is about how to use the application, it answers with the application's own documentation, taking into account the screen the person asking is on.
With each user's permissions
This is what sets it apart from pasting the documentation into a public chat: the chatbot only accesses what that person can see. If someone does not have access to a case file, they cannot look it up through the chatbot either.
Validating with AI: required is not the same as correct
It is the smallest change of all, and the one that keeps the database from filling up with records that are of no use.
What a form validates
That the field is not empty. That it is a number. That it has the format of an email address, a tax ID (such as the Spanish NIF), or a date. That it is within a range. None of these checks prevents someone from typing "no description", a dash, or "same as the previous one": the field is filled in and the record is saved.
What AI can validate
That the description actually describes the product. That the address is plausible. That one field does not contradict another in the same record. That the text is in the right language. And it does not block anything: it warns whoever is filling in the form, at that moment and not six months later.
Sometimes it is hard to search using a single concept
Search by intent, not by the exact word
A conventional search engine finds the records that contain the words typed. With AI, the search interprets the intent: someone who searches for "problems with the invoice" also finds the incident that says "duplicate charge on the receipt".
There are two ways to achieve this, and the choice depends mainly on one question: can the current database be modified?
If the database cannot be modified: AI expands the query
Before searching, the AI transforms what has been typed: it generates synonyms and related terms ("invoice", "receipt", "charge", "payment") and turns them into the filters that the application's search engine already understands, which searches for all of them. The database is not modified. In exchange, each search takes a little longer and has a small cost for the query to the model, which is reduced by storing repeated searches.
If an index can be added: search by meaning in a vector database
Each record is converted in advance into a vector that represents its meaning and stored in a vector database. The query is converted in the same way and the most similar records are retrieved, with no need to generate synonyms, and this can be combined with traditional text search. It responds instantly and with no cost per search, but the index has to be added and all the records processed once.
Even without access to the source code
How AI is integrated into an existing application
By modifying the code
If the source code is available, AI is programmed as one more function of the application, which calls it when it needs it, the same way it calls a payment gateway. The screens, the data, and the permissions stay the same.
By connecting to the database
Without access to the code, AI can work on the application's database: it reads the records and writes the result, for example a classification or a completed field, into the fields agreed on. Changes that modify data are confirmed by a person.
By connecting through an API
If the application offers an API, AI connects to it like any other program, without modifying the code or accessing the database. It is the safest option with software from another vendor, and the same one used in system integration.
AI only where the wait is worth it
Not everything has to go through AI. It is used for the tasks that would take the user a long time, where waiting a few seconds pays off. And not all AI takes the same time: a generative AI writes the answer and needs a few seconds, while a decision AI, such as Jev, only classifies or chooses an option and responds much faster.
Interchangeable models, commercial or private
The application does not connect to a specific model but to an intermediate platform: for example, LiteLLM for text and reasoning models and Fal.ai for image generation. Switching models is a configuration change, not a programming one, and with sensitive information the request goes to an open-source model on private servers, in a private AI. We explain how the model for each project is chosen in our section on AI models.
Logging and auditing of every operation
Every AI action is logged and can be audited: what it was asked, what it answered, what data it used, and who validated the result. This makes it possible to check that it works, correct it, and justify each decision. For complete processes, it is combined with automations or with agents.
Frequently asked questions about AI integration in applications
Which feature should be integrated with AI first?
Does a model have to be trained with the application's data?
Can AI be integrated into Odoo or another ERP or CRM?
How much does it cost to integrate AI into an application?
What AI feature does your application need?
Tell us what product you want to launch, or which tool your company uses that you would like to improve by integrating artificial intelligence.