“We want to add AI to our app” is, today, one of the phrases we hear most in early client meetings. And it’s also, almost always, an incomplete phrase: artificial intelligence isn’t a single technology you bolt onto a product, it’s a family of distinct capabilities (each built to solve a specific kind of problem) that get combined depending on what the app actually needs to do.
Picking the wrong technology isn’t a minor mistake. Integrating a general purpose LLM when what you actually need is a system that retrieves your own information, or designing a fully autonomous agent when a simple conversational copilot would do, ends up costing more, taking longer, and often delivering a worse experience than the right tool would have given you.
This guide is meant to help you make that choice with a clear head. We walk through eight AI technologies we regularly apply when building mobile apps at Mobivery, backed by Nunsys Group’s dedicated Artificial Intelligence department, and for each one we explain what it is, when it makes sense to use it, and when it’s probably not the best fit. You can see these eight technologies summed up on our AI applied to product page. If you already know the general landscape, you can pair this guide with our post on the 10 AI capabilities redefining mobile applications; here we focus on the decision criteria, not just on describing each capability.
1. LLM Integration: the language engine behind the product
What it is. Integrating a language model (LLM) means hooking your app up to a model that can understand and generate natural language, to build assistants, copilots or conversational experiences tailored to the product. In a way, it’s the base technology: many of the other capabilities on this list (RAG, agents, voice) get built on top of an LLM.
When to use it. When the user needs to ask questions in natural language, when you want to cut the friction of a complex form by turning it into a conversation, or when in-app customer support can be handled with dynamically generated answers instead of a static FAQ tree. It’s also the starting point you can’t skip if you plan to move on to RAG or agents down the line.
When it’s not the best fit. If the interaction you need is simple and predictable (a three-step flow, a short form), an LLM piles on complexity and cost without adding real value: a well-designed traditional interface is still faster and cheaper. It’s also not a good fit if you can’t commit to properly defining the assistant’s boundaries: a poorly scoped LLM produces inconsistent answers that hurt user trust more than they help it.
At Mobivery we can build on Vekai, Nunsys Group’s own in-house LLM, to put together assistants and copilots inside your app without having to train a model from scratch, which improves the project’s time to market and rules out the investment a custom LLM would require. Vekai pulls all of a company’s information sources into a single workspace so people can find any resource faster, and lets you spin up a separate AI assistant for each department, organizing knowledge into independent spaces.
2. RAG Systems: when the answer has to be spot on
What it is. A Retrieval-Augmented Generation (RAG) system connects the language model to your company’s real knowledge (catalogs, policies, documentation, customer history), so every answer is grounded in verifiable, up to date data instead of whatever the model happens to “remember” from training.
When to use it. Whenever accuracy matters more than fluency: an assistant that reports on insurance coverage, rate conditions, real product stock, or a company’s internal policies. Also when the underlying knowledge changes often and retraining a model every time a document gets updated just isn’t practical.
When it’s not the best fit. If your use case doesn’t depend on proprietary or changing information (say, a creative writing or brainstorming assistant), setting up a RAG system is unnecessary complexity. It also doesn’t make much sense when the volume of documentation is small and stable: sometimes it’s enough to drop the information straight into the model’s context, without needing a full retrieval architecture.
Vekai, the Nunsys Group LLM we covered in the previous section, builds this in natively: its chat ties every answer back to your company’s real information sources through RAG, making sure what it answers stays current and sparing you from having to expose sensitive information to external model training.
3. AI Agents: when you need to act, not just respond
What it is. An AI agent doesn’t just respond: it plans out several steps and carries out real actions within your systems (processing a return, updating a record, booking an appointment), chaining decisions together without needing supervision at every step.
When to use it. When the goal is automating a full, multi-step process that today requires repeated manual work: incident management, order reconciliation, approval flows with clear rules. Agents deliver the most value the more repetitive and well defined the process they’re automating is.
When it’s not the best fit. When the task calls for ambiguous judgment, carries high risk, or has consequences that are hard to undo (critical financial decisions, irreversible actions on sensitive data), a fully autonomous agent isn’t wise without extra layers of human oversight. If your process isn’t well defined internally yet, it’s worth sorting that ambiguity out first: automating a messy process just makes it messy faster.
For Ouro, a fintech company specializing in payment and digital banking solutions, we designed an agent that guides a new customer’s onboarding from start to finish: it gathers the required documentation, verifies the user’s identity, and sets up the account without manual intervention, a process that carries particularly high stakes in a sector with strict regulatory compliance requirements.
4. Computer Vision: when the camera is the entry point
What it is. Computer vision lets the app interpret images, documents or video in real time: object recognition, identity verification, document reading, visual quality control or augmented reality.
When to use it. When the process you want to digitize starts, today, with a photo or a physical document: customer onboarding with identity checks, claims management backed by photo evidence, visual inventory in retail, or quality inspection in industrial processes. It’s especially valuable when it replaces a slow manual process that’s prone to human error.
When it’s not the best fit. If the data you need already exists in structured form (text, a database), adding a computer vision layer is one complexity too many. It’s also not the first priority when the case volume is low enough that the manual process is still efficient: the payoff from automating with computer vision grows with volume.
In industrial settings, we’ve worked on identifying, through cameras and artificial intelligence, whether technicians are wearing the right Personal Protective Equipment (PPE: helmet, gloves, goggles, harness) before entering a risk area. Bringing this capability into a mobile app makes sense because it turns the worker’s or supervisor’s own device camera into an instant checkpoint: instead of relying only on fixed cameras installed on the plant floor, any access to a risk area can be verified on the spot, from the phone, and logged automatically as compliance evidence, with no need for a supervisor to review every access manually.
5. Personalization: when every user needs something different
What it is. AI-driven personalization automatically adapts content, recommendations, or even the screen’s own layout, to each user’s behavior, preferences and context, in real time.
When to use it. When your app has enough user volume and behavioral data for distinct patterns to emerge across segments, and when content relevance directly drives business metrics like conversion or retention: e-commerce, content, consumer goods. The wider and more varied the catalog or content, the more value personalization adds.
When it’s not the best fit. With little user volume or historical data, a personalization engine has nothing to learn from, and the result will be unreliable or outright counterproductive. It also doesn’t fit well in products where predictability is an advantage (professional tools where users expect to find the same thing in the same place every time), because personalization can introduce friction instead of cutting it down.
In a new e-commerce app we’re building, embedded inside an event ticketing app, AI recommends events and products based on the user’s past purchases, so each person sees a different catalog tailored to their actual interests instead of the same generic list everyone else gets.
6. Multimodal AI: when a single format isn’t enough
What it is. Multimodal AI combines text, image, voice and other formats within a single interaction flow: a user can, for instance, photograph something and get a spoken answer back, or describe what they need out loud and receive a generated image instantly.
When to use it. When the user experience genuinely improves by combining formats: design or interior decorating apps where describing and seeing go hand in hand, accessibility tools that translate between formats depending on the user’s needs, or support flows where a photo explains things faster than a long block of text.
When it’s not the best fit. If the use case is fully solved with a single format, mixing in several just adds technical and interface complexity with no real benefit for the user. Multimodality should answer a genuine experience need, not get used as a technical showcase.
Our in-house LLM, Vekai, lets users combine image uploads with text within the same conversation: someone can, for example, attach a photo of a document, a product or an issue and describe in their own words what they need, and get back an answer that takes both formats into account at once, instead of processing them separately.
7. Voice Interfaces: when hands or eyes are already busy
What it is. Voice interfaces let users talk to the app in natural language to look up information or carry out actions, with a naturalness and a latency that now make full flows possible, not just simple commands.
When to use it. In hands free contexts (driving, fieldwork, industrial settings), in accessibility cases for users with visual or motor difficulties, and in markets where a digital literacy or language barrier makes voice feel more natural than typing.
When it’s not the best fit. In noisy environments where speech recognition loses reliability, in tasks that need high numerical precision (entering exact figures, for example) where a keyboard is still more trustworthy, or in public settings where talking to a phone isn’t comfortable or private for the user.
In a field sales app, a rep can describe, in natural language and their own voice, how a visit went, and the AI automatically sorts that information into the right fields of the form they need to fill in. When it picks up that some data is missing or what was given isn’t precise enough, the AI itself asks follow up questions by voice to fill in the gaps, instead of leaving the form half done or making the rep go back and check every field by hand.
8. On-device AI: when privacy and latency leave no room for exceptions
What it is. On-device AI runs models directly on the device’s chip, without sending data to a server: instant processing, offline operation, and a guarantee that sensitive information never leaves the phone.
When to use it. In sectors with especially sensitive data (health, banking, identity), in features where network latency isn’t acceptable (real time recognition while the user moves the camera), or in apps that need to keep working offline in areas with patchy coverage, such as fieldwork or logistics.
When it’s not the best fit. On-device models are more limited in size and capacity than the ones running in the cloud, so if the task needs the full power of a state of the art model (complex reasoning, high quality generation), cloud processing is still the better call, with on-device reserved for the parts that are most time critical.
In that same field sales app we talked about under voice, the rep can record their comment even with no connection, since the initial processing happens right on the device and the information syncs asynchronously as soon as coverage comes back. This matters a lot for this kind of app because the work happens on the move, between visits, where mobile coverage isn’t always great, and reps can’t afford to lose, or have to redo, a visit’s worth of information just because there was no signal at that moment.
How to choose: a quick way to decide
You don’t need to pick just one technology: most projects combine several, each one solving a piece of the problem. As a quick starting point, here are the questions we ask ourselves at Mobivery when kicking off a project:
- Does the user need to ask questions in natural language? → LLM Integration, and likely RAG Systems if answers need to draw on your own information.
- Is there a repetitive, multi-step process that’s still done by hand today? → AI Agents.
- Does the process start with a photo, a document, or a video? → Computer Vision.
- Is there enough user volume for distinct behavior patterns to emerge? → Personalization.
- Does the experience get better by combining text, image or voice? → Multimodal AI.
- Are the user’s hands busy, do they face accessibility challenges, or would they rather talk than type? → Voice Interfaces.
- Is the data especially sensitive, or does the app need to work offline? → On-device AI.
In more mature projects, these technologies don’t compete with each other, they work together. A good example is a copilot (LLM Integration) that answers accurately thanks to RAG, that can be triggered by voice (Voice Interfaces), that reads documents through Computer Vision, and that carries out certain actions autonomously as an agent (AI Agents), all of it personalized (Personalization) for each user and, wherever privacy calls for it, processed right on the device (On-device AI).
Judgment matters more than the trendiest technology
The right question is never “which AI technology is the most advanced?”, it’s “what problem do I have, and which technology solves it with the least complexity?” Adding AI to an app isn’t a goal in itself: it’s a tool that, chosen well, improves a specific experience or process, and that, chosen poorly, piles on cost and complexity with no real payoff for the user.
At Mobivery we approach every project by starting with that question, not with the technology. We bring the experience of our mobile development team together with the backing of Nunsys Group’s dedicated Artificial Intelligence department, which lets us recommend, and build, within our Build service, the mix of technologies that genuinely fits each client’s problem, rather than forcing a use case to match whatever technology is trending. If you’d like to see how we weave these technologies into our own development process, you can also read how we build a mobile app with AI, phase by phase.
Want to figure out together which AI technology fits your project?
