AI models are the engines behind intelligent app features, from chatbots that mimic human conversation to tools that summarise reports or translate content in real time.

There are two main routes: off-the-shelf models (like GPT-4 or Gemini) and custom-trained models tailored to your data. Off-the-shelf models are fast, powerful, and often plug-and-play via APIs. They’re ideal for general use cases, and they keep costs low and speed to market high.

Custom models, on the other hand, are designed around your unique data. These are more private, more precise, and ideal if you're operating in regulated sectors or need domain-specific language understanding. They also come with more complexity, cost, and lead time.

Think of these as the Swiss Army knives of AI. Providers like OpenAI, Google, and Meta have trained large models on vast datasets, making them capable of performing a wide range of tasks with minimal setup. They're typically accessed via API and charged on a pay-per-use basis.

These are perfect if you need:

• Speed to market
• Low upfront cost
• General-purpose AI features

Use cases include chatbots, content summarisation, sentiment analysis, text classification, and basic image or voice recognition.

Stat to know: According to McKinsey, organisations that adopt AI at scale report a 20% or more increase in revenue in core areas where AI is used effectively.

These are either built from scratch or fine-tuned versions of existing models. The value here is precision. If you're handling specialist data (e.g. legal, medical, engineering), you need a model that truly understands your content.

Custom-trained models allow you to:

• Control tone and vocabulary
• Maintain data privacy
• Ensure accuracy for niche language

However, they require significant investment. You’ll need clean, annotated datasets, AI/ML engineers, compute power, and time. They’re best reserved for businesses with complex requirements and the scale to support them.

Most businesses fall somewhere in the middle. Hybrid approaches combine the speed of off-the-shelf tools with the control of custom logic.

A great example is Retrieval-Augmented Generation (RAG), where an app uses a pre-trained model but retrieves private data from your database to inform its responses. That way, the model isn’t trained on your content, but it can still serve users with your knowledge.

AI agents represent a significant leap beyond traditional bots. While a chatbot might give you an answer based on a script or predefined logic, agents work more like autonomous assistants. They’re not just reactive, they’re proactive, capable of holding memory, reasoning across multiple steps, and making decisions.

Think of an AI agent as someone who not only understands your instruction, but also figures out how to achieve it across different systems. If a user tells a travel app, “Book me a hotel in Paris near the Eiffel Tower with great reviews,” an AI agent can break that down, pull in data from different APIs, compare reviews, evaluate prices, and complete the booking, all without human intervention.

What separates agents from simpler AI tools is their architecture. They’re built to manage context across time, use multiple tools (such as databases and APIs), and work toward complex goals rather than single-step responses. This means they’re capable of everything from handling support tickets to orchestrating logistics.

Stat to know: 62% of consumers are open to using AI to improve customer experience if it helps resolve issues faster.

To get there, though, you’ll need a solid data pipeline, high-quality structured inputs, and frameworks that support long-term memory and tool orchestration (like LangChain or AutoGPT). It’s an investment, but in the right scenarios, it can dramatically reduce manual effort, improve speed, and increase user satisfaction.

AI workflows are step-by-step processes that use AI tools to execute specific tasks. These aren't end-to-end autonomous systems, but they can save hours of time and significantly reduce manual effort.

They’re especially helpful when:

• There’s a large volume of data
• The process is predictable
• Speed and consistency matter

• Email triage: Sorting incoming messages by category and urgency
• Recruitment filtering: Scanning resumes for key qualifications and red flags
• Claims processing: Extracting key fields from forms and routing based on conditions
• AI-assisted moderation: Detecting offensive content or flagged behaviour in communities

Omega Healthcare automated parts of its medical claims process using AI workflows, saving 15,000 hours per month with 99.5% accuracy. It’s not just efficient, it’s transformative.

• Emotion: Apologising for an overcharge or calming a frustrated user? That still needs a human.
• Edge cases: Unique queries that break the pattern or fall outside normal parameters
• Creativity: New feature ideation, product vision, and branding still need a human brain

The smart play is to start small. Identify bottlenecks or high-volume tasks, then run pilots with oversight. Measure the results, and scale accordingly.

Using third-party AI services is one of the most efficient ways to bring advanced functionality into your app, without building a team of machine learning engineers from scratch.

With tools like OpenAI’s APIs, Amazon Rekognition, and Google Cloud Vision, you can unlock capabilities like language understanding, image classification, speech processing, and recommendation systems. For example, a retail app could use vision APIs to identify products in user-uploaded photos or integrate sentiment analysis tools to understand customer reviews at scale.

Stat to know: The global AI market is projected to grow to $407 billion by 2027, with AI-as-a-service being one of the fastest-growing segments.

But integration isn’t just about technical feasibility. You also need to think about how well the service fits with your app’s performance requirements, privacy obligations, and long-term goals. Real-time features, for example, demand ultra-low latency, something not all providers can guarantee. And if you’re working with sensitive data, knowing where and how third-party models store information becomes crucial.

The real value comes when your development partner aligns these services with your business goals. That means choosing providers based on compliance requirements, optimising costs with caching strategies, and ensuring each AI feature enhances (not complicates) the user experience.

AI is often confused with automation. But while they sometimes overlap, they are not the same thing.

“If this, then that.” Simple logic-driven actions based on triggers. Great for:

• Email sequences
• Stock updates
• App notifications
• Data syncing between systems

“Given what I’ve learned so far, what’s most likely?” Great for:

• Personalised user flows
• Predictive alerts
• Dynamic content generation

Stat to know: Companies using both automation and AI report a 30% higher productivity increase compared to those using either alone.

Let’s say you run a health app:

• Automation sends a follow-up when someone misses a session.
• AI predicts who’s likely to fall off the plan based on activity trends and engagement.

Combined, they let you act intelligently and efficiently.

The smartest apps use automation as a foundation and layer in AI to optimise the experience. You don’t need to start with AI. You need to start with the problem and then use the right tool for the job.

AI won’t fix a weak product. But it can enhance a strong one.

It’s tempting to chase trends, but real innovation means focusing on outcomes. Do your users need faster answers, more personalisation, or better recommendations? Great. AI might be the right solution. But if all you need is to automate your onboarding emails, then keep it simple and start there.

We’ve helped apps across travel, health, retail, and beyond identify the most impactful use cases for intelligence and often, those start with automation and evolve from there.

What we won’t do is sell you AI for AI’s sake. We’ll ask the right questions. We’ll pilot before you commit. And we’ll build you something solid, scalable, and genuinely useful.

Because your app doesn’t need to be the smartest in the room. It just needs to be smart enough to serve your users better, faster, and more meaningfully than the rest.

Need help deciding where AI fits into your product roadmap? Let’s talk. We’ll help you understand what’s possible, what’s practical, and what’s worth it.