AI features worth adding
You don't need to train your own model to put AI in your app. Most useful AI features today are built by connecting to existing models and services — a chat assistant, a search that understands meaning, image recognition, transcription, or recommendations that learn from behavior.
We integrate these capabilities into your app in ways that solve a real problem for your users, rather than adding an 'AI' badge that doesn't actually make anything better.
- Conversational assistants and chat built on language models
- Smart search that understands intent, not just keywords
- Image and document understanding (recognition, extraction)
- Speech-to-text and text-to-speech
- Recommendations personalized to each user
- Summarization and content generation where it fits
Doing it responsibly
AI features come with responsibilities that non-AI features don't. Models can be confidently wrong, they can be expensive per request, and they often send user data to an outside provider. Ignoring any of these leads to bad experiences, surprise bills, or privacy problems.
We design AI features with those realities in mind: guardrails for wrong or unsafe outputs, cost controls, and clear handling of what user data goes where, so the feature is trustworthy and not a liability.
- Guardrails for incorrect or inappropriate model output
- Cost controls so per-request pricing doesn't run away
- Clear handling of what user data is sent to AI providers
- Fallbacks for when the AI service is slow or unavailable
- Honest UI that doesn't overstate what the AI can do
An honest take on what AI can and can't do
We won't oversell AI. It's genuinely useful for language, patterns, and unstructured data, and it can make an app feel dramatically better when applied to the right problem. It's also not magic — it makes mistakes, and it's the wrong tool for tasks that need guaranteed-correct answers or simple deterministic logic.
We help you tell those apart, so you invest in AI where it pays off. A free consultation is the place to pressure-test your AI idea before building it.
- Applying AI where it's strong: language, patterns, unstructured input
- Avoiding AI where a simple, deterministic rule is better
- Being clear about accuracy limits with your users
- Prototyping the feature before committing to a full build
- Cost driven by the models used, usage volume, and complexity
More on app development
Frequently asked questions
Do I need to train my own AI model?
Almost never for a typical app. Most valuable AI features are built by connecting to existing, capable models through their APIs, which is far faster and cheaper than training your own. Custom training only makes sense in specialized cases with unique data, and we'll tell you honestly if yours is one of them.
How much do AI features cost to run?
Many AI services charge per request or per unit of usage, so ongoing cost scales with how much your users use the feature — unlike a fixed one-time build. We design with cost controls and estimate the usage economics with you up front, because an AI feature that's popular but unprofitable is a real risk worth planning for.
What about accuracy and users' data privacy?
Both need real attention. AI models can be confidently wrong, so we add guardrails and are honest in the UI about limits. And since many AI features send data to an outside provider, we're deliberate about what user data goes where and make sure that's handled and disclosed properly, especially for anything sensitive.