AI Integration Services for Existing Apps
You do not need to rebuild your product to add useful AI. We help you decide which AI features are worth shipping and which are hype, then integrate them into your existing web or mobile app, in production.
Start a ProjectWhat AI Integration Actually Means
"Add AI" is not a spec. When teams come to us, it usually breaks down into a handful of concrete feature types:
LLM Features
Summaries, drafting, rewriting, and natural language interfaces built into the workflows your users already have.
RAG & Knowledge Search
Retrieval-augmented generation over your own docs and data, so answers come from your content instead of the model's memory.
AI Copilots
An assistant inside your product that knows the user's context and can take actions, not just chat.
Workflow Automation
AI steps inside existing processes: triage, routing, classification, and drafts that a human reviews.
Data Extraction
Pulling structured data out of documents, emails, and images that used to require manual entry.
Recommendations
Personalization and suggestion engines that get smarter as people use your product.
How We Work on an Integration
- Assess the existing app. Architecture, data model, and where AI can plug in without breaking what works.
- Feasibility before commitment. A quick spike proves the feature is possible with your data before you fund a full build.
- Model and API selection. OpenAI, Anthropic Claude, Google Gemini, and vector databases where retrieval matters. We pick for the job, not the brand.
- UX design for AI features. Loading states, confidence signals, and graceful failure. AI features live or die on the interface.
- Backend integration. Our home turf is Rails backends and Flutter mobile, and we integrate with whatever stack you already run.
- Production monitoring and evals. Quality checks, cost dashboards, and regression tests so the feature stays good after launch.
Prototype vs Production AI Feature
Anyone can wire a model API to a text box in an afternoon. That demo hides the gap between "works in a demo" and "works for paying users." The gap is:
- Security. What data leaves your systems, where it goes, and what your customers agreed to.
- Cost control. Token budgets, caching, and model routing so usage growth does not surprise your finance team.
- Latency. Streaming, background jobs, and the right model size so users are not staring at a spinner.
- Hallucination handling. Grounding answers in your data, constraining outputs, and failing honestly when the model is unsure.
- Monitoring. Evals and observability so you find quality drops before your users do.
Closing that gap is most of the real work, and it is the part we are hired for. If you are building an AI product from scratch rather than adding features to one, see our AI app development service.
Who This Is For
- Founders with a live product whose users or investors are asking about AI
- Teams with an AI feature prototype that needs to survive production
- Businesses drowning in manual work that AI extraction or triage could absorb
- Products with a pile of docs and data that search cannot surface today
- Teams without an AI-experienced engineer who want senior help, not a hiring cycle. If you need ongoing technical leadership, that is our Fractional CTO lane.
We have shipped AI in production, in our clients' apps and our own. Browse the apps we have built, and read about how our retainers work.
Frequently Asked Questions
How much does AI integration cost?
It depends on scope. A focused feature like an LLM-powered assistant on top of your existing data can be a few weeks of work. A deeper effort involving RAG pipelines, workflow automation, and production evals can run several months. In the market, integrations commonly land in the $25K to $150K range depending on scope. We work on flexible monthly retainers, so scope can adjust as you learn what your users actually respond to.
Can AI be added to an existing mobile app?
Yes. Most AI features run server-side, so your mobile app calls your backend and the backend calls the model. That means the mobile release is often a normal feature release, not a rewrite. We ship Flutter on mobile and Rails on the backend, and we have added AI features to apps built by other teams too.
How long does AI integration take?
A single well-scoped feature typically takes four to eight weeks from assessment to production, including evals and monitoring. Multi-feature efforts, or apps that need backend work before AI can plug in, run longer. The assessment in the first week or two gives you a real timeline instead of a guess.
Do I need my own machine learning model?
Almost never. Most product AI today ships on foundation-model APIs from OpenAI, Anthropic, or Google, combined with your own data through retrieval and careful prompting. Training a custom model is expensive and rarely the right first move. If your product genuinely needs one, we will tell you, but the default answer is no.
What is the difference between an AI prototype and a production AI feature?
A prototype proves the idea on the happy path. A production feature handles the rest: security around your data, cost controls so a busy day does not blow the budget, latency users will tolerate, hallucination handling so wrong answers fail gracefully, and monitoring plus evals so you know quality is holding after launch. Closing that gap is most of the work.
Thinking About Adding AI?
Tell us about your app and what you want AI to do. We will give you a straight read on what is worth building, what it should cost, and what to skip.
Get Started