Tepia adds AI features to existing apps without a rebuild using OpenAI and Anthropic APIs and RAG.
Tepia adds AI features to existing mobile and web apps without a rebuild, using OpenAI and Anthropic model APIs, retrieval augmented generation and a separate AI service layer that plugs into your current backend.
Every Tepia build runs through six phases, Discovery, Design, Development and Testing, Training, Launch and Support, with a milestone at each stage.
Read: Add AI to an Existing App
A separate AI service layer with retrieval augmented generation over your data plugs into your current backend, so there is no rewrite of the app.
Read: AI Strategy and Implementation Consulting
A first production AI feature with Tepia typically ships in 6 to 10 weeks, using feature flags so it can roll out to a small group of users first.
Read: AI Chatbot Development Trained on Your Company Data
HIPAA aware architecture, GDPR and CCPA practices, and no training on your data by default, with open models in your own cloud when data must stay private.
Read: How to Integrate AI Into an App: A Step by Step GuideIndustry figures Tepia plans around when scoping ai integration work.
About 78 percent of organizations now use AI in at least one business function, according to McKinsey's State of AI survey.
Gartner projected that more than 80 percent of enterprises would have used generative AI APIs or deployed AI enabled apps by 2026.
About 42 percent of enterprise scale companies have actively deployed AI in their business, per IBM's Global AI Adoption Index.
The most common question Tepia hears from product owners is some version of: we have an app with real users, we want AI in it, and we cannot afford to start over. You do not have to. Modern model APIs are called over HTTPS like any other service, which means AI can be added to a five year old app the same way you would add a payments provider.
Tepia’s approach is to build a separate AI service that sits beside your existing backend. Your app calls it for the new feature; everything else keeps working as before. The service handles prompts, retrieval over your data, model selection, caching, rate limits and logging.
This matters because AI is the fastest moving part of the stack. Tepia has written a companion guide on adding AI to an existing app that covers the architecture decisions in detail.
Not every AI feature earns its place. Tepia scores candidates on three questions: does it save a user real time, does it use data only you have, and can it be measured. The table below lists features Tepia builds most often, with the data they need and how long a first version typically takes.
| Feature | Good fit when | Data it needs | Typical build |
|---|---|---|---|
| In app assistant answering questions about your product or account | Support volume is high and answers live in docs or account data | Help content, policies, user account context | 6 to 8 weeks |
| Search that understands natural language | Users struggle to find items, records or content | Your catalog or records indexed in a vector database | 4 to 6 weeks |
| Summaries of long content | Users read reports, notes, transcripts or threads | The content itself | 3 to 5 weeks |
| Document and photo extraction | Staff key in data from invoices, forms, labels or receipts | Sample documents and the target schema | 5 to 8 weeks |
| Classification and routing | Tickets, leads or requests are sorted by hand | Historical items with their outcomes | 4 to 6 weeks |
| Personalized recommendations and nudges | Retention depends on users finding the next useful thing | Usage events and content metadata | 6 to 10 weeks |
| Drafting and rewriting (messages, listings, notes) | Users write repetitive text inside your app | Examples of good output, tone rules | 3 to 5 weeks |
| Voice input and transcription | Field or clinical users cannot type | Audio, vocabulary list | 4 to 6 weeks |
Tepia typically recommends starting with one feature from the top half of this table, measuring it for a release cycle, then adding the next. Apps engineered for retention get there through a sequence of useful features, not one large AI launch.
AI integration runs through Tepia’s six phase process, tuned for a product that already exists. Discovery is a system investigation of your current app, backend, data stores and integrations, plus user interviews to find the moments where AI would save time. Tepia reviews third party dependencies and model vendor terms. Deliverables are an Investigation Summary, an Interview Summary and User Stories for the AI features, ranked by value and data readiness.
Design covers the conversational or assistive interface: how the feature is discovered, what the user sees while the model is working, how uncertainty and errors are shown, and how users correct the output. Tepia produces wireframes and sample designs for these states because AI features fail on trust, not on model quality.
Development and Testing adds a step most vendors skip: an evaluation set. Tepia builds a collection of real prompts with expected outcomes and runs it on every change, so accuracy is measured rather than felt. Alpha and Beta releases go out behind feature flags to a subset of users. Training covers any admin tooling for prompts and content sources.
Each engagement has an assigned Tepia project manager, US based design and engineering leadership, and hand picked engineers working in US overlapping hours. Full process at tepia.co/process.
Plenty of firms will add a chat window to your app. Tepia’s difference is thirteen years of disciplined engineering on products that people keep using, combined with a focus on human intelligence: AI features designed around what your users are trying to do rather than around the model. Tepia’s apps average 4.5 stars on the App Store and Google Play, and the AI features Tepia adds are held to the same bar.
Tepia also knows the rest of your stack. Because Tepia builds full apps, integrations and backends on Swift, Kotlin, React Native, Flutter, React, Node.js, .NET and Python, the AI service fits your existing architecture instead of fighting it. Examples of Tepia’s work are at tepia.co/our-work and the full service list is at tepia.co/services. For a chatbot trained on company documents specifically, see AI chatbot development.
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