AI and data · AI apps
AI app development company for products where AI does the core job
We design and build web and mobile apps where AI is the product, not a chat bubble bolted on: the workflow around the model, retrieval over your data, agents that take actions, voice where it helps, and the evaluation, guardrails and cost controls that keep it working after launch.
Short answer
An AI app is a web or mobile application whose main value comes from AI models: reading documents, answering from your data, generating content, taking actions or holding a conversation. Innovation Insight is an AI app development company that builds them end to end. An AI-first MVP costs $25k to $60k over 10 to 14 weeks; adding AI features to an existing product costs $8k to $60k; a full multi-role AI product costs $50k to $150k+. Running costs depend on model and volume.
Reviewed by Zain Khalid Malik, CTO & Co-founder · Updated
What counts as an AI app
| Type | What the AI does | Example from our work |
|---|---|---|
| Document intelligence | Reads long documents and extracts structured data for review | GovDoc AI: 50+ data points from government solicitations via RAG on AWS Bedrock |
| Guided AI consultant | Leads a user through a method in conversation, with voice in and out | Treasure Island AI: a React Native app that runs a seven-dimension business consultation |
| AI content generation | Interviews people and turns their answers into finished writing | Chat Your History: web chat, SMS and phone interviews turned into memoir chapters |
| AI search and recommendations | Understands intent and similarity rather than keywords | Cuts Like A Knife: natural-language music search and audio similarity with OpenL3 embeddings |
| Automated processing | Extracts data from documents at volume, with retries and dead-letter handling | Edge OCR: multi-tenant AI document processing with usage-based billing |
| Agents and voice | Takes actions in your systems or on the phone | See AI agent and voice agent development |
If you only need AI inside an existing product, such as a summary button, smart search or a support assistant, that is LLM integration and costs less. This page is for products where the AI is the reason people use the app.
How we build an AI app
- Discovery ($3k to $8k, one to two weeks): the user journey, where AI adds value and where plain software is safer, data sources and permissions, model options, an evaluation plan and a fixed estimate.
- Evaluation set before code: 50 to 200 real examples of inputs and good outputs, so every prompt, model or retrieval change is scored, not judged by eye.
- Thin vertical slice: one end-to-end flow on the real model with real data in the first two to three weeks, to prove quality and latency before building the rest.
- MVP: the full app around the model, with accounts, payments, admin, human review where accuracy matters, guardrails, logging of every model call and a cost budget per user.
- Launch and tune: watch real conversations, grow the evaluation set from failures, route easy requests to cheaper models and turn on caching.
Choosing the model
| Option | Strengths | When we use it |
|---|---|---|
| OpenAI GPT models | Broad capability, strong tool calling, large ecosystem | General assistants, agents, structured extraction |
| Anthropic Claude models | Long context, careful instruction following, strong writing and analysis | Document-heavy apps, drafting, coding tools; available on AWS Bedrock and Google Vertex AI |
| Google Gemini models | Multimodal input, large context, competitive pricing | Image, video and audio understanding; Google Cloud environments |
| Open-weight models | Run in your own cloud, no per-token vendor fees | Strict data residency, very high volume, on-premises requirements |
| Small or specialised models | Fast and cheap for narrow tasks | Classification, routing and extraction at scale |
We design the app so the model sits behind one interface and can be swapped by configuration: vendors release and reprice models every few months, and the cheapest model that passes your evaluation set is usually the right one. Data residency often decides the vendor; on GovDoc AI, Claude runs through AWS Bedrock so documents stay in the client's AWS account.
What an AI app costs
| Engagement | Cost | Timeline |
|---|---|---|
| Discovery with prototype and fixed estimate | $3k to $8k | 1 to 2 weeks |
| AI feature in an existing product | $8k to $60k | 4 to 12 weeks |
| AI-first MVP (web or mobile) | $25k to $60k | 10 to 14 weeks |
| Full AI product with several roles and integrations | $50k to $150k+ | 4 to 8 months |
| Dedicated AI team | From $4,000 a month per senior engineer | Ongoing |
Running costs are separate and usually smaller than people expect: a support-style chat on a small model costs well under a cent per conversation. Use the AI agent and chatbot cost calculator for your volume, the app development cost calculator for a feature-level build estimate, and our guide to the cost of an AI app like ChatGPT.
Risks we design for
| Risk | Control |
|---|---|
| Wrong or invented answers | Retrieval with citations, answer-only-from-context prompts, evaluation sets, human review on high-stakes outputs |
| Prompt injection and data leaks | Permission checks before retrieval, tool allow-lists, output filtering; we test against the OWASP Top 10 for LLM applications |
| Runaway model bills | Per-user and per-day token budgets, model routing, caching and batch for offline work |
| Vendor changes and deprecations | Model behind one interface, evaluation set to re-qualify a new model in a day |
| Privacy and regulation | Data processing agreements with each provider, regional hosting, no training on your data, HIPAA or GDPR controls where they apply |
For governance, we map risks and controls against the NIST AI Risk Management Framework when a client's compliance team asks for it, and document which model sees which data. Whether to fine-tune or use retrieval is covered in RAG vs fine-tuning.
Sources and further reading
Next step
Tell us what you're building and get a written estimate.
A senior engineer replies within one business day. NDA on request.
Products we've shipped, and what happened next.
Case studies written from the technical documentation of each project: the stack, the scale and the outcome.
Questions we get asked a lot.
How much does it cost to build an AI app?
An AI-first MVP costs $25k to $60k over 10 to 14 weeks with our team; adding AI features to an existing product costs $8k to $60k; a full AI product with several roles costs $50k to $150k+. Model usage is billed separately and is usually small at MVP volumes.
Which AI model should my app use?
The cheapest one that passes your evaluation set for quality and latency. We usually test models from OpenAI, Anthropic and Google side by side, and keep the model swappable by configuration.
Can you build AI apps for iOS and Android?
Yes. We build AI mobile apps in React Native or Flutter with the AI logic on a server, as on Treasure Island AI, which added voice input and spoken responses on both platforms.
How do you stop the AI from making things up?
Retrieval with citations from your own data, prompts that answer only from that context, an evaluation set that catches regressions, and human review wherever a wrong answer is costly.
Is our data used to train the AI models?
Not on the paid API terms we use: OpenAI and Anthropic do not train on API data by default, and Google does not use paid Gemini API data to improve its products (free-tier data can be). Where data must stay in your cloud, we run models through AWS Bedrock, Google Vertex AI or open-weight models you host.