AI Mobile Apps
Native iOS & Android apps with AI embedded in every interaction.
Learn moreEngineering-led AI delivery
Describe your AI idea.
We build it.
CodeKrypt is an AI app development company that builds AI-native mobile apps, web apps, chatbots and automation — from idea to production. React Native, Node.js and AWS, engineered by a team that ships.
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We reply within 12-24 hours · Or fill the full 6-step brief for a detailed scope
- 50+
- AI products shipped
- 12+
- Countries served
- 4 weeks
- Average first delivery
- 100%
- Engineering-led
What we build
AI products across every surface
From idea to production — mobile, web, automation, and everything in between.
AI Chatbots
Intelligent assistants for support, sales, and knowledge workflows.
Learn moreAI Automation
End-to-end workflow automation powered by large language models.
Learn moreAI SaaS Products
Multi-tenant platforms with dashboards, AI pipelines, and APIs.
Learn moreDocument Intelligence
Extract, summarise, and act on unstructured documents at scale.
Learn moreAI Modernisation
Add AI capabilities to your existing web or enterprise product.
Learn moreWhat AI apps look like
Interfaces we design and build
Production-grade mobile and web interfaces across mobility, media, fintech, retail and operations — with AI doing the part that needs judgement. Tap any screen to see how we build that category.
Shipped for a client
Mobility · React Native
EV charging app
Charger discovery, live session tracking and smart scheduling — shipped for an EV charging network.
Built and shipped end-to-end, spec to app store release
Shipped for a client
OTT · React Native
Live sports streaming
Live match streaming at scale, with highlights and personalised discovery — shipped for a sports OTT platform.
Built and shipped end-to-end, spec to app store release
Fintech · Mobile
Wealth dashboard
Portfolio tracking with AI insights that flag concentration risk before it bites.
Customer support
AI support assistant
Answers grounded in your own systems, with sources shown and a one-tap human handoff.
SaaS · Web
Document AI console
Processing volumes, auto-clear rate and a review queue for anything low-confidence.
Operations
Logistics control tower
Live shipment health with exceptions surfaced by AI before customers call in.
Retail · Mobile
Commerce app
Product discovery with fit guidance drawn from review data, not guesswork.
Health · Mobile
Fitness coach
Activity rings, trend history and a coach that adapts the plan to recovery.
Marked screens are shipped client products, shown unbranded under NDA. The rest are interface designs representing what we build. Full case studies available on request.
How it works
How to build an AI app, without the usual detours
AI app builders get you a demo in an afternoon. Getting the same thing into production — with permissions, evaluation and a rollback path — is the part that needs engineers. This is how we run it.
01
Describe the workflow
Send the idea in a sentence or fill the brief. We come back with scope, risks and a build order.
02
Thin slice first
One workflow end to end, instrumented, with an evaluation set — not five features half-built.
03
Harden it
Permissions, error paths, escalation to a human, retention rules and cost per session.
04
Ship and iterate
Live with tracing, so the next change is driven by measured failures rather than guesses.
From the blog
Notes on shipping AI products
AI Agents
AI agent evaluation framework: what to check before an agent goes live
7 min
Cost
Voice AI agent cost in India: what a production deployment actually runs
6 min
AI Agents
AI agent memory cost: what tiered context actually saves you
5 min
AI Agents
AI agent security: what prompt injection actually breaks, and the architecture that contains it
7 min
AI Agents
AI agent observability cost: what to budget before you scale
5 min
Architecture
On-device AI vs cloud AI for mobile apps: the real cost and architecture decision
5 min
Product Strategy
How to evaluate an AI development partner (the questions a demo won't answer)
4 min
Architecture
Document intelligence is not OCR: what actually breaks in production
5 min
FAQ
AI app development questions, answered
The questions founders and product teams ask us most often before starting a build.
- How much does it cost to build an AI app?
- It depends on whether the app answers questions or takes actions in your systems, because the second needs permissions, audit trails and a rollback path. Scope moves the number far more than model choice does. Our blog post on what an AI MVP actually costs breaks the structure down line by line.
- How long does it take to build an AI mobile app?
- A narrow first version — one workflow, instrumented, with an evaluation set — is typically a matter of weeks rather than quarters. Integrations with your existing systems and the evaluation work usually set the schedule, not the model.
- Can I build a mobile app with AI instead of hiring developers?
- AI app builders are genuinely good at producing a working prototype quickly. Where they stop is production: authentication, permissions, offline behaviour, app store review, evaluation and the ability to debug a bad answer six months later. Many teams prototype with a builder and bring in engineers to productionise it, which is a reasonable sequence.
- Do you build with React Native or native iOS and Android?
- React Native for most products, because one codebase covering both platforms is usually the right economics, and native modules cover anything platform-specific. Where an app is heavily dependent on platform APIs or real-time graphics, native is the better call and we will say so.
- Which AI models do you build with?
- Whichever fits the constraint that binds — accuracy, latency, cost or data residency. We design so the model is a component you can swap, with behaviour and knowledge held outside it, rather than welding the product to one provider.
- Do you work with companies outside India?
- Yes. We are based in Pune and work with founders and product teams worldwide, with overlap hours agreed up front.
- What do I get after sending a brief?
- A practical delivery plan from an engineer — scope, sequencing, risks and what we would leave out of v1 — usually within 12 to 24 hours.