On-Device AI & NLP SDK · Zero Cloud · Works Offline

Natural language UX.
No cloud. No cost per request.

Users type or speak commands. Your app understands and executes — using on-device machine learning. Not a single byte leaves the device.

iOS
Android
Web

Adding conversational UX today means painful trade-offs

Every path has a catch — until now.

Option 1

Cloud AI APIs

$0.01–$0.10 per request. At 100K active users doing 5 commands a day, that's $50K–$500K/month just for the API. Plus every query leaks to a third party you don't control.

Option 2

Build it yourself

You need an ML team, 6–12 months of development, and ongoing maintenance. Most product teams can't afford that — and it's not your core product anyway.

Option 3

Do nothing

Users stay stuck with menus and buttons while your competitors ship conversational UX. The gap widens every quarter.

Hardcoded eliminates the trade-off.

From natural language to structured action — on-device, on-premise

A user speaks or types. The on-device AI understands, extracts, and delivers a clean result to your business logic. Milliseconds. No cloud. Fully local.

1

User sends a natural language command

Text or voice — whatever feels natural. No special syntax required.

2

SDK recognizes intent & extracts entities

Intent detection at 99.0–99.7% accuracy on our test sets. All on-device, in milliseconds.

3

Multi-turn dialog fills in the gaps

If the command is incomplete, the SDK asks a clarifying question and collects missing data before handing off.

4

Structured result passed to your logic

Your existing code gets a clean intent + entities object — no guessing, no errors.

Crypto Wallet
Swap 200 USDC to ETH
intent: swap_token
{ amount: 200, from: "USDC", to: "ETH" }
Healthcare
Book me with Dr. Ivanov next Tuesday afternoon
intent: book_appointment
{ doctor: "Ivanov", date: "next Tuesday", time: "afternoon" }
Food Delivery · Multi-turn
Order the usual to work
SDK: "What's your work address?"
14 Elm Street
intent: place_order
{ preset: "usual", address: "14 Elm Street" }
99.7%
Intent recognition accuracy
~120 MB
SDK size on device
Days
Integration effort, not months
$0
Cost per request, ever

On-premise AI for apps that can't afford to send data to the cloud

Hardcoded is an on-device machine learning SDK — sometimes called on-premise AI, local AI, or edge AI — that brings natural language understanding directly into your iOS, Android, or Web app. Unlike cloud AI APIs (OpenAI, Google NLP, AWS Comprehend), Hardcoded runs the entire inference pipeline locally: no outbound requests, no latency from a round-trip, no per-token billing, and no data leaving the user's device.

On-device AI
Inference runs on the user's hardware
On-premise NLP
No external API calls, ever
Edge AI SDK
Works where there's no internet
Private AI
GDPR-compliant by architecture

Better for users. Better for the business.

Conversational UX isn't just a feature — it reshapes your metrics.

📈

Revenue & Retention

Users who control apps through natural language stay longer and do more. Less friction at every step means fewer drop-offs, more completed transactions, higher engagement.

💰

Fixed NLP Costs

Cloud NLP bills grow linearly with your audience. With Hardcoded, your NLP cost is a flat annual license — regardless of users or usage frequency. The faster you grow, the better your unit economics.

🏆

Competitive Edge

Most apps in regulated industries haven't added AI interfaces because privacy was unsolvable. Hardcoded solves it. You can ship what competitors can't.

🔒

User Trust

Your users don't want banking transactions or medical queries going to OpenAI. With Hardcoded, you can honestly tell them: nothing leaves the device. That's a real differentiator in trust-sensitive markets.

Regulatory Compliance

No outgoing data — no new liability zone. Nothing to declare under GDPR. Nothing to audit for financial regulators. Nothing to explain to legal.

📡

Works Offline

Field services, travel apps, logistics, low-connectivity regions. Hardcoded works identically without a network connection — because there's no network call to make.

Built for teams that can't afford the cloud trade-off

If any of these sounds like you, we should talk.

🏦 Sensitive Data Apps

Banks, fintech, crypto wallets, healthcare, legal, HR, government. Your users expect data to stay with them.

💸 Growing AI Bills

Already using cloud NLP? Your costs scale with your audience. On-device eliminates the per-request fee entirely.

⚖️ Regulated Industries

GDPR, central bank requirements, medical and financial regulators. No data transfer by design — nothing to audit.

📴 Offline-First Apps

Field services, travel, logistics, regions with weak internet. Hardcoded works the same with zero connectivity.

🛠 Teams Without ML

You build great products but have no ML engineers. Hardcoded gives you a ready, customizable NLP layer your current devs can integrate in days.

Full integration for $20,000

A fixed one-time fee. We handle the end-to-end integration — SDK setup, domain training, and engineering support — so your team doesn't have to.

End-to-end integration We set up and configure the SDK in your app from start to finish
Direct engineering support We work alongside your team through every step
Custom command vocabulary Model trained on your specific domain and app commands
Roadmap influence Your use case directly shapes what we build next
Get Integration →

$20,000 — one payment. No subscriptions, no per-token fees, no per-request costs. That's it.

Common questions


No. Once the SDK is installed, it works completely offline. All AI inference happens locally on the device — no network calls are made at any point.
No. No analytics, no telemetry, no callbacks. All on-device machine learning processing is local. We have no access to what users say or type.
Yes. Because all AI processing is on-device and no data leaves the device, there is nothing to declare under GDPR and no third-party data transfer to audit or disclose.
Intent recognition: 99.0–99.7% on our test sets. Entity extraction accuracy is validated during the pilot on your real app data.
About 120 MB — comparable to adding a quality image processing library. The tradeoff is zero ongoing cloud cost and full on-device privacy.
Yes. You define your intent vocabulary in a simple config file. The model is fine-tuned on your examples during the pilot integration.
English in the current version. Support for other languages is on the roadmap — pilot partners have input on prioritization.
No. Standard mobile or web development skills are enough. The ML is packaged in a ready-to-use binary — your engineers don't need to touch model code.

Ready to ship conversational UX
without the cloud?

Integrate in days. Keep every user query on their device.

Get Integration →