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 →