See Our AI in Action
This page demonstrates production AI systems built by Cropsly — from Hindi voice commands to intelligent chatbots and on-device model deployment. All demos use real data and patterns from delivered projects across the UK, Denmark, Germany, Norway, and the US.
3 interactive demos. Real project data. See what we build for clients.
200+
Projects delivered
18+
Clients worldwide
6+
Countries served
82%
Client retention
Voice Commands for Hotel Operations
RunHotel's on-device Hindi voice system — from speech to action in 120ms. Click a command to see the flow.
Room status: Available → Occupied
120ms
Latency
500+
Commands/day
3
Languages
On-Device SLM
Model
Zero
Cloud dependency
AI Chatbot in Action
See what a Cropsly-built chatbot looks like in production. Pick a conversation to watch it play out.
Cropsly AI
Online
AI Model Comparison
Compare open-source AI models we use in production. Filter by size, license, and deployment target.
Last updated: March 2026 · Approximate latency — varies by quantization and hardware. Benchmarked on RTX 5090 + Threadripper PRO.
| Model ↕ | Provider ↕ | Size ↑ | Latency ↕ | License ↕ | On-device |
|---|---|---|---|---|---|
| Qwen3.5-0.8B | Alibaba | 0.8B | ~9ms | Apache 2.0 | Yes |
| Gemma 3 1B | 1B | ~10ms | Gemma License | Yes | |
| Llama 3.2 1B | Meta | 1B | ~10ms | Llama License | Yes |
| SmolLM2 1.7B | Hugging Face | 1.7B | ~14ms | Apache 2.0 | Yes |
| Qwen3.5-2B | Alibaba | 2B | ~16ms | Apache 2.0 | Yes |
| Llama 3.2 3B | Meta | 3B | ~22ms | Llama License | Yes |
| Phi-4-mini | Microsoft | 3.8B | ~24ms | MIT | Yes |
| Ministral 3B | Mistral | 3.8B | ~25ms | Apache 2.0 | Yes |
| Qwen3.5-4B | Alibaba | 4B | ~28ms | Apache 2.0 | Yes |
| Gemma 3 4B | 4B | ~30ms | Gemma License | Yes | |
| Qwen3.5-9B | Alibaba | 9B | ~55ms | Apache 2.0 | No |
Need help choosing the right model? We'll benchmark it on your data.
Get Expert Guidance →Frequently Asked Questions
Common questions about our AI capabilities and how to get started
Can I use these AI systems in my product?
Yes. Every demo on this page is based on production systems we've built for real clients. We can build similar capabilities tailored to your business — from voice interfaces to intelligent chatbots and on-device model deployment.
How long does it take to implement?
A focused AI agent or chatbot MVP takes 4-8 weeks. Voice AI systems with on-device processing take 8-12 weeks. Complex multi-model systems take 3-6 months. Timeline depends on scope, integrations, and compliance requirements.
What tech stack do you use?
It depends on the project. Voice AI uses Whisper, Web Speech API, and on-device SLMs via ONNX Runtime. Chatbots use LangChain, Claude API, and vector databases. Model deployment uses PyTorch, TensorRT, and Hugging Face. We pick the right tool for each use case.
Are these demos based on real projects?
Yes. The voice demo is from RunHotel, our AI-powered hotel management system used in India. The chatbot demonstrates patterns from multiple client projects. The model comparison table reflects our internal benchmarks on RTX 5090 + Threadripper PRO infrastructure.
How do I get started?
Contact us at [email protected] or use our contact form. We'll schedule a discovery call to understand your requirements and recommend an approach. Most projects start with a 2-week discovery phase.
Ready to Build Your AI?
Tell us about your project. We'll build a proof of concept in weeks, not months.
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