We develop AI applications, web and mobile apps, and native macOS and iOS software for businesses, and keep maintaining them after launch. Our own products — XicoClean, Machi, Nerva and others — come from the same team and show the quality we deliver.
We bring large language models into your actual business, and handle all of it: picking the use case, designing the solution, building it, measuring the results and launching.
Web, mobile and desktop apps, delivered complete, from design and development to launch.
Native macOS and iOS apps in Swift 6 and SwiftUI. XicoClean is built this way.
Deployment, monitoring, fixes and new releases. Once your system is live, we keep looking after it.
Each can start with one small, concrete step and grow once the results are proven.
A lot of work follows the same few steps: open a few systems, look things up, sort it out, fill in a form, send it on. We hand that kind of process to an AI agent. It can use your existing systems and tools, and it stops and waits for a person wherever a decision is needed. Every step is logged, so when something goes wrong you can see why, and permissions match the systems you already have.
Common uses
Policies, product material and project documents tend to live in a dozen places. New people can't find them, and everyone else stops looking. We gather them into a knowledge base and use retrieval-augmented generation (RAG), so the model answers only from your material and cites its source. Who can see what follows your internal permissions, and when a document changes, the answers change with it.
Common uses
Connected to your website, app, WeCom and other channels, it replies at night and on holidays too. It can look up orders, stock and shipping, and recommend products by rules you set. When a question is complicated or sensitive, it passes the conversation to a person with everything said so far, so the customer doesn't have to repeat themselves. Good answers are kept and reused.
Common uses
Multimodal models read contracts, invoices, CVs, reports and images, and turn what you need into structured data. They compare, check and summarise it, then write it back into the systems you already use. Paperwork that took half a day by hand is done in minutes, and people only review the results.
Common uses
First we turn your brand's tone, vocabulary and no-go areas into rules. Then we generate product descriptions, social posts, marketing emails and localised content in batches, alongside image and video tools if you use them. A person reviews every piece before it's published, so output goes up without the style drifting.
Common uses
For sensitive data, or where things have to work offline, we deploy open models on your private servers or on local devices. Inference, retrieval and evaluation all happen inside your own environment, and the data never leaves it. XicoClean's on-device similar-photo detection works exactly this way.
Common uses
Still deciding whether AI is worth it, ready to build a product, or need someone to look after things for the long run? There's a way to start for each.
We map your use cases and priorities, estimate feasibility and cost, then build a working prototype on your real data, so you can decide whether it's worth going further.
A complete project, from requirements and design to development, testing and launch. Scope, schedule and acceptance criteria are written down before work starts.
Like an outside technical team of your own: new features, performance and cost improvements, model upgrades, operations and security.
We use whichever model gives the best results at the right cost and meets your compliance needs. Different parts of one system can use different models, and when a better one comes along, we can switch.
Models we've used and integrated
Web, mobile and desktop apps, and AI assistants inside WeCom, Feishu and DingTalk.
Tool calling, workflow orchestration, several agents working together, and human review steps.
Document parsing, vector retrieval (RAG), data cleaning and permission isolation.
Cloud APIs, private deployment and on-device inference, with evaluation and monitoring.
It's the same process we use for our own products. At the end of every stage you get something you can check.
We learn your background, goals and constraints, then work out the scope and the technical route. We'll tell you plainly what's worth doing and what isn't.
We settle the structure, the interactive prototype and the technical plan. For AI projects we prototype on real data first, so problems show up before the big spending starts.
We work in versions, and you can see progress at any time. Every version can be tried hands-on, and AI features come with results you can compare.
Deployment, monitoring, fixes and new releases, with results and costs tuned as we go. If something comes up after launch, you talk to the same people.
Habits we formed building our own products, applied to every client project.
Before building anything, we work out what problem we're solving, who it's for and what 'done' looks like. Sometimes the best answer doesn't need AI at all, and we'll say so.
Whether AI works isn't a matter of feel. For every use case we prepare a test set, and compare the same metrics before and after launch.
If data can be processed locally, it isn't uploaded. Who can see a client's data, and what it may be used for, is agreed upfront and written into the contract.
Our own products keep getting updates, and client projects keep getting maintenance. If something goes wrong after delivery, you can always reach us.
Didn't find your answer? Just email us.
Most often: AI agents and workflow automation, enterprise knowledge bases and Q&A (RAG), AI customer service and sales assistants, processing documents such as contracts and invoices, content generation and marketing, and privately deployed AI applications. We can also add AI features to your existing systems and apps, or to WeCom, Feishu and DingTalk.
We're not tied to any one model, and choose by quality, cost, speed and compliance. In China we often use DeepSeek, Qwen, Kimi, GLM and Doubao; elsewhere, OpenAI, Claude, Gemini and Llama, among others. Where data is sensitive, we can deploy open models on your private servers or on local devices.
We prefer enterprise APIs that don't train on customer data, or a private deployment. We only ask for the access we need, and how the data may be used is spelled out in the contract. Anything that can be processed locally isn't uploaded.
It depends on the scope. A focused prototype usually takes a few weeks; a full product moves forward in milestones. Once we understand what you need, we'll give you a clear plan, schedule and quote.
Whatever the contract says. Usually the source code and related intellectual property we develop for you are yours once the project is paid in full, and we hand over the code, documentation and deployment notes together.
Whether you want to bring AI into an existing business, build a product from scratch or need a long-term engineering team, write to us. Share the background, goals and timeline, and we'll reply within 1–2 business days with how we'd approach it.
Or email us directly · hi@xicoai.com