Scenarios
Typical problems in different industries, and how AI can be built into products and workflows.
Industry scenarios
E-commerce and retail
Lots of products and scattered questions keep support and operations busy with repeats.
How AI fits in
An agent handles pre-sale questions and after-sale routing; smart search and recommendations on the site help shoppers find what they want sooner.
- Agent
- Website with AI built in
What we deliver
Education and training
Student questions are scattered, course matching is manual and follow-up comes late.
How AI fits in
An admissions agent learns the need before recommending courses; the learning platform gets in-lesson questions and study-plan suggestions.
- Agent
- Software with AI built in
What we deliver
Business services and SaaS
Many features make onboarding slow, and tickets and docs are spread around.
How AI fits in
Natural-language actions and guidance are built into the product; a knowledge-base agent answers how-to questions.
- Agent
- Software with AI built in
What we deliver
Manufacturing and supply chain
Data sits in several systems, exceptions are caught by people watching, and reports take time.
How AI fits in
Orders, inventory and quality data are connected; an agent gathers exceptions into a daily report, and the software shows alerts with suggested actions.
- Agent
- Software with AI built in
What we deliver
Financial services
Large volumes of documents, complex rules and strict compliance.
How AI fits in
An agent does first-pass document review and key-point extraction, records every step, and hands off to a person for sign-off, so decisions can be traced.
- Agent
- Software with AI built in
What we deliver
Local services
Scattered stores, many reviews and questions, and limited operations staff.
How AI fits in
Mini-programs and sites get smart recommendations and booking guidance; an agent summarises reviews and suggests replies.
- Agent
- Website with AI built in
What we deliver
Frequently asked questions
How does a project usually start?
With a conversation to make the problem, the existing systems and the success criteria clear. Then we propose an approach and stages, and begin building once you agree.
How long does it take?
It depends on scope. A single agent or a simple demo is usually quicker; connecting several systems takes longer. After we talk, you get a stage-by-stage schedule rather than one vague number.
What do we need to provide?
A description of the process, the systems or interfaces we can reach, representative sample data or documents, and one person who can make decisions. Sample data is fine to start with; you don’t have to open real data on day one.
How are data security and privacy handled?
Before we start we agree with you which data may be used, where it is processed and who can access it. The specific confidentiality terms and deployment approach are set out in the agreement.
Which model do you use, and what does it cost?
We pick a model per task, balancing quality, speed and cost, and taking into account where your data may be kept. Model usage fees are described in the proposal so you can estimate them.
How is this different from putting a chat box in the corner of a page?
A chat box is one entry point that has nothing to do with the rest of the page. We build AI into search, recommendations, forms, review and workflows, so people benefit while using the product instead of going off to ask the AI.
Do you maintain it after delivery?
At delivery you get documentation and a handover. Ongoing maintenance, model tuning and new features can be agreed separately, and we settle how in the course of working together.
How do we judge whether it works?
Before starting we agree a few things we can observe, such as handling time, the share handed to a person and user feedback. After launch we review against them and decide what to adjust.
Curious which work AI could take on?
Tell us where your team spends the most time and effort, and we’ll work out together whether it’s worth doing and how.