AI & Automation

Practical AI and automation for business operations

We do not sell AI. We look for a specific task that costs your team hours every week, and we build something that does that task — then we measure whether it actually helped.

Discuss Your Project See concrete examples

How we scope an AI project

  • Name one task, done by real people, that takes measurable time
  • Check whether the information it needs already exists in usable form
  • Build the smallest version that could work, and test it on real cases
  • Compare it against how the task is done now — honestly
  • Expand it only if the comparison was favourable
Where this usually goes wrong

"We should do something with AI" is not a project

Most AI work that fails inside a business fails before any technology is chosen. It starts from the tool rather than the task. Someone installs a chatbot, points it at the website, and six weeks later it is answering questions nobody asked while the sales team still handles every real enquiry by hand.

The second failure is data that was never ready. An assistant that answers questions about your products is only as good as your product information. If the specifications live in forty PDFs of varying vintage, the honest first step is organising those — not buying a model.

The third is scope without a boundary. An assistant asked to handle everything will confidently handle things it should have escalated. Every system we build has an explicit edge: here is what it answers, and here is the point where it hands over to a person.

A useful AI project is narrow, connected to real data, and measured against the way the job is done today.

Where AI earns its place

Six patterns that repeatedly pay for themselves in businesses like yours.

Customer support

Answering the routine questions — specifications, availability, terms, shipping — at any hour and in any time zone, and escalating the rest.

Sales assistance

Helping a buyer find the right product from your range and capturing what they need before a salesperson picks it up.

Document search

Asking a question across contracts, manuals, quotations and internal files, and getting the answer with the source document attached.

Lead automation

Classifying incoming enquiries, drafting a first response, and routing the qualified ones into your CRM with the right owner.

Internal knowledge

A single place staff can ask how something is done here, answered from your own procedures rather than from the internet.

Workflow automation

Extracting data from documents, generating drafts, and removing the repetitive steps between one system and the next.

Three examples, in detail

Each of these is a pattern we build, described as a business problem first. None of them is a client — we are a new company and we will not pretend otherwise.

Pattern 01

AI Sales Assistant

Problem

Buyers in other time zones send the same twelve questions — dimensions, materials, MOQ, lead time, certifications, sample policy. They arrive overnight and get answered the next afternoon. Some buyers do not wait.

Solution

An assistant trained on your product catalogue, specification sheets and commercial terms, available on your website. It answers what it can from your own documents, says so when it cannot, and captures the enquiry with everything it learned in the conversation.

Boundary

It does not quote prices, commit to lead times, or negotiate. Those go to a person, with the conversation attached so nothing is repeated.

Measured by

Enquiries received outside business hours that arrive already qualified, and the drop in first-response time.

Pattern 02

AI Document Assistant

Problem

The answer to "what did we agree with this supplier about packaging?" exists — in a contract, a revision of a specification, or an email from two years ago. Finding it takes half an hour, and only two people know where to look.

Solution

Your documents indexed into a searchable knowledge base that staff can ask questions of in plain language — in English or Vietnamese — with every answer citing the source file and the page it came from.

Boundary

Access follows your existing permissions. It answers only from your documents, and when it finds nothing it says so rather than inventing a plausible answer.

Measured by

Time to find a known fact, and how often the answer is right when checked against the source.

Pattern 03

AI Lead Automation

Problem

Enquiries arrive from the website, from marketplaces and by email. Some are serious buyers, many are students, resellers or spam. Everything gets the same manual triage, so real buyers wait behind noise.

Solution

Incoming enquiries are classified by intent, product area and market; a first response is drafted for a person to approve; and qualified leads are written into your CRM with an owner and a follow-up date already set.

Boundary

Nothing is sent without a human approving it, unless you later decide a specific category is safe to automate fully.

Measured by

First-response time on qualified leads, and the share of sales time spent on enquiries that could convert.

Also part of this service

AI website chatbots Grounded in your content, with a defined escalation path to a person.
AI knowledge assistants Internal Q&A over your own procedures, in English and Vietnamese.
AI content workflows Drafting product descriptions and translations for a human to review and approve.
AI email and enquiry automation Classification, routing and draft replies across your shared inboxes.
AI features inside your CRM Summaries, next-step suggestions and duplicate detection where they save real time.
AI in your existing software Added to a system you already run, rather than delivered as a separate tool.
LLM and API integration Connecting language models to your data with the access controls that implies.
Business process automation Not every step needs AI. Much of the saving is ordinary automation done well.

What AI is not going to do for your business

This section exists because most AI pages are missing it, and because a project that starts with accurate expectations is the one that succeeds.

  • It will not fix disorganised information. If your product data is inconsistent, an assistant will answer inconsistently. Organising the data comes first, and it is often the whole project.
  • It will not replace your sales team. It removes the repetitive first layer so your people spend their time on the conversations that need judgement.
  • It will not be right every time. Language models make mistakes. Every system we build is designed around that fact: grounded in your documents, citing sources, and escalating rather than guessing.
  • It will not run unsupervised from day one. A person reviews the output until the accuracy is demonstrated, not assumed.
  • It is not always the answer. Sometimes the honest recommendation is a better form, a clearer page, or a small piece of ordinary automation. We will tell you when that is the case.

Related

Service

Custom Software

AI works best on top of a system that already holds your data properly.

Explore Software Industry

Professional Services

Document-heavy businesses are where document search pays back fastest.

Read more Service

English & Bilingual Websites

A sales assistant is only as good as the product information behind it.

Explore Websites
Let's build together

Which task in your business
takes the most repetitive time?

Start there. Tell us what it is and we will tell you honestly whether AI is the right tool for it.

Discuss Your Project
Email Us Send a Message