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AI SOLUTIONS

AI Solutions

Let AI take the repetitive work, and keep your people on the decisions.

Services included

Assessment Chatbot Automation RAG

AI is worth adopting when it goes into a real process, not when it adds another chat window. We help you adopt AI customer service, process automation and RAG knowledge bases: repetitive, time-consuming manual work moves to the system, and common questions are answered from a knowledge base built on your own documents. Before anything is adopted, we map the operational bottlenecks and the state of your data to judge which steps suit AI.

WHO IT IS FOR

When this applies

These are the situations we see most often in the work we take on. If one of them matches where you are, it is usually a good time to talk.

“The same questions get answered dozens of times a day”

Opening hours, specifications, pricing rules — questions with a standard answer take up most of the support team's time, and outside office hours no one replies at all.

“Product manuals and pricing rules are scattered”

The answer is written down somewhere, but new staff do not know where to look, and experienced staff still page through file after file.

“The same set of documents eats several days every month”

Reports and standard documents are assembled by hand and copied between systems — repetitive, slow and easy to get wrong.

“You want to adopt AI but are unsure where it fits”

You know you want it, but not which steps should go to AI, or whether your current data is good enough to support it.

WHAT WE DO

What this service covers

Assessment

Adoption Assessment

We map the operational bottlenecks and the state of your data, to judge which steps suit AI.

Chatbot

AI Customer Service

Common questions answered around the clock; anything it cannot find is handed to a person.

Automation

AI Process Automation

Documents and reports generated automatically, systems connected, notifications scheduled.

RAG

RAG Knowledge Base

Answers drawn from your own documents, with the source cited; an update means replacing a file.

SCOPE & TIMELINE

Project size and schedule

Settle the scale first, then the details. The actual scope and quote always follow the requirements interview and the agreed function list; we do not sell fixed packages.

Adoption assessment (AI process audit)

What it covers

We map the operational bottlenecks and the state of your data, say which steps suit AI and which do not, and only then decide whether to go further. This can be taken on by itself; you do not have to choose what to adopt beforehand.

Schedule

The schedule depends on how many departments and how many processes are covered; it is set out when the scope of the assessment is confirmed

How it is priced

The process audit defines the scope first, and pricing then follows the steps actually adopted

AI customer service and RAG knowledge base build

What it covers

A knowledge base built on your own documents, able to cite its sources. When it cannot find an answer it replies that it is not sure and hands the conversation to a person, and after launch it keeps growing from the conversation logs. The knowledge base can sit on a server you nominate.

Schedule

The schedule depends on how many documents there are, how well organized the data is, and which support channels are connected; it is set out once the knowledge base scope is confirmed

How it is priced

The build is priced by document volume and the channels connected; model usage is billed on actual calls and is listed separately in the quote

The schedule and pricing notes on this page are for planning purposes. The formal quote is issued in writing, based on the scope agreed in the requirements interview.

WHEN NOT TO BUILD

If a build is not warranted, we say so

Not every request is worth building. In the situations below we will suggest holding off, or handling it a less expensive way, and explain why.

Where AI does not fit, we will not force it in
If the assessment shows that a step is not suited to AI, we say so and explain why. We do not adopt AI for its own sake.
Judgments about money and entitlements stay with people
AI can still get things wrong, so answers are restricted to the knowledge base you provide. When it cannot find an answer it replies that it is not sure and hands over to a person, which is far safer than letting it guess.

FAQ

Frequently Asked Questions

Can AI customer service give wrong answers?

Yes, which is why we restrict its answers to the knowledge base you provide rather than letting it improvise. When it cannot find the data it replies that it is not sure and hands the conversation to a person, which is far safer than letting it guess. After launch, the conversation logs show what it answered wrongly or could not answer at all, so the knowledge base keeps growing and accuracy improves month by month.

How does a RAG knowledge base differ from ordinary AI customer service?

The difference is where the answer comes from. Ordinary AI customer service answers from what the general model already knows, which can produce statements that have nothing to do with your company. RAG first finds the relevant passages in your product manuals, pricing rules and FAQs, answers from those passages, and can cite the source. When the information changes you replace the document; there is no need to retrain the model.

Is it safe to put internal company data into AI?

The risk is controlled through how it is deployed. The knowledge base sits in your own database or on a server you nominate, we restrict which data it can reach, and queries are logged. If external models are a concern, you can move to an enterprise plan that states plainly it will not train on your input, or consider running an open-source model on your own server.

LET'S TALK

Let's write your next system in the future tense.

Not sure which one to start with? Tell us where the work gets stuck today, and we will propose how to tackle it and what the scope should be, before we talk price.

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