AI Systems & Automation

Software that reads, answers, sorts or drafts, so people spend less time on repeat work. A few examples: an assistant that answers staff questions from your policy documents, a workflow that moves an invoice from the inbox to approval, or a first responder on WhatsApp that qualifies an enquiry before a person steps in.

AI is useful where the task is clear, the data exists and a mistake can be caught. We will say when a simple automation, or none at all, is the better answer.

Businesses whose teams do the same tasks every week: retyping data, chasing approvals, building the same report, answering the same questions. Companies with a steady stream of enquiries on WhatsApp or their website. Leaders who suspect AI could help but want a clear view before they commit.

The easiest first step is an AI Opportunity Audit. It ranks where AI could save time or cost in your business and ends with a short written report you keep.

• Assistants and AI agents connected to your data. An agent is an assistant that can also take actions, such as updating a record. See AI agents.
• Custom AI integrations, including MCP servers. MCP, the Model Context Protocol, is a standard way to let AI tools use your systems.
• Knowledge bases and internal AI search, where staff ask a question and get an answer with its source.
• Workflow and process automation. See workflow automation.
• AI reception for WhatsApp and websites. See WhatsApp AI reception.
• AI content and document workflows: reading invoices and forms and drafting content, always with a review step.
• An AI operations audit, an AI governance and usage policy, and workshops for teams.

We also build and run our own software in this area, including Site Bite, a chatbot for websites that answers only from a business's own content and shows its source. You can read about it among our products.

We follow The Path. You set the pace, and we agree the schedule with you at the start. In Listen we map the task, the data and what counts as a correct answer. In Shape we pick the approach, the AI model and the limits it must stay within, and we write down how it will be tested. Build delivers it in sprints, with a demo each time. Launch includes testing against real questions and a handover. Evolve is where the system stays accurate: we check answers, review mistakes and adjust.

Human checks are designed in from the start. A person approves anything that is hard to undo, and uncertain answers go to a person.

The TechSum Standard applies to every AI system and automation:

• Secure by default: we agree what each system may see, where data is stored and which model provider is used, and access follows the same rules as your team.
• Built to scale: a new step, a new tool or more volume means changing the flow, not starting over.
• Tested before it ships: every assistant tested against a set of real questions before launch, and again after every change.
• Looked after after launch: automations are monitored, so a broken step raises an alert instead of failing in silence.
• Small changes handled, not invoiced: a new question for the assistant or a tweak to one workflow step gets done. It does not become a new project.
• Honest advice, even when it means less work for us: if a simple automation, a change in process or nothing at all is the better answer, we say so before anything is built.

It starts with a clear task and a result you can count, such as fewer minutes spent per invoice or fewer unanswered enquiries. Answers show their source. A set of real questions must pass before and after every change. Staff can flag a wrong answer, and someone fixes it.

We also work out running costs before building, because usage fees depend on the AI model you choose.

If you want AI added for appearance, we will say so. If your data is not available, or the task has no clear answer, we will suggest fixing the groundwork first. And if an off-the-shelf automation tool would do, we will point you to it.

To talk through your case, tell us what you need.