AI & Automation
AI Agents and Automation
AI agents, chatbots, workflow automation and marketing automation, built as one connected system on top of the tools you already use.
Talk to us about AI Agents and AutomationOne service for putting AI to work inside a business: agents that carry out multi-step tasks, chatbots that answer from your own knowledge, automation that moves work between your systems, and marketing automation that captures and follows up leads.
They are offered together because real projects rarely need only one. A support chatbot needs an agent behind it to look up an order. An agent needs automation to pass its result to the next system. Lead follow-up needs all three. Building them as one connected system, with one team, avoids four tools that do not talk to each other.
Problems this solves
- Skilled people spend their day on repetitive, multi-step work that follows the same pattern every time.
- Customers and staff wait for answers that already exist in your documents.
- Information is copied by hand between a CRM, inboxes, spreadsheets and operations systems.
- Leads arrive and wait hours or days for a first reply.
What this covers
AI Agents
Software that takes a goal, decides the steps, uses your tools and systems, and asks a person before anything risky.
Read more about AI AgentsAI Chatbots
Conversational AI grounded in your documents and data, that says when it does not know and hands over to a person.
Read more about AI ChatbotsAI Automation
Workflow automation that connects the systems you already use, with AI for reading, classifying and drafting.
Read more about AI AutomationAI Marketing Automation
Lead capture, scoring, routing and follow-up built into your CRM, with AI-assisted drafting.
Read more about AI Marketing AutomationTypical projects
Examples of the kind of work this covers, not a list of past clients.
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Support ticket triage
Incoming tickets are read and classified, the customer and order are looked up, and a reply is drafted for a support agent to approve.
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Knowledge chatbot
Customers or staff get answers from your own documentation, with a clean handover to a person when the answer is not there.
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Document intake
Invoices, forms and emails are read, checked against your records, and only the unclear ones go to a person.
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Lead follow-up
Enquiries are captured, summarised and routed to an owner, with a first reply drafted for review.
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Back-office sync
CRM, accounting and operations systems are kept in step without anyone retyping data.
What an AI agents and automation project includes
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A scoped first use case
One clearly bounded piece of work with a success test, chosen with you before anything is built.
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Integrations with your systems
Tested connections to the APIs, databases, CRM and tools the work depends on, each with its own permissions and limits.
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Grounding in your own knowledge
Answers and decisions built from your documents and data, not from the model's memory.
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Guardrails and human approval
Rules for what the system may never do, and approval steps where a person confirms before money moves or a customer is contacted.
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Evaluation and monitoring
A test set from your real cases, plus logs of every step and its cost, so quality is measured and not assumed.
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Documentation and ownership
Code, prompts, test sets and infrastructure are yours, in your repositories, with a written guide for your team.
Is AI automation the right fit?
A good fit when
- The work is repeated often and has a clear definition of done.
- The systems involved have APIs, or can be given one.
- A wrong result can be caught by a check or a person before it costs you.
Probably not when
- Nobody can yet say what a correct result looks like. Start with AI Consulting.
- The process changes every week. Stabilise it first.
- An existing product already does the job well. Buy it.
How an AI automation project runs
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01
Understand
A short call to understand the actual problem, not just the requested feature. We push back if the scope doesn't match the goal.
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02
Architect
A concrete technical plan and estimate before any code is written, covering stack, integrations, and where the real risk is.
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03
Build
Iterative delivery with visibility into progress, not a black box until launch day.
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04
Validate
Tested against real-world requirements and failure modes, not just the happy path, before anything reaches production.
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05
Operate
Deployed, monitored, and supported after launch, with infrastructure and observability included from day one. Never a forced retainer.
Technology we work with
- Language model APIs from the major providers
- Retrieval over your own documents (RAG)
- Python, Django and Celery for the system around the model
- PostgreSQL and Redis
- Your CRM, help desk and internal APIs
Questions about AI Agents and Automation
Do we need all four, or can we start with one?
Start with one. Most projects begin with a single use case, such as a support chatbot or one automated workflow, and add the others when the first has proved its value.
What is the difference between an agent, a chatbot and automation?
A chatbot answers questions in a conversation. An agent carries out a task across several steps and systems. Automation runs fixed steps the same way every time. Many products combine them: a chat interface in front, an agent doing the work, automation moving the result on.
Can this work with the software we already use?
Yes. Most of this work sits on top of systems you already run. We check what each system's API allows before promising anything.
Is AI always needed?
No. Many valuable automations are plain rules and integrations. We add AI only where a step involves free text, documents or judgement, and say so when a simpler approach is better.
How do you keep it reliable?
By testing against real examples of your work before launch, keeping a person in the loop for risky actions, and logging every step so problems are visible. We do not promise zero errors.
How much does an AI agent or automation project cost?
It depends on scope, so we do not publish a price list. After a short call to understand the problem you get a written estimate with its assumptions, before any commitment. If the work is not worth doing, we say so.
From the blog
What an AI Agent Costs to Run and How to Keep It Predictable
Agent costs are driven by steps, context size and model choice, not by the number of users. Each can be measured and limited.
How to Test an AI Agent Before It Touches Real Customers
A demo proves an agent can work once. A test set built from real cases shows how often it works, and where it does not.
AI Agent Guardrails: What to Restrict and What to Approve
An agent that can act needs limits that do not depend on the model behaving. A practical way to sort actions into free, approved and forbidden.