Artificial Intelligence & Automation
AI where it earns its place. Automation where simple rules will do.
Most of the value in business AI comes from connecting it properly: to your documents, your CRM, your inbox, and to a person who approves what matters. The model is only one part of the system; data, integrations, permissions and evaluation determine whether it is useful.
We design and build rule-based automations, AI-assisted workflows, internal knowledge assistants, customer-facing chat, tool-using agents and custom AI applications. They differ a great deal in cost, reliability and risk, and part of our job is to recommend the simplest one that solves the problem.
We start with the task, the people doing it and the result that should improve. The technology follows from that.
Where to begin
Most projects begin with the AI Strategy & Opportunity Audit or with one focused automation. Both are small, defined pieces of work that show where AI is worth it before anything larger is built.
Four areas, twelve services.
AI Advisory & Strategy
AI Agents & Automation
AI Development & Integration
AI Deployment & Support
AI Strategy & Opportunity Audit
Find out where AI is worth it, where simpler automation is enough, and where neither is.
Not every process needs artificial intelligence. Often the best result comes from a mix of simpler automation, better-connected software and a few carefully chosen AI capabilities.
We look at how your team works, which tools it uses and where manual effort or scattered information slows things down. Then we name realistic opportunities and help decide which are worth pursuing first.
What we deliver
- Interviews and workflow mapping
- Inventory of tools, data sources and access
- Assessment of each opportunity: value, feasibility, risk
- A recommendation per task: automation, AI-assisted or manual
- Data protection and security considerations
- Pilot definition with success criteria
- Estimate of build and running costs
- Prioritised implementation roadmap
Who it’s for
Businesses exploring AI for the first time, or organisations looking to move beyond isolated AI experiments.
Worth knowing
An audit may conclude that ordinary software improvements or rule-based automation offer better value than an AI system. That is a useful result, not a failed one.
Automation, assistants, agents: what’s the difference?
These approaches differ in how predictable they are, what they cost to run and how much testing they need. Choosing the simplest one that does the job is a large part of building a reliable system. In every case, decisions with financial, legal or reputational consequences stay with a person.
Rule-based automation
A fixed sequence of steps triggered by an event, with no model involved.
Predictable, cheap to run and easy to test. The right choice whenever the steps can be written down as rules.
ExampleA form submission creates a CRM record, assigns a task and sends a notification.
AI-assisted workflow
An automation with one or two AI steps: reading an email, classifying a request, extracting fields.
Handles messy input that rules cannot. Output is checked against a defined structure, and uncertain cases go to a person.
ExampleIncoming invoices are read, checked against the order and queued for review when something doesn’t match.
Conversational assistant
A chat interface that answers questions and collects information, limited to approved content.
Quality depends on the content it draws from and on clear rules for handing over to a person.
ExampleA website assistant explains the services and prepares an enquiry for the team.
Knowledge system (RAG)
An assistant that retrieves passages from your documents and answers from them, with citations.
Users can check the sources behind each answer; whether it is correct still has to be tested. Needs maintained documents, permission-aware retrieval and regular quality checks.
ExampleAn employee asks how a procedure works and gets the steps with links to the source documents.
Tool-using agent
A model that chooses between permitted tools and takes several steps toward a goal.
Flexible where tasks vary, but less predictable, more expensive and harder to test. Needs narrow permissions, limits and approval checkpoints.
ExampleAn agent checks an order, reads the returns policy and proposes a resolution for an employee to approve.
Custom AI application
A new product or internal tool built around an AI capability, with its own interface, accounts and data.
The most effort and the most control. Worth it when no existing tool fits and the capability is central to the work.
ExampleA secure app where staff upload contracts, compare clauses and generate structured drafts.
How we work
From a defined problem to a working system.
Discover
We start with the business process, the people involved and the result that should improve, and review the existing tools, data and constraints.
Define
We agree the scope, the dependencies and how success will be measured. Where possible, the first step is one focused use case that can be tested before anything is expanded.
Prototype
We test the riskiest technical assumptions on representative examples, which shows the limitations early and lets us adjust the approach before the main build.
Build & integrate
We develop the solution, connect the approved systems and set up the interfaces, permissions and operating rules.
Validate
We test representative scenarios, including incorrect input, missing data and actions that need human approval.
Deploy & improve
We prepare the system for daily use, document it and agree monitoring, support and next steps.
Responsible by design
Intelligent systems need clear boundaries.
AI systems may handle business information, customer records and operational processes. Their design should reflect that responsibility.
Grounded
Where facts matter, systems answer from relevant, approved sources rather than from the model’s general knowledge.
Controlled
Systems act only within explicitly defined permissions. Sensitive actions require a person’s approval before they run.
Evaluated
Systems are tested on realistic examples, including cases where the right response is to admit missing information or hand over.
Maintainable
Every deployment has documentation, monitoring, a named owner and a practical way to improve it.
Data handling, hosting region, model provider and retention settings are agreed during scoping. Where data must remain within your own infrastructure, we assess self-hosted models or an approved private environment. Regional cloud services may meet data-residency requirements, but they are not the same as processing data entirely within your own infrastructure. Each option has its own trade-offs in quality, cost and maintenance.
Questions
No. Most projects begin with an operational problem rather than a technical specification. We can review the current process and help decide whether automation, an assistant, a custom application or a simpler software change is the right approach.
Often, yes. It depends on the available APIs, integrations, permissions and technical limits. We check these before confirming the approach.
Not necessarily. Where practical, we work with the software you already use and connect it. A replacement is worth considering only if the current platform creates limits that cannot reasonably be worked around.
Only where you decide it should. Routine, low-risk steps can run automatically. Sensitive actions can require a person’s confirmation, and permissions follow the business process.
That depends on the provider and the terms of the service used. Business API offerings from the major providers generally do not train on customer data by default, but terms differ and change, so we review them for your case before any production data is connected. Consumer chat subscriptions are not a suitable basis for confidential workflows.
It depends on the task. Models from providers such as Anthropic or OpenAI, used through their business APIs, are chosen per project for quality on your examples, cost, data terms and hosting region. The application is built so the model can be changed later.
We agree the measures before implementation. Depending on the project, they may include time saved, manual steps removed, completion rates, answer quality, error rates, processing cost or how many employees actually use it.
Yes. A focused pilot is often the most practical way to test an approach and see whether it is useful before committing to a larger implementation.
The complexity of the workflow, integrations, data quality, security requirements, user permissions, testing and maintenance. Third-party subscriptions, cloud infrastructure and model usage add ongoing costs, which we estimate up front.
Yes, as a separate agreement scoped to the project. It may include monitoring, technical fixes, integration maintenance, evaluation updates and further development.
Current models handle many languages well, but quality varies by language and task. We test each language with representative examples before launch.
Usually a description of the process or problem, access to the relevant systems where appropriate, representative example data and a person who can review and approve key decisions. The details are settled during discovery.
AI where it earns its place. Automation where simple rules will do.