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Engine Hook

AI implementation & business automation

Make AI work for your business.

We implement AI agents, automate business workflows and integrate AI into the systems your team already uses. For SMEs and mid-sized companies that want results in production, not another pilot.

Discuss an AI Project See How We Implement AI
Example: supplier invoicesIllustrative

  1. Input

    Invoice arrives

    PDF attachment in a monitored inbox

  2. AI step

    AI extracts fields

    Supplier, dates, line items, totals, PO number

  3. Rule / check

    Validate and match

    Required fields present, totals agree with the PO

  4. Human approval

    Review exceptions

    Mismatches go to a person with the reason

  5. Output

    Post to accounts

    Approved invoices recorded, every step logged

AI does the reading. Rules do the checking. People approve the exceptions.

The problem

The work that quietly slows your business down

Most AI value sits in ordinary operational work. These are the patterns we look for first.

01

Re-keying between systems

Details arrive in emails and PDFs, then someone types them into the CRM, the ERP or a spreadsheet. Slow, error-prone and hard to audit.

02

Requests waiting in inboxes

Enquiries, orders and support requests sit until someone has time to read, sort and route them. Response times depend on who is in.

03

Knowledge in a few heads

Policies, procedures and product answers live with a handful of experienced people, who get interrupted all day.

04

Pilots that never ship

A promising AI experiment works in a demo, then stalls: no integration, no owner, no plan for the cases it gets wrong.

What we do

Implementation first. Everything else supports it.

One flagship service, delivered end to end, with focused services around it.

Flagship service

AI Implementation, End to End

From a defined business problem to a tested, deployed and documented AI-enabled solution, connected to the systems you already run.

How implementation works
  • Discovery and opportunity assessment
  • Solution design and tool selection
  • Integration with your systems
  • Testing, deployment and monitoring
  • Documentation, handover and team enablement

AI Workflow Automation

Take the repetitive steps out of processes like document handling, inbound requests and reporting, with people approving what matters.

Learn more

AI Agents and Assistants

Assistants that look things up, draft and act inside your tools, within permissions you set and with human approval where it counts.

Learn more

AI Training and Adoption

Role-specific training that helps your team use the systems we implement, and use AI responsibly in their daily work.

Learn more

AI integration and custom systems

Connecting AI to your CRM, ERP, document stores and internal tools, or building a focused internal tool where nothing suitable exists.

AI operations and optimisation

Agreed support after launch: monitoring, fixes, model and prompt updates, and improvements as your processes change.

How it works

Five steps from problem to production

The same disciplined process on every engagement, scaled to the size of the problem.

  1. 01

    Discover

    We map the workflow as it runs today: who does what, in which systems, and where the time and errors go. Then we agree which problem is worth solving first.

    You getA prioritised list of use cases and a recommended starting point.

  2. 02

    Design

    We design the solution around your existing systems: the data flow, the AI steps, the rules, who approves what, and what happens when something fails.

    You getA written design with scope, exclusions and acceptance criteria.

  3. 03

    Build and integrate

    We build the workflow or agent and connect it to the tools your team already uses, with access limited to what the job needs.

    You getA working solution in a test environment.

  4. 04

    Test and deploy

    We test against real examples, including the awkward ones, then deploy with logging and monitoring in place.

    You getA deployed solution, test results and documentation.

  5. 05

    Adopt and optimise

    We train the people who use it, review how it performs, and improve it within the agreed support period.

    You getA team that uses the system, and a clear handover.

See the approach

See how we design for real operations

Three demonstrations, documented step by step: the problem, the design, the safeguards and the limits. They use sample data and are not client deployments.

All work
Demonstration · design

Intelligent Business Workflow

A supplier invoice arrives by email. AI reads it, the workflow checks it against the purchase order, and only clean invoices are posted. Everything else goes to a person.

See how it works Demonstration · design

Company Knowledge Assistant

An assistant that answers staff questions from a controlled set of company documents, shows where each answer came from, and says so when the answer is not there.

See how it works Demonstration · design

AI Sales Operations Assistant

A new lead comes in. The assistant researches it from approved sources, drafts a recommendation and a reply, and waits for approval before anything reaches the customer or the CRM.

See how it works

Why it holds up

Built to be trusted in daily operations

AI that works in a demo is easy. AI your team relies on every day needs discipline around it.

Scope in writing

Every engagement starts with agreed scope, deliverables, exclusions, assumptions and acceptance criteria. You know what you are buying before work starts.

People approve what matters

Consequential actions, like sending to a customer or posting to your accounts, go through human approval unless you decide otherwise.

Tested on the awkward cases

We test against real examples from your workflow, including the incomplete, the ambiguous and the wrong, before anything goes live.

Documented and handed over

You get documentation your team can use, and a handover that means the solution does not depend on us to keep running.

Technology

Vendor-neutral by design

We do not sell a platform, and we do not force one stack onto every project. Models, automation tools, cloud services and integrations are chosen per project, and the reasons are written down.

What decides the choice

  • The business problem, not the latest tool
  • Systems you already run
  • Integration options and their limits
  • Data privacy and security requirements
  • Reliability and ease of maintenance
  • Cost to build and cost to run
  • Your team’s technical capability
  • Vendor lock-in and portability
  • Expected performance and volume
  • Who supports it long term

Adoption

A system only works if your team uses it

Training is tied to what we implement: role-specific sessions on the new workflow, practical AI literacy, and clear guidance on responsible use.

  • Role-specific workshops on the implemented workflow
  • Practical AI literacy for teams and managers
  • Responsible-use guidance your team can follow
Training and adoption

Looking for individual courses? Visit the Academy.

Questions

What buyers usually ask

What kinds of AI projects do you implement?
Workflow automation (documents, inbound requests, reporting), AI agents and assistants that work inside your tools, AI integrated into existing systems such as your CRM or ERP, and focused internal tools. We usually start with one clearly bounded use case and expand from there.
Do we need to know which AI tool we want?
No. We choose tools after we understand the problem and your systems, weighing integration, data requirements, cost to run, reliability and how easily your team can support it. We are not tied to a single vendor.
Can you work with our existing software?
Usually, yes. Most business systems can be connected through an API, an integration platform or a controlled export. Discovery confirms the options before any build starts. If a system cannot be connected safely, we say so.
How does an engagement begin?
With a conversation about the workflow and what a good outcome looks like. If there is a fit, the usual first step is an AI Opportunity Assessment: a short, fixed-scope engagement that ends with a prioritised roadmap and a recommendation.
How are projects priced?
Against an agreed scope. Before work starts you receive a written proposal with scope, deliverables, exclusions, assumptions and acceptance criteria. Third-party costs such as AI model usage, cloud hosting and software subscriptions are listed separately from implementation and support fees.
How do you handle sensitive business data?
We ask for the minimum access the work needs, prefer to work inside your own accounts and environments, and use anonymised samples during discovery where possible. Tool and provider choices take your data requirements into account, and data handling is agreed in writing.
What happens after deployment?
You get documentation, a handover and a defined period for fixing defects. Ongoing monitoring and improvement is available as a separate, agreed engagement.
Can you work remotely with our team?
Yes. Engine Hook is set up for remote delivery: video workshops, shared documentation, and access to your systems through accounts you control.

Have a process that should run itself?

Tell us about the workflow, the systems involved and what a good outcome looks like. If AI is the right tool, we will show you where to start. If it is not, we will tell you.

Discuss an AI Project See How We Implement AI