When does AI make sense for a business?
Problems we help solve
AI works best where a business has recurring questions, documents, data or communications but lacks the time to analyse them. We start with a small use case that can be tested and expanded safely.
- Employees spend too long searching for information in documents, procedures and emails.
- The team manually summarises documents, tickets or messages.
- The business wants to test AI but is concerned about data leakage and a lack of control.
- Knowledge is dispersed across files, SharePoint, Teams and other systems.
- Repetitive tasks need support but do not require full process automation.
Fit
Who is this service for?
Businesses with extensive documentation
We build use cases around approved knowledge sources.
Businesses using Microsoft 365
We take account of SharePoint, Teams, OneDrive, permissions and access policies.
Operational teams
We support summarisation, classification and rapid access to information.
Businesses considering AI
We assess risks, data and use cases before licence or integration costs are incurred.
Service scope
What does an AI implementation involve?
Use-case selection
We identify where AI can deliver a tangible benefit.
- business knowledge,
- documents and messages,
- recurring questions.
Data preparation
We organise information sources, access and quality.
- approved documents,
- user permissions,
- up-to-date materials.
Pilot
We create a small-scale version of the solution for testing.
- a user group,
- sample questions,
- response evaluation.
Maintenance
We establish controls and a framework for further development.
- knowledge ownership,
- source updates,
- monitoring of errors and exceptions.
Outcomes
What will you receive?
- a selected AI use case with a clear business objective,
- an assessment of data, permissions and risks before implementation,
- a pilot solution tested against real examples,
- recommendations on tools, licences and integrations,
- a framework for maintaining and developing the solution after launch.
We do not begin with a promise that AI will solve everything. We begin with an area where usefulness and risk can be measured.
Delivery approach
How is the service delivered?
- Objective. We define the problem that AI is expected to address.
- Data. We review the sources, permissions and quality of information.
- Design. We select the tool, constraints and testing approach.
- Pilot. We launch the solution for a selected group.
- Assessment. We review the responses, errors and benefits.
- Decision. We recommend expanding the solution, changing its scope or stopping the project.
Delivery standards
How do we control risk?
- We do not connect AI to data without establishing permissions.
- We separate the experiment from the company's production process.
- We test answers on examples used by the team.
- We determine the owner of knowledge sources and responsibility for keeping them up-to-date.
- We document limitations so users know when not to trust an automated response.
Cost
What determines the price?
The price depends on the use case, the number of data sources, security requirements, integrations, licensing, the number of pilot users and the scope of ongoing maintenance.
FAQ
Frequently asked questions
Can AI use company files?
Yes, but this requires access controls, reliable sources and clear rules of use.
Do we need to buy licences straight away?
Not always. It is worth validating the use case first, then selecting the appropriate tool and licensing model.
Will AI replace employees?
In most cases, it supports employees with searching, summarisation and classification, while decisions and accountability remain with the business.
Can we run a pilot?
Yes. A pilot is the best way to assess usefulness without taking on significant risk.
Can you help us develop an AI use policy?
Yes. We can prepare rules of use, restrictions and good-practice guidance for your team.