AI FOR DAILY BUSINESS TASKS
AI trained for your business tasks
Your team should not spend its day fixing the same AI mistakes. If a general tool misreads product terms or sorts requests incorrectly, we can adapt existing AI using examples from your business. You receive the adapted software, test results and estimated running costs. We first check whether simpler changes would solve the problem.
AI AT WORKExample: sorting customer requests
- Customer request
- AI suggests the right department
- Your team checks the choice
Less time sorting requests. Test the choices on your own examples before daily use.
What you get
These are possible parts of a full project. The audit identifies what you need, a separately priced pilot checks the value, and you decide on implementation after seeing the results. See what each stage includes.
Fewer repeated corrections
AI trained on your terminology, categories and required formats. Tests establish whether it improves on your current method.
Evidence you can check again
A saved set of real cases with agreed correct answers, so later updates can be checked in the same way.
Costs you can plan around
Measured speed, accuracy and cost at your expected workload, so you can weigh the benefit against the expense.
The files needed to run it
Training code, settings and model files delivered to your agreed systems. Ownership and licence terms are set out in the contract.
What we can set up
We agree which of these tasks your project needs before quoting the work.
Prepare usable examples
Remove duplicate records, correct labels and keep separate examples for training and independent testing.
Teach the task
Train an existing model to follow your categories, terminology and output format.
Learn your quality rules
Use examples your team has ranked to improve answers when correct-answer examples alone are not enough.
Check errors before use
Measure incorrect categories, missing fields and other agreed errors. Your specialists check the results.
Check speed and cost
Try suitable model sizes and processing settings on the equipment that will run the service.
Connect it to your software
Provide a connection your systems can use, with checks that the service is working and alerts when it fails.
Where this could make a difference
Possible applications, not completed client projects or promised results.
Send support requests to the right team
AI suggests a department and urgency for each ticket. Staff review uncertain cases instead of sorting every request from scratch.
Read order details from free text
AI returns information in the exact fields your software expects. This reduces manual reformatting and makes missing values easier to spot.
Use your business terminology
Adapt an existing model to specialist vocabulary for drafting and summarising documents, with staff reviewing the result.
How a project runs
We agree the task, test it with your staff and review the results before putting it into daily use.
Step 1: Check the simplest option
Compare clearer instructions, better source documents and extra training on a sample of your data.
Step 2: Agree what correct means
Your team selects test cases and correct answers before training begins.
Step 3: Train and compare
Test each version against the current method and record its mistakes.
Step 4: Put the model into use
Check speed and cost on your chosen equipment, connect it to your software and set up failure alerts.
What to check before starting
- We adapt existing models. Training a completely new general AI model is outside this service.
- Extra training is only recommended if testing shows it is needed. Clearer instructions, better documents or cleaner data may be enough.
- We check the original model licence against your intended use before training starts.
Technical details for your IT team
Tools are chosen for your existing systems and project requirements. You do not need to choose these before discussing the business task.
- Python
- PyTorch
- Hugging Face · Transformers, Datasets, PEFT
- Docker
- Cloud GPUs
- REST APIs
- Serving engines · e.g. vLLM, chosen per workloadoptional
Common questions
What does training an existing AI model mean?
We show an existing model examples of the task and correct answers. This helps it follow your terminology, categories or required format. We then test it on examples it did not see during training.
How much data do we need?
The task and the quality of the examples matter more than a fixed number. During the audit, we review what you have and identify any missing examples before quoting a pilot.
Can it run on our own equipment?
Yes, subject to the model size and your equipment. We can also use your cloud account. We check speed and accuracy on the proposed setup before launch.
Related tasks we can help with
- Keep your AI tools runningGive your team a clear way to operate AI tools: tested updates, failure alerts, cost tracking and written instructions.
- Answers and drafts from your company documentsHelp staff find a checked answer and prepare a first draft from approved company information. Source links make the answer easier to verify.
AI AT WORKCheck the results before staff rely on AI
- Choose real work examples
- Check AI results against correct answers
- Your team decides whether to launch
Find incorrect answers and missing information during testing, before the tool enters daily use.
CHECK THE BUSINESS CASE
Will this save your team enough work?
We agree how to measure time spent, errors and running costs. The pilot gives you evidence for the decision about a full build.
See how we test the idea
LET'S TALK ABOUT YOUR BUSINESS
What would your team do with more time?
In a 20-minute call, tell us what your team types, copies or searches for every day, and what those hours could be used for instead. We'll explore where AI could help and what we need to check.
Let's talk about your business20 minutes to explore what takes up your team's time