Where AI earns its place in operations
Language models are very good at a narrow set of business tasks: reading unstructured text and turning it into structured data, deciding which category something belongs in, summarising long threads, and drafting a first version of a routine reply. They are unreliable at arithmetic, at knowing facts they were not given, and at making commitments on your behalf.
We integrate AI into the first set of tasks and keep it away from the second. Every AI step we build has a defined input, a defined output, a check on that output, and a fallback for when the check fails.
What we integrate AI into
Email and ticket triage
Classify incoming messages by intent and urgency, extract key details and route them.
Document understanding
Read documents of varying layouts and return structured fields. See document processing.
Draft replies
Suggest responses to routine requests using your policies and order data, for a person to approve.
Knowledge search
Answer staff questions from your procedures, contracts and product data, with sources cited.
Data clean-up
Standardise names, addresses and product descriptions, and flag likely duplicates.
Summaries and handovers
Turn long email threads, call notes or case histories into short, consistent summaries.
The controls we build around every AI step
- Structured outputsThe model returns defined fields, validated before anything uses them.
- GroundingAnswers are based on documents and data you provide, not the model's general knowledge.
- Confidence thresholdsLow-confidence results go to a person instead of proceeding.
- Evaluation setsA test set of real examples, re-run whenever prompts or models change.
- LoggingInputs, outputs and decisions recorded for review and audit.
- Cost limitsUsage tracked per workflow, with alerts before spend exceeds budget.
An example AI integration
A narrow, well-controlled AI step often delivers more than an ambitious one.
- Before
- Two people read every email and forward it to the right team, adding hours of delay at busy times.
- AI step
- A language model classifies each email into one of twelve agreed categories and extracts the order or account number, returning structured data.
- Controls
- Low-confidence results go to a person; a weekly sample is checked; a test set of real past emails is re-run before any prompt or model change.
- Measure
- Routing accuracy, time to first response and the share of emails handled without manual sorting.
How an AI integration project runs
Define the task
Inputs, outputs, success criteria and what happens when the AI is unsure.
Build an evaluation set
Real past examples with correct answers, including hard cases.
Prototype and measure
Models and prompts compared on the evaluation set for accuracy and cost.
Integrate and monitor
Connected to your systems with logging, thresholds and review queues.
Common questions
Which AI models do you use?
We use business-grade models from the major providers and choose per task based on accuracy, speed, cost and data-handling terms. Workflows are built so the model can be changed without rebuilding everything.
Will our data be used to train AI models?
No. We use API and enterprise offerings whose terms exclude training on customer data, and configure retention settings to match your requirements.
What does AI usage cost to run?
For most operational workflows, model usage costs are small compared with the labour they replace, often tens to a few hundred dollars a month. We estimate it during the assessment and set limits in production.
Can AI run without human review?
For low-risk, high-confidence tasks such as routing an email, yes, once accuracy is proven in the pilot. For anything involving money, customer commitments or compliance, we keep a person in the approval step.
What if a better model comes out?
Workflows are built so the model can be swapped. We re-run the evaluation set on the new model and switch only if it performs better at an acceptable cost.
Find out what one process could save you
Book a paid automation assessment. We map the process with your team, measure the time it takes, and give you a fixed-price build plan with expected savings. If automation is not worth it, we will tell you.