AI applied to a defined problem, with a number attached
Most AI projects fail because they start with the technology rather than a task worth automating. We start with the task, the volume, and what a mistake costs.
- Automation targeted at tasks with a measurable payback
- Accuracy stated against a human baseline, not asserted
- Low-confidence cases escalated instead of guessed
The problem we hear most, and how we solve it
If this sounds like your business, you're in good company. Here's what changes when we work together.
The problem
There is real pressure to 'do something with AI', which produces pilots that demo well and never reach production. The projects that do pay back tend to be unglamorous: reading four hundred supplier invoices a week, routing support tickets, forecasting demand for a thousand SKUs, finding the right document in ten years of archives.
What we do about it
We identify tasks that are high-volume, rule-ambiguous and currently manual, then quantify them: how many per week, how long each takes, what an error costs. Only then do we choose an approach — often a small model or classical technique rather than the largest language model available, because accuracy per unit cost matters more than sophistication.
How we can help with AI & Intelligent Automation
Document processing
Invoices, purchase orders, delivery notes and forms read and structured automatically, with low-confidence extractions routed to a person.
Demand forecasting
SKU-level forecasting from your sales history, with seasonality and promotions accounted for, feeding directly into reorder points.
Classification and routing
Support tickets, emails and requests categorised and routed automatically, with the model retrained on how your team actually corrects it.
Intelligent search
Semantic search across documents, tickets and knowledge bases that finds the right answer when nobody remembers the exact wording.
Workflow automation
Rule-based automation for the steps that do not need a model at all — frequently the cheapest and most reliable win available.
Human-in-the-loop by default
Confidence thresholds, review queues and audit trails, so automation degrades into human review rather than into silent errors.
Everything you need, handed over properly
No vague promises. Here's what you can expect to have when the work is done.
Tools & technologies we use
- Python
- Machine Learning
- LLM Integration
- ASP.NET Core
- PostgreSQL
- Azure
- Docker
- Automation targeted at tasks with a measurable payback
- Accuracy stated against a human baseline, not asserted
- Low-confidence cases escalated instead of guessed
- Full audit trail of automated decisions
- Running costs modelled before you commit to production
A clear process, with no surprises
A short assessment produces a shortlist with estimated accuracy, cost and payback for each. We build the highest-value one as a bounded pilot measured against a human baseline. Production versions ship with a confidence threshold and human review for low-confidence cases — the goal is reliably removing eighty percent of the work, not pretending to remove all of it.
- 1
Meet Up
Schedule a meeting with us. Tell us about your business and help us understand your priorities and needs.
- 2
Team Up
After you've approved our proposal, we'll seamlessly implement the solutions so you immediately start benefiting from your partnership with ITS.
- 3
Scale Up
See how managed technology can set you up for success and continue growing your business. If not, we'll happily refund your investment.
A production system handling a real workload with measured accuracy, a monitored confidence threshold, and a clear escalation path for the cases it should not decide alone.
What every client can count on
Say the difficult thing early
If an approach is wrong, a date is at risk, or the feature you asked for will not solve the problem, you hear it while there is still time to act. Comfortable silence is the most expensive thing in software.
Build it to be maintained
The code we write will be read far more often than it was written, frequently by someone else. Clarity beats cleverness in every case where the two conflict.
Measure what we claim
Faster, better and more reliable are only meaningful with a baseline and a number after. We agree the measurement before starting the work.
Leave you independent
Documentation, handover and code quality are aimed at a client who could take the system entirely in-house. Retention through lock-in is not a business we want.
Questions clients usually ask
Start with the assessment. It looks at your highest-volume manual tasks, estimates achievable accuracy and running cost, and produces a shortlist with payback periods. Sometimes the honest conclusion is that a well-built rules engine solves it for a tenth of the cost, and we will say so.
No. Where hosted models are used we select deployments with no-training guarantees and appropriate data residency. Where the data is sensitive enough, we run models in your own environment. This is agreed in writing before any data moves.
It depends entirely on the task and the quality of your data, which is why the pilot exists. Structured document extraction commonly lands in the mid-to-high nineties; open-ended classification is lower. We report measured accuracy on your data rather than quoting benchmark figures.
That is assumed rather than hoped against. Confidence thresholds route uncertain cases to human review, every automated decision is logged and reversible, and accuracy is monitored in production so drift is caught rather than discovered.
More answers in the full FAQ.
Often combined with
- Custom Software DevelopmentSystems built around how your business actually works
- Web Application DevelopmentFast, secure applications that hold up under real load
- Mobile App DevelopmentAndroid and iOS apps people keep on the home screen
- ERP & Business Management SolutionsOne system for finance, inventory, sales and operations
- UI/UX DesignInterfaces that reduce the work, not just the clicks
- Cloud & DevOpsInfrastructure you can deploy to on a Friday
- API & System IntegrationConnecting systems that were never designed to talk
Ready to talk about ai & intelligent automation?
Tell us what your current process is costing you in time, errors or missed opportunities. That's where every useful conversation starts.
- We reply to project inquiries within one business day.
- Ready-made products to configure, or software built from scratch