Your team spends hours on document sorting, schedule conflicts, and chasing data anomalies. We write the AI that handles those tasks in the background, so your people focus on decisions that actually matter.
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Most teams know where they waste time. They just lack the tooling to stop it. Here are four patterns we see repeatedly, paired with what our AI software actually does about each one.
Invoices, purchase orders, compliance forms: someone on your team reads each one, classifies it, and routes it to the right folder or person. That process eats 12 to 15 hours per week for a typical 30-person operations team. Errors creep in after lunch, and backlogs pile up on Mondays.
Our document-processing model reads incoming files within two seconds, tags them by type, extracts key fields (dates, amounts, counterparties), and pushes them into your existing system. The model trains on your own historical data, so it learns your naming conventions and edge cases inside the first week.
Resource planners juggle people, rooms, and equipment across shifting deadlines. Spreadsheets can track what happened yesterday, but they cannot predict what will clash next Thursday. By the time a conflict surfaces, someone has already driven to the wrong site.
We feed your calendar, task, and resource data into a constraint-satisfaction model layered with a lightweight forecaster. It flags probable conflicts 48 hours out and suggests two or three alternative arrangements ranked by cost and travel time. Planners approve with one click.
A sensor drifts, a payment pattern shifts, a supplier quietly changes terms. These anomalies are obvious in hindsight but invisible in real time when you are watching dozens of dashboards. The cost of catching them late ranges from expensive to catastrophic.
Our monitoring layer sits on top of your data streams (IoT feeds, transaction logs, ERP events) and applies statistical deviation models tuned to your thresholds. Alerts arrive via email, Slack, or SMS with a plain-English explanation of what changed and why the system thinks it matters.
End-of-month reports pull numbers from five sources, require manual formatting, and still need a senior manager to write the narrative. The whole ritual takes two to three days and produces a PDF nobody reads past page four.
Our report builder queries your databases on schedule, assembles charts, and writes a summary paragraph for each section using a language model fine-tuned on your previous reports. The draft lands in your inbox as an editable document. Most clients cut reporting time by 80%.
We follow a five-stage process. No stage takes longer than two weeks, so the full cycle finishes inside six weeks for a standard deployment.
We shadow your team for a day, map every manual touchpoint, and identify the three highest-impact automation targets. You receive a short written brief with estimated time savings per target.
Our engineers connect to your data sources (databases, APIs, file shares) and evaluate volume, quality, and labelling. If your data needs cleaning, we scope that work separately and honestly.
We build a working prototype on a sample of your real data. You test it in a sandbox environment and give feedback. Iteration happens daily, not in fortnightly sprint reviews.
The validated model connects to your production systems. We add logging, error handling, and rollback mechanisms. Security review happens here, including data-residency checks for UK-based hosting.
Your team gets a two-hour training session, a runbook, and access to a monitoring dashboard. We stay on a support contract for the first 90 days, with a four-hour response SLA.
We share numbers, not adjectives. Each figure below comes from a client who agreed to let us publish their results anonymously.
A 120-driver fleet used spreadsheets for route planning. After deploying our predictive scheduler, missed-delivery incidents dropped from 31 per month to 8. The ops manager now spends mornings on strategy instead of firefighting.
Paralegals were manually sorting 400+ incoming documents daily. Our classifier handles 96.8% of them without human review. The remaining 3.2% get flagged for a quick manual check, which takes about 20 minutes total.
Sensor drift on a production line went unnoticed for weeks at a time, causing batch rejections. Our anomaly detector caught the first drift event within 14 minutes of onset. Reject rates fell by 61% over one quarter.
No. We handle model development, training, and deployment. Your team needs only a point of contact who understands the business process we are automating. Technical integration is on us.
All data remains on UK-based servers unless you specifically request otherwise. We use Azure UK South or AWS eu-west-2 (London) by default. Data never leaves the region without written approval from your side.
Every prediction carries a confidence score. Items below your chosen threshold get routed to a human reviewer instead of being auto-processed. We also log every decision the model makes, so you can audit any output after the fact.
A single-model deployment (for example, document classification) starts at £8,500. Multi-model projects with custom integrations range from £18,000 to £35,000 depending on data complexity. We quote after the workflow audit, not before.
Yes. Each stage is billed independently. If you decide after the prototype that the ROI is not there, you pay only for stages completed. We keep the prototype code in escrow for 12 months in case you want to resume later.
Tell us what process is eating your team's time. We will reply within one business day with an honest assessment of whether AI is the right fix.
