Most AI projects stall between prototype and production. Our team in England gets models into live systems within 8 to 14 weeks, tested against your real traffic and edge cases.
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Each engagement starts with your specific bottleneck. We do not sell generic platforms or dashboards nobody opens.
Computer vision models that inspect products on your existing camera feeds. We train on your defect library, typically 500 to 2,000 labelled images, and integrate via a lightweight API that returns pass/fail in under 40 ms.
Time-series models built on your sales, weather, and calendar data. One logistics client reduced overstock by 22% in the first quarter after deployment. We retrain monthly so accuracy does not drift.
Natural language models that tag inbound messages by urgency, topic, and sentiment. Trained on your ticket history, not generic corpora. Average accuracy after fine-tuning: 91% across 15 intent categories for a recent SaaS client.
Receipts, invoices, contracts: we build OCR + NLP chains that pull structured fields into your ERP. One client in the financial sector cut manual data entry from 12 hours a week to 45 minutes.
Unsupervised models that flag unusual patterns in sensor readings, transaction flows, or network logs. We set alert thresholds collaboratively so your ops team gets signal, not noise.
Internal knowledge assistants grounded in your documentation. Unlike generic chatbots, these cite the source paragraph and refuse to hallucinate. We index PDFs, wikis, and Confluence spaces into a vector store you own.
Transparency matters more than slide decks. Here is what happens once you sign.
We connect to your databases, warehouses, or file shares. Our team profiles schema quality, null rates, class balance, and volume. You receive a written report with a go/no-go recommendation. If the data is not ready, we say so and help you fix it before burning compute budget.
We train a minimal viable model and benchmark it against a simple heuristic. If the model cannot beat the heuristic by at least 8 percentage points on your chosen metric, we pivot the approach or renegotiate scope. No vanity demos.
Feature engineering, hyperparameter search, adversarial testing. We simulate edge cases drawn from your historical incidents. The model gets harder exams each sprint.
Containerised deployment on your cloud account (AWS, Azure, or GCP). We write the API contract, set up monitoring dashboards, and run shadow mode alongside your current process for two weeks.
Documentation, runbooks, and a retraining pipeline your engineers can trigger without us. We stay on a lightweight support contract for the first 90 days post-launch.
The most common reason is not bad algorithms. It is a gap between the data science team and the engineers who run production infrastructure. Models trained in Jupyter notebooks do not survive contact with real traffic patterns, schema changes, or cold-start scenarios.
Our team includes ML engineers and platform engineers in the same sprint. Every model we build has a deployment plan from day one, not as an afterthought in month six. That is why 96% of our clients stay past the first engagement.
Straight answers, no jargon walls.
Describe the problem, attach a sample if you can, and we will reply within one working day with an honest assessment of feasibility.
482 Blind Lane, St. Lueilwitz Heath, England, BM60 4TM, United Kingdom