We build machine learning pipelines, natural language processors, and computer vision systems that solve real problems. No hype, no jargon. Just models that work in production and deliver measurable returns within weeks.
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We started Pro AI Tactics because too many companies were paying for AI projects that never left the proof-of-concept stage. Our founding team spent years inside large enterprises watching promising models gather dust because nobody planned for deployment, monitoring, or retraining.
So we built a consultancy that treats deployment as the starting line, not the finish. Every engagement begins with a hard look at your data infrastructure, your team capabilities, and the specific business metric you want to move. If a spreadsheet formula would solve the problem, we say so.
Based in England, we serve clients across the UK and Europe. Our team of twelve includes applied researchers, MLOps engineers, and a data ethics specialist who reviews every project before it ships.
Six core capabilities, each backed by production-tested tooling and clear deliverables.
We train regression and classification models on your historical data to forecast demand, churn, equipment failures, or revenue. Most clients see a usable prototype within three weeks and a production API within six.
From intent classification in customer support tickets to entity extraction from legal contracts, we fine-tune transformer models on your domain vocabulary. Average accuracy improvements over generic APIs: 12 to 19 percentage points.
Quality inspection on manufacturing lines, shelf-stock detection in retail, document digitisation for insurance claims. We handle annotation, model training, and edge deployment on NVIDIA Jetson or equivalent hardware.
Models without monitoring decay fast. We set up automated retraining pipelines, drift detection alerts, and A/B testing frameworks so your models stay accurate as your data changes. We work with AWS SageMaker, GCP Vertex AI, and self-hosted Kubernetes clusters.
Before writing a single line of code, we audit your data estate. Where is it stored? Who owns it? What governance gaps exist? The output is a prioritised roadmap that tells you which AI use cases are feasible now and which need data engineering first.
Your ERP, CRM, or warehouse management system probably has an API. We connect trained models directly into those workflows so predictions appear where your staff already work, with no extra dashboard to check.
Four phases, clear milestones, and a fixed-price option for every stage.
We interview stakeholders, review your data catalogue, and define the target metric. This takes five to ten working days and ends with a feasibility report.
Our engineers build a minimum viable model using a representative sample of your data. You see results in a Jupyter notebook or a simple web app within three weeks.
We containerise the model, write integration tests, and deploy it behind a versioned REST API. Load testing, security review, and documentation are included.
Monthly performance reports, automated drift alerts, and scheduled retraining runs. If accuracy drops below the agreed threshold, we retrain at no extra cost under our SLA.
Honest answers to the questions prospective clients ask most often.
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160 Annie Copse, Schmitt-over-Wiegand, SX78 0HD, England, United Kingdom
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