We build machine learning models, data pipelines, and intelligent automation tailored to the problems your team actually faces. No generic dashboards. Real answers from your own data.
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We started ValidAI Tech because too many companies were buying AI tools they didn't need and ignoring the data they already had. Our founding team spent years inside enterprise data science departments, watching projects stall because the gap between a proof of concept and a production system was never closed.
So we close it. Every engagement begins with your existing data, your existing infrastructure, and a clear question you want answered. We do not sell licences to platforms you will never fully use.
Our office sits at 14 Labadie Fold, Runolfsson-le-Weber, TD52 8PL, England, United Kingdom, and we serve clients across the UK remotely and on-site. Most of our projects run eight to sixteen weeks from kick-off to a deployed model your operations team can rely on.
Each service is delivered as a complete project with documented code, model cards, and a handover session so your internal team can maintain and retrain everything we build.
We train regression and classification models on your historical records to forecast demand, churn, equipment failure, or any outcome you track. Typical accuracy improvements over rule-based systems range from 18 to 35 percent.
From customer support ticket classification to contract clause extraction, we fine-tune language models on your domain vocabulary. One retail client reduced manual ticket routing by 72 percent in the first quarter after deployment.
Quality inspection on a production line, inventory counting from shelf images, safety-gear compliance on construction sites: we design and deploy convolutional networks that run on edge hardware or in the cloud, depending on your latency requirements.
A model is only as good as the data feeding it. We build extraction, transformation, and loading pipelines that clean, validate, and version your data automatically. Our standard stack uses Apache Airflow and dbt, but we adapt to whatever your team already runs.
Not sure where to start? We run a two-week assessment: interview stakeholders, audit your data assets, and deliver a prioritised roadmap with cost estimates. You keep the roadmap whether or not you hire us for implementation.
Deployed models drift. We set up automated monitoring that alerts your team when accuracy drops below a threshold you define, and we configure retraining pipelines so the model refreshes itself on new data without manual intervention.
We keep the process transparent. You see working code every two weeks, not a slide deck at the end.
We spend the first week understanding your business question and mapping every data source. If the data is messy, we say so early and estimate the cleanup cost before you commit.
Within three weeks we deliver a working prototype with baseline metrics. You can test it against real examples and tell us where it fails. That feedback shapes the next iteration.
We containerise the model, write API endpoints, set up logging, and deploy to your chosen environment. Load testing happens before anything touches live traffic.
Documentation, a recorded walkthrough, and 30 days of post-launch support are included in every project. After that, ongoing monitoring retainers are available month to month.
Real feedback from teams we have worked with in the past twelve months.
"They built a demand forecasting model for our warehouse in nine weeks. Stock-outs dropped by 40 percent before Christmas, which more than paid for the project."
"The NLP classifier they trained on our support tickets freed up two full-time agents. The handover documentation was thorough enough that our junior developer handles retraining on her own."
"Honest about what AI can and cannot do. They talked us out of a computer vision project that wouldn't have worked with our camera setup and proposed a simpler sensor-based approach instead."
If your question is not here, call or email us and we will answer within one business day.
Tell us about your data challenge and we will respond within 24 hours with an honest assessment of whether AI is the right approach.
