We design, train and deploy machine-learning models that fit the way your team already works. No generic dashboards, no feature bloat. You describe the problem; we ship a model that solves it, usually inside six weeks.
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Smart AI Systems Lab started in 2021 with a two-person team and one conviction: most businesses don't need a general-purpose AI platform. They need one model, trained on their data, answering one specific question really well.
Our founding engineers spent a combined twelve years inside large consultancies watching million-pound AI projects stall because scope kept expanding. So we took the opposite approach. We scope tightly, prototype in two weeks, and iterate with your feedback before we ever touch production infrastructure.
Today we are a team of nine, based in England. We have delivered predictive-maintenance models for logistics companies, natural-language classifiers for legal firms, and demand-forecasting pipelines for e-commerce brands. Each project started with a single conversation about what was costing the client time or money.
If you are not sure whether AI is the right answer, that conversation is still worth having. We will tell you honestly when a simpler solution would work better.
Every engagement starts with your data and your constraints. Here are the categories most of our projects fall into.
Time-series and regression models that forecast demand, churn, equipment failure, or cash flow. We train on your historical data, validate against a hold-out period you choose, and expose predictions through a simple REST endpoint your existing tools can call.
Document classification, entity extraction, sentiment scoring and summarisation. We fine-tune transformer models on your domain vocabulary so the system understands the jargon your staff actually uses. Typical accuracy improvements over off-the-shelf models run between 12 and 25 percentage points.
Object detection, defect inspection and image classification pipelines. We handle labelling workflows, augmentation strategies and edge deployment so the model runs on the hardware you already have, whether that is a GPU server or a Raspberry Pi on a production line.
A model is only as good as the data feeding it. We build extraction, cleaning and feature-engineering pipelines in Python and SQL, scheduled through Airflow or Prefect, with monitoring that alerts you before stale data reaches the model.
Containerised model serving, A/B testing, drift detection and automated retraining. We set up the infrastructure so your model stays accurate after launch, not just on demo day. Most clients run on AWS or Azure; we support both, plus bare-metal setups.
Not ready for a full build? We run two-day technical audits that map your data landscape, identify the highest-value AI use case, and estimate cost and timeline. You get a written report, not a slide deck full of buzzwords.
We keep the steps short and the feedback loops tight.
A 45-minute video call where we learn about your data, your pain point and your success criteria. No commitment required.
We examine a sample of your data (under NDA) and confirm whether the signal is strong enough to train a useful model. This takes three to five business days.
Inside two weeks we deliver a working model you can test against real inputs. Performance metrics, confusion matrices, example predictions: everything is documented.
Your team tests the prototype and flags edge cases. We retrain, adjust features and re-evaluate until accuracy meets the threshold we agreed on.
We containerise the model, wire it into your systems via API, and set up monitoring dashboards. Typical deployment takes one week after sign-off.
"They built a demand-forecasting model that cut our overstock costs by 18% in the first quarter. The whole project took five weeks from first call to production."
Rachel M., operations director
"Our legal team used to spend two hours a day sorting incoming documents. The NLP classifier they built handles 93% of that automatically now. Genuine time saved, not hype."
David T., head of compliance
"What impressed me most was the honesty. They told us one of our three proposed use cases wasn't worth pursuing and saved us months of wasted effort."
Priya K., CTO
Most projects fall between £8,000 and £35,000 depending on data complexity and deployment requirements. We quote a fixed price after the data review, so there are no surprises.
We can work inside your infrastructure if data governance rules require it. Many clients give us access to a sandboxed environment rather than exporting data. We sign an NDA before seeing any data at all.
Most data is. Our pipeline-engineering service handles cleaning, imputation and feature extraction. During the data review we will tell you exactly which gaps matter and which ones we can work around.
Yes. We document everything: training scripts, data schemas, retraining procedures. If your team has Python skills, they can run the retraining pipeline themselves. We also offer a monthly support retainer if you prefer us to handle it.
You will have a working prototype within two weeks of data access. Production deployment usually follows four to six weeks later, depending on integration complexity and your internal review cycles.
Tell us what problem you are trying to solve. We will reply within one business day.