We build artificial intelligence that fits your actual business

Most AI projects fail because they start with the technology instead of the problem. We work the other way round: you describe the bottleneck, we figure out whether a model, a rule engine, or a simple script is the right fix. If a neural network is overkill, we will tell you.

Send us your data problem
Engineers reviewing AI data visualisations in a modern office

Four weeks from question to working prototype

01

Data audit

We look at what you already collect, what is missing, and what is noisy. This takes three to five days.

02

Hypothesis

We write a one-page brief that states the prediction target, the baseline accuracy, and the cost of being wrong.

03

Prototype

A minimal model trained on your real data, tested against the baseline. You see results in a dashboard, not a slide deck.

04

Decision

You decide whether to deploy, iterate, or stop. No lock-in contracts. The code and the model weights are yours.

What we build

Predictive models

Demand forecasting, churn scoring, equipment failure prediction. We train gradient-boosted trees or small neural nets depending on your data volume. Typical training set: 10,000 to 500,000 rows. Delivery includes the model file, a REST endpoint, and monitoring alerts for data drift.

Computer vision

Quality inspection on production lines, document digitisation, and aerial image analysis. We fine-tune pre-trained detection networks (YOLO, EfficientDet) on your labelled images. A recent project for a packaging company reduced false rejects by 38% in the first month.

Natural language processing

Ticket classification, sentiment extraction from customer reviews, and contract clause tagging. We use transformer-based models sized to your throughput requirements so you are not paying for GPU time you do not need.

Data pipeline engineering

Your model is only as reliable as the data feeding it. We design extraction, transformation and loading workflows using Airflow or Prefect, with automated tests that catch schema changes before they reach the model.

Ongoing model support

Models decay. Customer behaviour shifts, product catalogues change, sensor calibration drifts. We offer quarterly retraining cycles with performance reports, or we set up automated retraining triggered by accuracy thresholds you define.

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Models deployed since 2021
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Industries served
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Whiteboard with hand-drawn AI architecture diagrams in our office

Who we are

Authentica AI started in 2021 when two machine-learning engineers got tired of watching six-figure AI budgets produce nothing but PowerPoint decks. We wanted a consultancy that writes code in week one, not month three.

Our team now numbers seven people: five ML engineers, one data engineer, and one project lead. Everyone codes. Nobody carries the title "strategist."

We work from 509 Jay Row, North Reinger-Leuschke, England, YG93 4JS, but most of our client work happens on-site during the audit phase and remotely after that.

Questions we hear often

How much data do I need before an AI project makes sense?
It depends on the problem type. For tabular prediction (churn, demand), a few thousand labelled rows can be enough if the signal is strong. Image classification usually needs at least 500 labelled images per category, though transfer learning can stretch that. During the data audit we give you a frank assessment: if your dataset is too small, we will say so rather than build something unreliable.
Do you work with companies outside England?
Yes. About a third of our current clients are in mainland Europe. The audit phase can be done over video calls if travel is impractical, though we prefer at least one in-person session to understand your physical operations.
What does a typical project cost?
The four-week prototype phase is a fixed fee. We quote after the initial call because scope varies hugely: a churn model on clean CRM data is a different job from a defect-detection system that needs new camera hardware. Rough range for the prototype: £8,000 to £25,000. Production deployment and support are quoted separately.
Who owns the model and the code?
You do. We deliver source code, trained model weights, and documentation in a private Git repository that belongs to your organisation. If you want to bring the work in-house later, you can.
Can you integrate with our existing software stack?
We have deployed models behind REST APIs consumed by SAP, Salesforce, custom ERPs, and plain spreadsheets via a lightweight web interface. If your stack is unusual, tell us in the first call and we will confirm compatibility before any money changes hands.

Talk to us about your project

Ring or email and describe the problem in two sentences. We will reply within one working day with an honest take on whether AI is the right tool.

+44 7550 163108

[email protected]

509 Jay Row, North Reinger-Leuschke, England, YG93 4JS