Rapid AI Prototypes
Our proof of concept service is not just about demonstrating what's possible, it's about establishing what's practical, profitable and tailored to your business needs.
What you come away with
AI opportunity definition
We will work together to develop a rapid AI PoC, enabling you to explore opportunities and determine how AI can generate value for your business.
Rapid solution exploration
A PoC helps to validate the feasibility of an idea. It allows you to test the proposed AI system on a smaller scale before fully committing to its development.
Return on Investment assessment
We concurrently assess the technical feasibility and the return on investment of the proposed AI solutions. The process helps build stakeholder confidence, validating the feasibility and practicality of the system.
Scope and budget for a full-scale production build
Receive an accurate estimate of the resources required to develop and implement the production ready AI system. This includes not only compute resources but also time and personnel.
Commercials
£40,000
to test your concept
30 Days
development period
London
based team
3 Engineers
working full-time
20% back
of the PoC cost on the MVP build
1 Project Manager
focused on successful delivery
What are the benefits of partnering with Mercury Labs
Results Orientation
Our team is focused on maximising real-world business impact, not just technical elegance.
Deep AI Expertise
Our multidisciplinary team has a broad range of experience using AI solutions for businesses
Full Technology Stack
Our full-stack engineering capabilities cover everything from data infrastructure to front-end UX.
Cost-Effective
It can be more cost-effective, especially if you don’t have an in-house AI team.
Focus on Ethics
We prioritise fairness, interpretability, and transparency to ensure your AI is socially responsible.
Project Perspective
We can provide an objective perspective on the project.
Case Study
Develop a proof of concept to evaluate whether an AI system can be used to improve verification qualify of manual human covid test verification.
Project Insights
Trained object detection and image classification models using Google's cloud machine learning services on existing LFT datasets.
Near 99% accuracy achieved with the classification model, but more negative <redacted> samples were required for validation.
Inconclusive, incomplete, or faulty tests were not addressed in the models.
Business Insights
AI driven labelling can reduce costs by 40-50%
Placing the model inside the verification process can reduce human error.
Using cloud based machine learning services could speed up AI development.
Production ready development would take a further 4 months.
Stakeholders able to plan against "getting left behind".
Aim to roll out alongside human verifiers to build confidence in the model before increasing dependency on AI.
