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.

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.

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.

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.

Commercials

Test your idea / £40,000
Test your idea
/ £40,000
Develop your idea / 30 days
Develop your idea
/ 30 days
London based team
3 Engineers working full time
20% back of the PoC cost on the MVP build
1 Project Manager focused on project delivery

Partnering with Mercury Labs

Guided by a relentless commitment to simplifying your business journey through intuitive and effective Notion templates.

Results Orientation

Our team is focused on maximising real-world business impact, not just technical elegance.

Deep AI expertise

Our multidisciplinary team has the AI

Full Technology Stack

Our full-stack engineering capabilities cover everything from data infrastructure to front-end UX.

Project Perspective

We can provide an objective perspective on the project.

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.

Case Studies

x-medical
x-medical
x-medical

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.

Case Studies

Contact us

Office

25 Eccleston Place
SW1W 9NF
London
United Kingdom

Commercials

Test your idea / £40,000
Develop your idea / 30 days
London based team
3 Engineers working full time
20% back of the PoC cost on the MVP build
1 Project Manager focused on project delivery

Partnering with Mercury Labs

Guided by a relentless commitment to simplifying your business journey through intuitive and effective Notion templates.

Results Orientation

Our team is focused on maximising real-world business impact, not just technical elegance.

Deep AI expertise

Our multidisciplinary team has the AI

Full Technology Stack

Our full-stack engineering capabilities cover everything from data infrastructure to front-end UX.

Project Perspective

We can provide an objective perspective on the project.

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.

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.

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.

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.

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.

Commercials

Test your idea / £40,000
Develop your idea / 30 days
London based team
3 Engineers working full time
20% back of the PoC cost on the MVP build
1 Project Manager focused on project delivery

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.

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.

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.

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.