How we started
Ethos Ai Bridge began in 2019 as a two-person side project. Priya Nair, a data scientist who had spent six years building recommendation engines for a Manchester e-commerce company, teamed up with Daniel Calder, a software engineer with a background in financial-services infrastructure. They kept meeting the same problem: businesses were buying AI tools they did not need, while ignoring straightforward modelling work that would have saved them money within months.
So they started offering honest assessments. Sometimes that meant telling a prospect to use a spreadsheet instead of a neural network. Word spread. By mid-2021 the team had grown to seven, and the client list included logistics firms, NHS-adjacent health-tech startups and two regional law practices automating document review.
Today we are twelve people. We still say no to projects that do not make financial sense for the client, and we still answer the phone ourselves.
What guides our work
We wrote these down early on and have not needed to change them.
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Honesty over revenue
If your problem does not need machine learning, we will tell you on the first call. We have turned away work worth tens of thousands of pounds because the simpler solution was better for the client. That honesty earns repeat business, which is worth more in the long run.
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Ownership, not dependency
Every model, every pipeline, every line of code belongs to you. We write documentation that your internal team can follow, and we offer handover training as part of every engagement. The goal is that you can run the system without us if you choose to.
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Measurable outcomes
We agree on success metrics before work begins. If the project is a demand-forecasting model, we define the acceptable error range, the baseline to beat and the evaluation window. No vague promises about "leveraging data". Concrete numbers, reviewed monthly.
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Responsible AI
We check for bias in training data, document model limitations clearly and advise against deploying systems in contexts where the risk of harm outweighs the benefit. This is not a marketing position; it is how we scope every project.
The people behind the models
We are a mix of data scientists, software engineers and one very patient project manager. Here are the founding members.
Priya Nair
Co-founder and lead data scientist. Priya holds an MSc in machine learning from the University of Edinburgh. She has published peer-reviewed work on time-series forecasting for retail supply chains and leads our model-development practice.
Daniel Calder
Co-founder and engineering lead. Daniel spent eight years building transaction-processing systems for a London bank before moving back to Manchester. He designs the infrastructure that keeps our models running reliably at scale.
Sade Okonkwo
Senior project manager. Sade keeps timelines honest and clients informed. She joined in 2021 after managing digital-transformation programmes at a Greater Manchester NHS trust, where she learned to translate between technical teams and stakeholders who just want to know when things will be ready.
Key milestones
2019 — First client
Built a churn-prediction model for a Manchester SaaS company. The model identified at-risk accounts 14 days earlier than their existing rule-based system.
2020 — Incorporated
Registered as a limited company and moved into our first shared office space on Nienow Field. Hired our third team member, a data engineer.
2021 — NHS health-tech project
Partnered with a digital-health startup to build a natural-language processing pipeline that extracted structured data from clinical letters, reducing manual data entry by 40%.
2023 — Twelve-person team
Reached twelve full-time staff. Opened a second project track focused on computer-vision applications for quality inspection in manufacturing.
2024 — European expansion
Signed our first clients in Germany and the Netherlands, delivering remote engagements with on-site discovery sprints.