About Codebrains
Putting AI into practice.
Knowledge, best practices, and practical support for adopting AI with a clear purpose.
Codebrains brings software architecture and quality engineering to applied AI: how a model fits into an existing system, how to test it, and who owns a wrong answer.
Our Insights share knowledge and best practices that connect technical choices to business value, product quality, and the responsibilities of running AI in practice.
We also support organisations in applying that knowledge: identifying useful applications, shaping the systems around the model, and establishing how to evaluate and operate them.
The work around the model
Three parts of one system.
Architecture
Understand how models, data, and existing software fit together to support an AI product.
For example: design a support assistant that connects to approved knowledge and order systems, with clear limits on the actions it can take.
Evaluation
Decide what good performance means and measure whether an AI product meets that standard.
For example: test a support assistant on real customer questions and verify answer accuracy before approving a model change.
Operational reliability
Keep an AI product dependable, affordable, and accountable during everyday use.
For example: monitor a support assistant’s failures, set response-time and cost limits, and route unresolved issues to a person.
People behind the writing
Production experience. Applied to AI.
Akshay Bansod
Software architect with fourteen years in production systems, now working on applied AI — where a model fits and what it is allowed to decide.
Experience across retail and sport tech, including low-latency services, cloud-native architecture, and architecture governance. Based in Pune, India.
Let’s discuss your next step.
Explore our Insights or contact us about adopting AI in your organisation.