Responsible AI Is an Engineering Decision

Post by Elad Nachmias, BridgeWise CTO

Trust is everything in financial services. When investors rely on AI-driven insights to make decisions about their capital, there is no room for error. Hallucinations, inaccuracies, or regulatory missteps don’t just damage credibility, they can have real financial consequences. That’s why at BridgeWise, responsible AI has become a core principle and an engineering challenge that we’ve been solving, in production, at scale. Our proprietary technology is built around a finance-specific Micro Language Model (MLM) that is purpose-built for wealth, trained on financial data, and designed to deliver accurate, compliant analysis across more than 70,000 global assets. But building a great model is only half the challenge. The harder question is: how do you know it’s still great after every training cycle?

Every time we retrain our model, we risk what’s known as catastrophic forgetting, where the model gains new capabilities but loses something it knew before. So we set out to automate it. Working with the AWS PACE team, we built an automated evaluation pipeline using an LLM-as-a-Judge approach, where a foundation model on Amazon Bedrock evaluates our model’s responses against ground truth data, scoring each answer and explaining its reasoning. We kept humans in the loop, not to review everything, but to periodically validate the automated scores and catch drift. What used to take 4 days of manual effort now takes 3 hours.

Our CTO  Elad Nachmias co-authored the full technical deep-dive on the AWS for Industries blog.
If you’re building AI for financial services and thinking seriously about model quality, evaluation infrastructure, or what responsible AI actually looks like in production, this read is worth your time.

See the full post here.