Lasse Hyldahl Jensen

I joined Avido as its Founding Engineer and third co-founder, after working with Avido’s CEO in Lunar’s AI Colony. We help banks and financial firms test, monitor, and audit the AI they put in front of customers. I’ve taken our product from a vibe-coded Next.js MVP on Vercel to something banks run in production, onboarded our two engineers, and I run regular knowledge-sharing sessions on building, orchestrating, and evaluating AI agents.

My main work is the evaluation platform, and it is largely a system of AI agents working together. Some agents simulate real end-user conversations against a customer’s AI; others grade the answers against evals like faithfulness and context recall. The quickstart I built adds more agents on top of clustering and vector search to turn a customer’s raw data dump into clean, usable test sets, and customers can stream their own production conversations in over OpenTelemetry and reuse them as test material. It runs on the Vercel AI SDK over OpenAI and Azure OpenAI, with Postgres and pgvector for the semantic search.

I own the product end to end, from the Next.js frontend and Node.js (Hono) backend down to the infrastructure on Azure Kubernetes: Pulumi for infrastructure as code, Flux for GitOps, GitHub Actions for CI, and the Grafana LGTM stack for observability. A smaller piece I also built is the AI Core, a configurable conversational-AI engine customers deploy to build their own assistants on, with tools wired in through MCP, moderation as a guardrail, and every request traced back for evaluation.

Avido is now used by companies like Skatteguiden, Sydbank, Kamina, and ABN AMRO. To get through one bank’s procurement we installed the software inside their own private cloud, shipping the same container images whether we host it or they do. Security is held to the standard banks expect, with passwordless Azure Workload Identity and zero-trust networking between services, and our SOC 2 penetration test came back with zero findings.

In early 2024, two colleagues and I started Lunar’s AI Colony, a small team set up to find out how far generative AI could go across the bank’s products and internal work. The brief was simple: get hands-on and show value quickly.

We spent the first month on internal tooling, an assistant for staff and then a support chatbot wired into Lunar’s knowledge base. Those worked well enough to convince us, and the wider organisation, to put AI in front of real customers.

We launched the customer-facing assistant in April 2024 and tuned it on real usage. Once people trusted it, we built a full support system on top.

The support system connects to Intercom through its REST API and answers customers in seconds, where a reply used to take up to three days. It resolves 85% of Lunar’s support cases today; for the rest, it gathers what a human will need, screenshots of a bug and so on, and hands the case over.

Between them, the assistant and the support system handle over 40,000 customer interactions a month. It all ran on Lunar’s stack: Go microservices behind Swagger and GraphQL APIs, with retrieval over PostgreSQL and pgvector.

I built backend microservices in Go, mostly services that exposed APIs to Lunar’s app and internal tools through Swagger and GraphQL.

As an intern I built an anti-corruption layer in front of one of Lunar’s partner integrations. API calls had been forwarded straight to the partner, which added latency and left little control over response times; the layer fixed that and let us move to event sourcing across the affected microservices. Those endpoints were hit every time one of Lunar’s million users opened the app, and we shipped changes to them continuously, sometimes several times a day.

Full-time, I took over Lunar’s card management system. It dated back to the 2019 banking licence and the rushed 2020 launch, so it carried real technical debt. I reworked the architecture that was holding back scalability and built an integration with Nets, Lunar’s card issuer, to keep the two systems in sync and reconcile any discrepancies automatically.

For my Bachelor’s project, I collaborated with IBM Denmark and Smukfest to develop the backend for Smukfest’s new mobile app. The backend was designed for scalability and maintainability, and it integrates various Content Management Systems (CMS) while enabling push notifications to all devices via Expo.

The app successfully launched for Smukfest 2023, supporting over 50,000 attendees, and was built to keep running even when parts of the backend failed.