1- Tell us about yourself / your co-founder(s)

Avido was founded by operators with experience from banking and other regulated enterprises. The founding team has carried direct responsibility for quality, risk, and business outcomes in production GenAI systems.

Between us, we have implemented multiple GenAI applications in live production, in some cases covering more than a third of a company’s total operational workload. We understand where GenAI breaks in the real world because we have been responsible when it did.

We also understand how fundamentally cross-functional GenAI is in regulated industries. Success requires alignment across technology, legal, risk, compliance, content, and business teams. Designing for that reality is core to how we build Avido.

2- Who are your target customers, and what problem do you address for them?

Our target customers are financial services companies such as banks, insurers, and fintechs with ambitious GenAI plans.

Their core problem is not access to models or the ability to build applications. It is the inability to define, measure, and govern quality at scale. As GenAI usage grows, most organizations struggle to understand whether their systems are behaving as intended, whether they remain compliant over time, and how to improve them systematically.

3- What is your product / solution, who do you compete with, and what is your USP?

Avido is the quality and control platform for GenAI in financial services.

We help customers:

• Define what “good” looks like at task, topic, and policy level
• Measure system behavior, not model benchmarks, before and after launch
• Monitor drift, regressions, and real-world behavior in production
• Generate governance-ready documentation for internal stakeholders and regulators
• Provide actionable insights that enable fast iteration, stakeholder feedback, and continuous improvement

We also include a document optimization layer that improves knowledge base coverage and reduces contradictions, which directly improves downstream GenAI behavior.

We compete with internal tooling, manual QA processes, and fragmented evaluation solutions. Our USP is that we are built for regulated, production environments. We unify quality definition, evaluation, monitoring, and governance into a single operating layer that can be reused across initiatives.

4- What is your current stage and traction, and how can our network help you in the next 6–12 months?

We are active with several enterprises, including multiple banks and a number of fintechs.

Our customers come to us either as they move from pilots to production or after an initial launch, when they realize that their current approach does not scale. They may have something live, but it is not replicable across initiatives, relies on discovering failure modes by chance, and cannot support the pace or volume of GenAI deployments they are planning.

In the next 6–12 months, the highest leverage from your network is access to senior decision-makers in financial services who are responsible for scaling GenAI safely. This includes heads of AI, digital transformation, risk, compliance, and customer operations.

5- How do you go to market? How are banks or insurers working with you (or can work with you)?

We go to market top-down, typically entering through a live or imminent GenAI system where quality, confidence, or regulatory concerns are blocking scale.

Banks and insurers work with us to:

• Gain a clear, defensible view of current system performance
• Identify what is required to bring the application up to the desired quality and risk standard
• Continuously monitor behavior as usage grows and systems evolve
• Produce documentation that satisfies internal legal requirements and external regulators

We are often brought in by mature teams who have already built parts of this themselves. They realize that it is a large, ongoing effort and difficult to replicate consistently across initiatives. Avido becomes the unified quality control layer used across all GenAI deployments, from inception through production and beyond.

6- Any relevant industry trends or market shifts we should be watching?

Several shifts are becoming clear:

First, GenAI systems are becoming more complex. Instead of single model calls, we increasingly see composite systems combining multiple models, deterministic code, retrieval layers, and external data sources. This raises the bar for quality control.

Second, GenAI fundamentally differs from previous technologies. You can no longer rely on binary acceptance criteria. Outputs are unstructured and judgment-based, which makes cross-functional collaboration essential.

Third, regulators are focusing less on which model is used and more on system behavior, controls, and evidence. This is driving demand for repeatable, auditable quality processes.

7- What’s on your bookshelf or podcast app? Your favourite place for a coffee or a drink?

On the podcast side, Acquired is a clear favorite, particularly the original TSMC episode. This week, I chanced upon a Jonathan Bi podcast episode on Alpha School and the reported results of AI-supported learning. It has been stuck in the back of my mind since.

On the bookshelf, Stephen Fry’s retelling of The Odyssey stands out as a recent read.

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What emerges clearly from this conversation is that the future of GenAI in financial services will not be defined by models alone, but by the systems, controls, and processes built around them. As regulators shift their focus toward behavior, evidence, and accountability, the ability to define and measure “good” at scale becomes a strategic advantage.

Avido’s perspective reflects a broader shift in the industry: from experimentation to operational maturity. For banks, insurers, and fintechs looking to scale GenAI with confidence, the question is no longer if quality governance is needed — but how quickly it can be embedded into everyday operations.