1. What motivated your organization to participate in this year's Global AI Awards?

When conversational AI tools started proliferating across enterprises, the gap we kept seeing wasn't in model quality, it was in what happened when a developer hit a real error. We ran a lot of onboarding sessions as we built out the Precisely Developer Portal, and the pattern was consistent: someone would spend 20 minutes in documentation and still not understand what went wrong. That gap, between what docs tell you and what you actually need in the moment you're stuck, is where Gio™ lives. We entered these awards to put our answer to that problem in front of the broader AI community and to invite an honest comparison.

2. Could you give us an overview of the AI solution or breakthrough you submitted for consideration?

Our submission is a conversational AI developer portal built around Gio™, our AI assistant for API development. Gio lets any user, technical or not, query APIs, generate working code, and diagnose real errors in plain English. Gio is not a documentation chatbot. It was trained on actual API error patterns from live usage, which means it can tell you what went wrong in a real integration, not just how an API is supposed to work when everything goes right. The portal covers 40+ APIs across seven capability areas and connects to Claude, Cursor, and Copilot through open-source MCP servers. It is a bridge between natural language API access and live enterprise data, rather than another FAQ interface.

3. How did your team collaborate to develop and refine this AI innovation?

The most important thing we did was observe rather than assume. We watched developers work through real onboarding sessions and tracked where friction actually appeared, not where we expected it to. That changed what we built. Gio's conversational AI capabilities were sharpened through feedback loops between customer support, developer tooling, and model improvement that hadn't existed inside the organization before this project. Creating those pipelines was as much an organizational challenge as a technical one. But the result is an AI assistant for developers that's useful at the moment someone is stuck, not just at the moment they're exploring.

4. What impact do you expect your AI work to have on the broader AI community or society as a whole?

Conversational AI APIs have the potential to democratize access to high-quality enterprise data, but only if they can connect developers to accurate, real-world information rather than training set approximations. The clearest example is insurance underwriting. A carrier pricing a homeowner policy in a flood-prone area is making a decision that depends on accurate location data, including elevation, flood zone, proximity to water, and historical claim patterns. If the data is wrong, the pricing is wrong. And unlike a chatbot giving bad movie recommendations, a mispriced policy has real financial consequences. Gio lowers the barrier for underwriting teams to access Precisely spatially enriched data without needing deep integration expertise. That matters in ways that go beyond productivity. As conversational AI developer tools become the standard interface to enterprise data, the quality of what those tools connect to will determine the quality of AI-driven decisions across every industry where accuracy is consequential.

5. Were there any notable challenges during the development of this AI solution, and how did you overcome them?

The biggest challenge in building a conversational AI assistant that genuinely solves developer problems, rather than just answering questions about them, was error diagnosis. Teaching Gio to interpret real API failures required building something that didn't exist: a feedback loop between live API error patterns, customer support cases, and model fine-tuning. We had to instrument real usage, identify recurring failure modes, and continuously sharpen the AI assistant's ability to be useful at the exact moment a developer is blocked. The organizational challenge of connecting those three functions was, in some ways, harder than the technical work.

6. How does your organization nurture a culture that drives continuous AI innovation?

The clearest signal is how we treat the AI developer portal, as a first-class product surface, not a documentation afterthought. That mindset is what turned the Precisely Developer Portal into a full conversational AI experience rather than a reference site. Partners see it in how we run design partnerships before general availability: the product they give input on is the product that ships. And open-sourcing our MCP server implementations under an MIT license is a deliberate signal that we're building conversational AI developer tools for the ecosystem, not locking developers into proprietary infrastructure.

7. What advice would you offer to teams or companies aiming to make meaningful contributions in the AI space?

Treat developer experience as a product, not a feature. The conversational AI tool that gets adopted is rarely the most technically sophisticated one, it's the one where a developer can go from zero to first value in 24 hours or less. Build for the moment someone is stuck, not just the moment they're exploring. And invest in feedback loops between real usage and model improvement early. The gap between a conversational AI API that answers questions and one that actually solves problems is almost entirely determined by how grounded its training is in actual user behavior, not benchmark performance.

8. What are your organization's long-term goals in AI, and how are you planning to advance the field moving forward?

The next evolution for our conversational AI platform is moving Gio from diagnostics into active workflow generation. Today, Gio can tell you how to fix an integration or generate code for an API call. Where we want to get to: a developer describes what they want to build, "I need to enrich a batch of addresses, geocode them, and score property risk for each one," and Gio scaffolds the full integration, selects the right APIs, and generates working code end to end. That shifts conversational AI developer tools from a support layer to a genuine creative partner in the development process. The broader thesis is that trusted, location-anchored data will be the foundation that AI systems reach for when they need to act in the real world, and Gio is our most accessible entry point to that foundation for developers worldwide.

9. Are there any emerging AI technologies or trends your team is particularly excited about right now?

The Model Context Protocol has changed what conversational AI can do for developers. The ability for an AI assistant to call live, authoritative data sources, rather than draw on training data that may be months or years out of date, is the most consequential shift in applied AI we've seen. It's what makes Gio a true conversational AI API interface rather than a knowledge retrieval tool: the answers are accurate because they're grounded in live data, not approximated from a training set. The other trend we watch closely is the convergence of spatial reasoning and large language models. Location has always been one of the highest-signal variables in business data, and as conversational AI developer tools get better at incorporating spatially enriched inputs, the quality of AI-driven decisions across insurance, logistics, public health, and financial services will improve substantially.

To dive deeper into Precisely's award-winning work, visit their website at https://www.precisely.com/