When many enterprises weren’t even thinking about agentic behaviors or infrastructures, Booking.com had already “stumbled” into them with its homegrown conversational recommendation system.
This early experimentation has allowed the company to take a step back and avoid getting swept up in the frantic AI agent hype. Instead, it is taking a disciplined, layered, modular approach to model development: small, travel-specific models for cheap, fast inference; larger large language models (LLMs) for reasoning and understanding; and domain-tuned evaluations built in-house when precision is critical.
With this hybrid strategy — combined with selective collaboration with OpenAI — Booking.com has seen accuracy double across key retrieval, ranking and customer-interaction tasks.
As Pranav Pathak, Booking.com’s AI product development lead, posed to VentureBeat in a new podcast: “Do you build it very, very specialized and bespoke and then have an army of a hundred agents? Or do you keep it general enough and have five agents that are good at generalized tasks, but then you have to orchestrate a lot around them? That's a balance that I think we're still trying to figure out, as is the rest of the industry.”
Check out the new Beyond the Pilot podcast here, and continue reading for highlights. Moving from guessing to deep personalization without being ‘creepy’Recommendation systems are core to Booking.com’s customer-facing platforms; however, traditional recommendation tools have been less about recommendation and more about guessing, Pathak conceded. So, from the start, he and his team vowed to avoid generic tools: As he put it, the price and recommendation should be based on customer context.
Booking.com’s initial pre-gen AI tooling for intent and topic detection was a small language model, what Pathak described as “the scale and size of BERT.” The model ingested the customer’s inputs around their problem to determine whether it could be solved through self-service or bumped to a human agent.
“We started with an architecture of ‘you have to call a tool if this is the intent you detect and this is how you've parsed the structure,” Pathak explained. “That was very, very similar to the first few agentic architectures that came out in terms of reason and defining a tool call.”
His team has since built out that architecture to include an LLM orchestrator that classifies queries, triggers retrieval-augmented generation (RAG) and calls APIs or smaller, specialized language models. “We've been able to scale that system quite well because it was so close in architecture that, with a few tweaks, we now have a full agentic stack,” said Pathak.
As a result, Booking.com is seeing a 2X increase in topic detection, which in turn is freeing up human agents’ bandwidth by 1.5 to 1.7X. More topics, even complicated ones previously identified as ‘other’ and requiring escalation, are being automated.
Ultimately, this supports more self-service, freeing human agents to focus on customers with uniquely-specific problems that the platform doesn’t have a dedicated tool flow for — say, a family that is unable to access its hotel room at 2 a.m. when the front desk is closed.
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