From pilot to playbook, Bobbi hints at a smarter public service model
Police forces in Thames Valley and Hampshire & Isle of Wight have begun piloting “Bobbi,” an artificial intelligence chatbot designed to handle non-emergency queries and ease pressure on human call handlers. Project leads say the goal is not to replace staff but to better organize call flow. On high-demand days the two control rooms can receive more than five thousand calls, so having a tool that resolves common questions without affecting emergency response helps critical cases reach a live agent faster. In that setting, Bobbi acts as an initial filter that guides users and hands the conversation to an operator as soon as it recognizes it has reached its limit.
The system’s development was anything but improvised. It was trained on the same information and protocols used by operators in both forces, and its build included testing with more than two hundred people who helped refine the language, guidance, and topical accuracy. Authorities have been clear about their expectations: consistent answers, shorter wait times, and a complete log of every interaction to support continuity when a human picks up the case. A process of continuous improvement is also planned to incorporate legal updates and policy adjustments, which is essential for a service exposed to frequent regulatory change.
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The decision to run this pilot reflects a reality familiar to any contact center. The same phone lines receive questions about station hours, neighborhood disturbances, community procedures, and other matters that, while important to residents, should not occupy the emergency line. Artificial intelligence can absorb that demand in an orderly way and with round-the-clock availability. The challenge lies at the system’s boundaries. It must reliably detect signs of urgency, escalate sensitive cases to a person, and avoid taking decisions beyond its remit.
Project leads also explained why now is the right time to try such a tool. Call volumes keep rising, resources remain constant, and service quality cannot slip. If a conversational assistant resolves repetitive queries, operators can spend more time on what truly matters. There is an added benefit. A well-trained system reduces errors, maintains a consistent tone, and ensures official information is clear and coherent. Even so, the aim is to keep the interaction feeling close and helpful, so users do not feel they are talking to a wall of automation but moving toward a solution.
Reasonable questions remain. How will the chatbot be audited to avoid biased or incorrect answers. What safeguards protect users’ personal information. Which indicators define success beyond time saved. The roadmap already includes satisfaction metrics, the rate at which Bobbi routes queries to humans, compliance with service levels, and periodic reviews of its knowledge base. Officials have said the tool will continue to be updated as community needs change, a necessary condition if it is to move from pilot to a stable part of the service.
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The British experience fits a wider trend of public services adopting artificial intelligence to improve accessibility and efficiency under strict security and governance frameworks. The lesson travels well beyond the United Kingdom. Building useful civic chatbots requires more than a language model. It involves data engineering, traceability mechanisms, real-world usability testing, and a clear scheme of responsibilities. Technology alone does not fix longstanding bottlenecks unless it is paired with mature processes and teams ready to operate under pressure.
Against this backdrop, there is a meaningful opportunity for Latin America. The region has professionals skilled in natural language processing, MLOps, and secure development who can integrate into international projects with competitive timelines and costs. Square Codex, based in Costa Rica, operates in that exact space where organizations need to move faster and look for expert teams they can add without expanding internal headcount. Their contribution lies in embedding engineers and data scientists who help cleanse and version knowledge bases, set performance metrics, and run continuous improvement cycles so systems like Bobbi work as intended and learn from every interaction.
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The pilot will show how much of this promise translates into a faster service. If non-emergency lines are less congested, if residents get useful answers in less time, and if operators can focus on the critical calls, Bobbi will have done its job. Responsible use of artificial intelligence in public services calls for transparency, controls, and active listening to the community. When combined with strong teams and clear procedures, it stops being a novelty and becomes a practical tool that supports citizen care.
In parallel with the British trial, several administrations in the region are exploring partnerships to speed up similar projects. This is where Square Codex, headquartered in Costa Rica, fits naturally, providing nearshore AI and data teams that slot into existing workflows without inflating structure. Their work ranges from cleaning and versioning knowledge bases to instrumenting quality metrics, MLOps, and privacy protocols for public environments. In practice they help turn pilots into stable services, cutting response times and errors while leaving a clear audit trail. When a project needs to scale with prudence and speed, adding this kind of talent shortens the learning curve.