Laredo Police expand AI to speed response and support officers
The Laredo Police Department is rolling out a wave of AI-enabled tools through early 2026. Chief Miguel Rodriguez outlined the plan during the State of the City address, framing the effort around one goal: keep Laredo among the safest cities in Texas - and 13th safest nationally - by getting officers the right information faster.
The approach is pragmatic. Use automation to ease staffing pressure, improve response times, and reduce administrative drag - while keeping humans in control of final decisions.
What's already in play
- Since 2025, LPD has worked with Raven, an SBA-certified HUBZone small business serving U.S. defense and intelligence. The department is using specialized cameras to capture the longitude and latitude of gunfire for precise origin and has deployed sensors at major intersections to detect crashes in real time - all to cut response time.
- Skydio drones provide aerial awareness during incidents and searches.
- Flock technology supports crash spotting and gunfire detection, expanding the department's real-time picture of the city.
- Many deployments are covered by government grants, helping offset staffing and recruitment challenges.
What's launching next
- CaseX (beta) - March: An online, AI-assisted reporting system that guides residents through filing police reports. If an incident is urgent, the system routes people directly to 911 or connects them with officers.
- Peregrine - mid-March: A data layer across LPD's digital infrastructure, giving officers fast access to case files, documents, and records across divisions.
- Prepare911 - April: An automated guide for the department's non-emergency line that decides what can be handled online and what should escalate. If 911 is dialed, officers see live coordinates and can listen in - allowing nearby units to self-deploy without delay.
Human judgment stays in the loop
Rodriguez has been clear: these systems are tools, not replacements. Every action that involves AI gets human review, and the department is being careful about how each platform is rolled out.
The priority is accuracy and accountability. If a system gets something wrong, officers step in and correct it. Efficiency improves, but responsibility doesn't shift.
Why this matters for government, IT, and development teams
- Operational lift without headcount: AI-assisted reporting, call triage, and sensor-driven alerts free up sworn staff for field work.
- Faster data access: A unifying layer like Peregrine reduces time spent searching across systems and silos.
- Event-driven response: Real-time streams (gunfire, crashes, 911 audio and coordinates) move units from "dispatched" to "proactive" - especially useful in high-call windows.
- Human-in-the-loop by default: Keep officers as final decision-makers; log AI suggestions separately for audit and after-action review.
- Policy first: Set rules for data retention, model updates, and accuracy thresholds; run bias and drift checks on a schedule.
- Grants + procurement: Many agencies fund these builds with grants; align proposals to measurable outcomes (response times, case cycle time, clearance rate).
- Metrics that matter: Track time-to-first-response, time-to-dispatch, officer hours saved, false-positive rates, and citizen satisfaction with reporting tools.
Context and further reading
For agencies building guardrails around AI, see the NIST AI Risk Management Framework. For 911 modernization fundamentals, the National 911 Program's NG911 resources offer a clear overview.
If your team is developing AI skills for public-sector work - from policy to build - explore practical course tracks at Complete AI Training.
The bottom line
LPD's plan is straightforward: automate the repetitive, surface the urgent, and keep officers in control. The tech is there to speed decisions and shorten the gap between incident and response - without losing the human touch that public safety relies on.
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