Peregrine

Unifying fragmented public safety data into a single, searchable operational picture.

Contact for pricing United States

Peregrine is an American public safety data integration platform built to solve a major headache for law enforcement and emergency services: critical data exists, but it's trapped across a dozen legacy, disconnected systems that make it nearly impossible to search during active incidents.

Overview

Most public agencies run on layers of legacy IT—CAD systems, records management systems (RMS), camera feeds, ALPRs, and fire dispatch, all purchased from different vendors. These systems use incompatible data formats and rarely talk to each other. Peregrine's core value isn't about selling yet another standalone system. Instead, it ingests, cleans, and aligns these existing sources into a unified query interface, allowing dispatchers and investigators to instantly trace full cross-system contexts in one place. Layered on top is Peregrine AI, providing automated summaries and natural language Q&A.

Their service model is uniquely hands-on: they utilize "forward deployed engineering," embedding engineers directly inside client agencies to guide implementation rather than just handing over software and wishing them luck. In government agencies where data is notoriously messy, this approach is practically essential. Peregrine serves law enforcement, corrections, fire and EMS, emergency management, and 911 dispatch centers. The company recently closed a $250 million Series D funding round.

Ideal for public agencies and large organizations whose data is scattered across legacy systems and who want to clean up their infrastructure before attempting AI adoption.

Key Features

Multi-source data ingestion: Integrates data from existing dispatch, records, and video systems
Data cleaning and alignment: Standardizes historical data with inconsistent formats and field definitions
Unified query interface: Pulls complete cross-system contexts into a single search bar
Peregrine AI: Automated data summarization and natural language Q&A
Data governance and access control: Built to meet strict public sector auditing requirements
Multi-domain modules: Supports law enforcement, corrections, fire/EMS, emergency management, and 911 dispatch
Forward-deployed engineering: Dedicated onsite engineers to drive deployment and adoption

Pros

Does not force agencies to rip and replace existing legacy systems
Onsite engineering support dramatically lowers public sector deployment failure rates
Fixes data foundations before implementing AI, following the correct order of operations
Well-capitalized ($250M Series D), lowering vendor continuity and longevity risks

Cons

Pricing is not publicly disclosed; designed as high-ticket enterprise government procurement
Embedded deployment model means longer project lifecycles and slower time-to-value
Centralizing cross-system data inherently magnifies privacy and misuse risks
Value is heavily dependent on the quality of the agency's legacy data; garbage in, limited results

Use Cases

Dispatchers looking up historical incident contexts for a specific address in a single interface
Investigators cross-referencing people, vehicles, and case connections across multiple systems
Emergency management units consolidating real-time multi-source info during natural disasters
Corrections facilities combining inmate profiles and shift logs into one view
IT departments auditing and governing scattered institutional data

Editor's Note

To be frank, what Peregrine does isn't glamorous—it's just plumbing: ingesting, cleaning, and matching database fields. But having witnessed countless organizations rush into AI initiatives only to watch them stall because their underlying data dates couldn't even match formats, Peregrine proves an old truth: 90% of an AI project's success relies on data engineering.

FAQ

Is Peregrine meant to replace existing CAD or RMS systems?

No. It acts as an integration layer that pulls in data from your existing systems, aligns it, and offers a unified search interface. Agencies can keep using their current CAD or RMS, which is why public institutions are far more willing to adopt it without a massive 'rip-and-replace' headache.

What is 'forward-deployed engineering'?

It means engineers are physically or operationally embedded onsite with the client to help tackle messy data formats, operational workflows, and integration hurdles, rather than relying solely on remote customer support. Public sector data is notoriously chaotic, and while expensive, this model is practically the most effective way to ensure success.

Does centralizing this data create privacy risks?

Yes, and it is a challenge that must be addressed head-on. Centralizing previously siloed data and making it easily searchable increases both efficiency and the potential for misuse. Robust role-based access controls, audit trails, and strict purpose-limitations must be built into the foundation from day one, not bolted on afterward.