Staff Augmentation

Absolutely. Most agencies and enterprises start with a 4–6 week POC to validate feasibility, benchmark accuracy, and secure internal buy-in before committing to a full deployment.

Yes. Most government clients start with a discovery phase under fixed price and then choose either a fixed-price implementation or a time-and-materials structure depending on the level of uncertainty and iteration required.

Mature teams typically work with streaming platforms like Kafka, Pulsar, or Kinesis, GPU-optimized inference pipelines, multi-camera grids, and large-scale deployments that process tens of thousands of events per minute in real time.

All personally identifiable information is removed or anonymized at the edge. Only event-level metadata flows into the system. Compliance with CJIS, state-level privacy laws, and cloud security standards is standard practice.

Different vendors offer different SLAs, but most production systems target accuracy ranges of 85 to 95 percent for incident detection, inference latency between one and five seconds, and system uptime between 99 and 99.9 percent depending on contract level.

A well-architected traffic AI platform scales horizontally by adding compute clusters, expanding ingest nodes, and onboarding additional camera grids. No rewrite is required as long as the system is built using an event-driven architecture.

Yes. Waze CCP allows incoming and outgoing incident data, and most 911 systems can be integrated through CAD interfaces or middleware layers. Some custom connectors are often needed because many government systems use legacy environments.

Annual maintenance usually represents 15 to 25 percent of the original development cost and covers model updates, infrastructure, monitoring, bug fixes, feature improvements, and on-call support if required.

Development is carried out in compliant cloud environments with strict access control, encryption, logging, monitoring, and audit readiness. Teams typically deliver all required documents including SSPs, POAMs, incident response plans, and security architecture diagrams.

You retain complete ownership of all custom code, trained models, and documentation unless your contract states otherwise. The vendor only keeps ownership of any internal tools they used to accelerate development.

Yes. Development teams can use synthetic datasets, transfer learning, public datasets, and early data collection from your existing camera feeds. Fine-tuning with your real data happens after the initial deployment.

An MVP usually costs between $120k and $350k, depending on scope, real-time requirements, number of data sources, and integrations. A full statewide or multi-city platform ranges from $500k to over $3M, depending on compliance, uptime requirements, and the number of camera endpoints.

Most MVPs take 8 to 14 weeks. If the system needs hardware integration with cameras or sensors, development usually extends an additional 4 to 6 weeks.

A strong delivery team typically includes a technical lead, one to two ML or computer vision engineers, one to two backend developers, a data engineer for streaming pipelines, a DevOps or SRE specialist for infrastructure, and a project manager. Some projects also include a traffic engineering subject-matter expert for validation.

You can work either way. Most organizations begin with the development team’s keys during prototyping and switch to their own keys for production to ensure compliance, governance, and billing control.