Your AI Belongs in Production, Not Stuck in a Notebook

Every organization has a graveyard of AI POCs that never made it past a demo. The model worked in a notebook. The stakeholders nodded. Then nothing happened because nobody planned for data pipelines, inference infrastructure, monitoring, or the engineering lift required to run a model at scale. AI Implementation at Proplr exists to close that gap. We take AI workloads from validated concept to production system, running on Google Cloud, governed and observable from day one.

Hybrid Resource model: Practitioners plus Proplr Agents.

This is not a handoff to a dev team that reads your requirements doc and disappears for three months. Proplr embeds senior resources directly into your team, engineers who have shipped agentic systems. Our Hybrid Resource model pairs human practitioners with purpose-built agentic systems running through Operational Imprints trained on real production workloads. The result: 10-20x the throughput of a traditional build engagement, with full transparency into what is being built and why.

The Proplr Agents handle the repetitive engineering scaffolding infrastructure-as-code, generating test suites, scanning configurations against security baselines, and producing deployment manifests. Every output is reviewed by a practitioner before it touches your environment. You get speed without sacrificing control.

Agent Drops: Purpose-built agentic systems, deployed.

Each agent is a multi-step agentic system designed for a specific workflow: document processing, anomaly detection, cost optimization, compliance monitoring, or whatever your Propulsion Score identifies as the highest impact use case. We scope each Agent Drop to deliver measurable value within four weeks of engagement start. No six-month roadmaps. No feature creep. One agent, one workflow, shipped.

Data engineering that actually supports production AI.

Models are only as reliable as the data feeding them. Before any Agent Drop, we audit your data estate and build the pipelines required to sustain production inference. That means datasets with proper partitioning and access controls, Cloud Storage buckets structured for training artifacts, Dataflow or Dataproc jobs that keep feature stores current, and lineage tracking so you know exactly what data produced what prediction. If your data is not ready, we make it ready not as a separate workstream that delays everything, but as a core part of the implementation sprint.

Production deployment on Google Cloud.

We build on the infrastructure your team will actually operate. That means Vertex AI for model training, tuning, and serving. Cloud Run for lightweight inference endpoints that scale to zero when idle. GKE for agent workloads that need persistent orchestration. Every deployment includes monitoring through Cloud Monitoring and Logging, alerting on model drift and latency degradation, and CI/CD pipelines in Cloud Build so your team can iterate after we hand off. We do not build demo environments. We build production systems with the observability and governance required to run them at scale on Day 2.