Multi-agent AI orchestration, end-to-end data engineering, cloud-native infrastructure, and predictive ML deployed as live operational APIs. This is not a dashboard project — it's the execution infrastructure that runs your enterprise.
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Every component is production-grade from day one. We build systems that your engineering team can own, extend, and scale — not black boxes that require us to maintain.
Multiple AI agents working together across your operations. One handles supplier communication, another processes documents, a third monitors production metrics. They share context and escalate to each other — or to humans — when needed. Built on OpenClaw and Claude Coworks frameworks.
Databricks, Spark, or ClickHouse — the right engine for your data volume and query patterns. Pipelines that ingest from every operational system, transform for analytics and AI, and serve results in real time. Not a data lake that nobody queries — a live data infrastructure.
AWS or Azure with Kubernetes orchestration. Infrastructure-as-code so everything is reproducible, auditable, and scalable. Auto-scaling that responds to actual load, not worst-case estimates. Your cloud bill reflects what you use, not what you provisioned.
Machine learning models deployed as operational APIs that your systems call in real time — demand forecasting, anomaly detection, quality prediction, or whatever your operations need. Not models in a notebook. Live, versioned, monitored production endpoints.
Executive dashboards, operational dashboards, and departmental views — all drawing from the same data infrastructure. Power BI or Apache Superset, embedded or standalone, with row-level security and role-based access.
Complete system audit at 6 months: pipeline reliability, model accuracy, agent performance, infrastructure cost optimization, and architecture recommendations for the next phase. All support included for the full period.
Technology stack
We choose tools based on your scale, your team's capabilities, and what will still work in 3 years — not what's trending on Hacker News.
Claude, Langgraph, Claude Coworks, OpenClaw, custom agent frameworks
Databricks, Apache Spark, ClickHouse, dbt
AWS, Azure, Kubernetes, Terraform, Ansible, Powershell
Power BI, Apache Superset, embedded analytics
MLflow, scikit-learn, PyTorch, real-time serving
n8n, Power Automate, custom orchestration
Real deployment
ERPOnline for production tracking. ResellerOnline for self-serve ordering. A Supplier Fabric Portal for vendor management. BI dashboards across every department. All integrated, all running on live data. Now we're adding an AI layer — conversational operations, automated invoice reconciliation, and predictive demand forecasting — on top of infrastructure that was designed to support it from the start.
Engagement model
Every phase delivers a production system. You see results before the full engagement is complete.
Deep discovery across all systems and stakeholders. We design the target architecture, deploy the data infrastructure, and connect your first data pipelines. You have a working data layer before Phase 2 starts.
Dashboards go live across departments. First AI agents are deployed and validated with real data. Your operations team starts using the system while we continue building.
Multi-agent orchestration deployed. Predictive models go live as APIs. Full system hardening, documentation, team training, and handoff. 6-month performance review window begins.
Common questions
Not sure which tier?
Full BI stack + AI agents across 2–3 workflows. Ideal for $10M–$200M revenue operations that need unified data and intelligent automation without full infrastructure transformation.
Book a discovery call. We'll assess your current architecture, identify the highest-impact systems, and scope a phased engagement.
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