Independent products built with the same agent stack we deploy for clients. Each solves one high-value problem — validated, automated, or killed.
As a PMP and SAFe Agilist practitioner, I've spent years inside every major delivery methodology. They were designed for a world where humans write every line of code. That world is ending.
Neo is a work-in-progress framework that replaces the legacy stack — built ground-up for teams where AI agents are the primary builders and humans are the architects of clarity.
Humans define the what and why — clear product vision, strategic priorities, and measurable outcomes.
AI agents handle implementation — writing code, running tests, managing integrations, and iterating at machine speed.
People provide context, resolve ambiguity, review outputs, and govern quality. Humans steer; agents execute.
The build methodology for the AI Agent age. Replaces legacy frameworks (Scrum, SAFe, Waterfall) with a model where agents build and humans provide vision, strategy, and clarity.
AI platform for developer documentation at scale, providing company-level actionable insights from codebases and technical knowledge.
Real Estate ROI Automation. High-volume market analysis and automated response for asset managers.
Logistics Cost Optimization. Flight booking automation engineered for high-frequency corporate travel networks.
Forthcoming articles on data architecture, cloud migration, and enterprise AI — drawn from real engagements across Banking, Insurance, and Industrial sectors.
A global chemical manufacturer was running a fragmented legacy data stack across 4 cloud vendors. We redesigned their entire data architecture on Azure Databricks, eliminating redundancy and automating critical pipeline orchestration.
Traditional RPA is brittle and maintenance-heavy. Agentic AI systems built on LLMs with tool-calling can handle ambiguity, adapt to edge cases, and orchestrate multi-system workflows without constant human intervention.
Real-time transaction processing in regulated banking environments requires more than speed — it requires auditability, lineage tracking, and zero data loss guarantees. Here's the architecture we've proven at scale.
Every year a legacy data stack runs unchallenged, technical debt compounds. We quantify the true cost — not just in cloud spend, but in analyst hours lost, missed AI opportunities, and compounding data quality failures.