Ironstar Consulting works with startups, SMBs, and large enterprises. We help small businesses adapt and thrive in the age of AI. We help larger organizations stand up a center of excellence. And we rapidly revamp a single line of business when one has fallen behind.
The shape varies. The focus is always the same: systems that can run under real load, real budgets, and real scrutiny.
CAPABILITIES
Three pillars.
Agentic AI Systems
Production agents that operate over live data, integrate with real workflows, and fail in ways your team can understand and control. Multi-source retrieval, lineage, validation, CISO-ready reporting, and real human-in-the-loop boundaries.
Security, Reliability & Scale
Multi-petabyte platforms with 99.99% uptime in regulated environments. CDC pipelines, streaming analytics, and threat-grade observability. Built to run boring at 3am.
Cloud Platform Architecture
AWS, Azure, GCP. Snowflake and Databricks. Greenfield design or rescue work on platforms that have grown expensive and fragile. Senior delivery from someone who writes the code.
SELECTED IMPACT
Real systems, real numbers.
Sub-second auction analytics for an international auction house.
A global auction house was running their executive operating reports on overnight batch. Leadership saw auction performance more than a day after the hammer fell, and the numbers regularly disagreed with the floor. We rebuilt the path: WAL-based change data capture from the operational systems into a streaming pipeline, with Snowflake as the analytical store. Latency dropped from 24+ hours to single-digit seconds. The C-suite now watches results land near-live, and the BI numbers reconcile against the floor without a daily fire drill.
24h → <10s · BI reconciles to floor
Production AI over petabytes of dark data at a Fortune 50 financial.
A Fortune 50 financial institution had years of security telemetry sitting effectively dark: too large to analyze in any usable timeframe, too sensitive to move. We deployed production AI agents on AWS Bedrock that review, assess, and isolate threats in real time, operating directly over petabyte-scale stores of previously inaccessible data. The result: a step-change in enterprise risk visibility, faster containment, and a security posture that finally reflected the data the business was already collecting.
PB-scale · 99.99% uptime · live triage on AWS Bedrock
Agentic threat hunting on live data at a cybersecurity startup.
A cybersecurity startup needed to scale analyst output without scaling headcount. We built two layers of AI: an ingestion layer that auto-normalizes and projects heterogeneous source data, and an agentic layer that does live threat hunting on top of streaming detections. The agent queries multiple sources programmatically, assembles context with full data lineage and validation, and produces a CISO-ready operational report for every alert and incident. Analysts now arrive at incidents pre-briefed instead of starting from raw logs.
Clinical AI scribe with billing intelligence for healthcare.
After-hours documentation is the tax most clinicians pay nightly. We built a clinical AI scribe that produces structured visit summaries with medically irrelevant chatter (weather, weekend plans) filtered out, leaving only what belongs in the chart. The system suggests clinical billing codes from each encounter and integrates with existing EHR systems under full HIPAA compliance. Doctors recover hours every night; practices capture revenue they were leaving on the table.
Mid-market data platform redesign: $5M+ savings and new revenue.
A growing organization had accumulated four overlapping analytics stacks across teams: duplicated pipelines, runaway warehouse bills, and product teams shipping one-off dashboards no one trusted. We consolidated onto a single platform with clear domain ownership, decommissioned the redundant tooling, and turned the cleanest dataset into a productized feed that downstream teams (and eventually external partners) paid to consume. Net: several million in infrastructure and licensing savings, plus a new revenue line that hadn't existed before.
Most data platforms aren't slow because the workload is hard. They're slow because nobody's gone back to look. A recurring engagement pattern: redesign partition keys, eliminate accidental full-scans, replace schedule-driven jobs with incremental CDC, and move cold tiers to the storage class they should have been in from day one. Typical outcome over a 4–6 week engagement: 10–20× faster pipelines on 30–60% lower cloud spend, with no change to the surface API your downstream consumers depend on.
All work delivered in regulated, security-sensitive environments.
HOW ENGAGEMENTS START
Start with a real conversation.
Most work begins with a short architecture review or technical sanity check. This is usually enough to identify the main cost drivers, structural risks, and whether deeper work makes sense.
If it does, we move quickly. If it doesn't, you leave with clarity.
The first 30 minutes are free. A real technical conversation about the system you're trying to build or fix.
Founder & Principal Architect, Ironstar Consulting
Data architecture and engineering leader with 15+ years building and operating large-scale data platforms across AWS, Azure, and GCP. Background spans security analytics, large-scale ingestion, Snowflake and Databricks architectures, and production AI systems on live data.
Has led teams of up to 20 engineers and delivered repeated multi-million-dollar cost reductions while increasing system scale, reliability, and scope.
Ironstar Consulting reflects how I work: senior focus, hands-on execution, and an unrelenting emphasis on outcomes.
