Real AI Solutions. Real Results.
Explore how we've built AI platforms, ML pipelines, and autonomous systems that deliver measurable enterprise value across industries.
AI-RUN SOS: Staffing Operating System
AI-native platform automating 90-95% of IT staffing operations
Designed and built an AI-native staffing platform with 12 autonomous agents that process 61M+ vendor requirement signals extracted from 812K emails. The system executes closed-loop workflows — from email triage to follow-up to trust scoring — replacing 10 recruiters with 1 operator + AI.
The Challenge
IT staffing firms lose millions to manual email triage, inconsistent follow-ups, tribal vendor knowledge, and recruiter turnover. Industry-average conversion is only 2-5%.
Our Solution
12 policy-bound AI agents with immutable audit trails, outcome-based vendor Trust Graph, Microsoft Graph API email intelligence, and a 47 DB model architecture with 160+ API endpoints. Full closed-loop execution.
Key Outcomes
- Replaces 10 recruiters with 1 operator + 12 AI agents
- Extracts 61M+ vendor requirement signals from email data
- Outcome-based Trust Graph scores vendor reliability
- Full immutable audit trail for compliance
- Target: 1 closure/day = $5M/year gross margin
Technologies Used
Impact Metrics
DRA: Data Reliability Agent
AI agent that triages failed data pipelines in under 2 minutes
Built an AI agent that automatically triages failed dbt pipelines and proposes safe, human-approved SQL fixes using lineage analysis and past incidents. Reduces mean-time-to-triage from 30-120 minutes to under 2 minutes.
The Challenge
Data pipeline failures cost enterprises ~$15M/year. Manual triage takes 2-8 hours per incident with high MTTR and risk of human error. On-call engineers face alert fatigue.
Our Solution
End-to-end loop: Ingest → Triage → Propose → Validate → Approve → PR. dbt-native agent with lineage-first blast radius analysis, dual-mode (heuristic + LLM) reasoning, and approval-gated execution. Auto-creates GitHub PRs.
Key Outcomes
- Reduced triage time from 30-120 minutes to under 2 minutes
- 90% reduction in MTTR (2-8 hours to <15 minutes)
- 8x-80x ROI ($50K-$200K annual savings per team)
- Approval-gated execution ensures zero auto-apply risk
- Auto-generated GitHub PRs with full context and lineage
Technologies Used
Impact Metrics
SanGPT: Enterprise SAN Intelligence Platform
The single pane of glass for enterprise SAN — powered by AI
Built an AI-powered Storage Area Network intelligence platform that replaces fragmented, vendor-specific management tools with a single, unified system. Storage administrators can ask questions in plain English and get tool-backed, data-driven answers about their entire SAN environment — spanning multiple vendors, fabrics, and data centers.
The Challenge
Fortune 500 companies manage 20+ storage arrays across 3-4 vendors with separate tools. Storage teams spend 60-70% of their time on manual data gathering. Outages cost $100K-$500K per hour.
Our Solution
Conversational AI interface backed by 50+ tools with zero-hallucination guarantees, ML-driven anomaly detection, capacity forecasting, and autonomous remediation. Multi-vendor connectors unify Pure Storage, Dell, HPE, NetApp, Brocade, and Cisco under one canonical data model.
Key Outcomes
- 4x ROI achieved in the first year of deployment
- Reduced incident MTTR from 4-8 hours to under 1 hour
- Prevented capacity exhaustion with 95% confidence forecasting
- Unified 7 vendor platforms into a single AI-powered interface
- Automated chargeback reporting saving 15+ hours per week
Technologies Used
Impact Metrics
DevopsSREGPT: Operational Intelligence Platform
Natural language answers for reliability, delivery, cost, and risk
Built an operational intelligence platform for engineering and finance teams that answers reliability, DORA metrics, cost, and risk questions in natural language using real telemetry data from Prometheus, Grafana, and OpenTelemetry.
The Challenge
Fragmented tools (metrics, cost, incidents in separate systems). Slow root cause analysis. Reactive firefighting. Finance and engineering misaligned on cost and reliability.
Our Solution
Tool-using AI copilot that runs real PromQL/LogQL/TraceQL queries — never hallucinating data. Unified reliability + DORA + cost + risk view. Vendor-neutral OpenTelemetry-based architecture with risk-based early warning.
Key Outcomes
- Unified reliability, delivery, cost, and risk in one platform
- Real PromQL/LogQL/TraceQL queries — never hallucinated data
- Vendor-neutral architecture based on OpenTelemetry
- Change-to-incident causality for proactive risk management
- RBAC, audit logs, and explainability for enterprise compliance
Technologies Used
Impact Metrics
PutsEngine: Institutional-Grade Options Intelligence
Automated detection of asymmetric PUT opportunities using ML
Designed an institutional-grade automated PUT options trading engine that detects stocks likely to decline 5-20% within 1-10 days using dark pool analysis, Gamma Exposure (GEX), distribution detection, and multi-model ML convergence.
The Challenge
Identifying asymmetric put option opportunities requires analyzing institutional footprints across multiple data sources in real-time. Manual analysis misses time-critical signals.
Our Solution
9-step detection pipeline with 4-system convergence engine (EWS, Gamma Drain, Weather, Direction). 144-ticker universe across 15 sectors. 10 institutional footprints tracked. Early Warning System detects positions 1-3 days before breakdown.
Key Outcomes
- 990 opportunities detected in a single month
- Early Warning System detects positions 1-3 days before breakdown
- 4-system convergence eliminates false positives
- Vega Gate for IV regime management and risk control
- Capital Ramp protocol for position sizing optimization
Technologies Used
Impact Metrics
TradeNova: Multi-Agent Options Trading Platform
5-agent system with reinforcement learning on AWS EKS
Built an institutional-grade automated options trading platform on AWS EKS combining 10+ detection layers, a 5-agent multi-agent system, and reinforcement learning. Master Picks unified scoring aggregates signals across all agents for optimal trade selection.
The Challenge
Detecting institutional mechanical pressure 1-2 days before major price moves while managing risk across multiple positions requires computational intelligence beyond human capability.
Our Solution
5-agent orchestrator (Trend, MeanReversion, Volatility, EMA, Options) with Master Picks scoring (0-350+ points). 7-layer Market Weather System. Moonshot engine scanning for 5x-30x opportunities. Self-improving ML with nightly retraining.
Key Outcomes
- 5-agent multi-agent system with unified scoring
- 10+ detection engines for signal diversity
- Self-improving ML with nightly Bayesian weight updating
- 5-tier profit cascade (TP1 +40% through TP5 +200%)
- Total AWS infrastructure cost under $280/month
Technologies Used
Impact Metrics
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