Capability
AIOps
Systems too complex for humans to watch need software that watches itself. We apply machine learning to operations — detecting anomalies, correlating events, and automating remediation — so your platforms stay healthy and your on-call stays quiet.
Proven where it counts
100+ microservices, event-driven architecture
Accelerated build, deployment, and scaling timelines for a large-scale program by maintaining 150+ AWS EC2 instances and a 90-microservice polyglot containerized platform.
- Java
- GovCloud
- Microservices
Real-time insights across unified systems
Built secure data pipelines connecting Salesforce cloud and on-prem TSA systems, giving decision-makers real-time, actionable insights.
- Salesforce
- Security
- Data Pipelines
API design & development
Accelerated delivery with cost savings for two public-facing API products. Transitioned from Akamai to AWS firewall for a direct cost reduction.
- API
- AWS
- Cost Optimization
What we deliver
Telemetry & Ops Data Engineering
Metrics, logs, and traces unified into one operational picture on OpenTelemetry — the data foundation everything downstream depends on.
Anomaly Detection
ML models that learn normal behavior and flag what matters — cutting alert noise so operators see real incidents, not static thresholds.
Event Correlation
Automatic grouping of related alerts across services and layers, so one root cause produces one actionable incident.
Automated Remediation
Runbook automation and self-healing responses for known failure modes — resolving incidents before a human is paged.
Incident Triage Acceleration
AI-assisted triage that summarizes incidents, surfaces probable cause, and drafts the first response — cutting minutes from every page.
Capacity & Performance
Forecasting demand, right-sizing infrastructure, and tuning cost and performance with data instead of guesswork.
Approach
Less noise, faster answers, fewer pages.
Most operations teams don't lack data — they drown in it. We start from the incidents that hurt, instrument what actually explains them, and add machine learning where it removes toil: anomaly detection that respects seasonality, correlation that turns fifty alerts into one incident, and automation for the fixes you've already written down.
- Baseline first: honest measurement of MTTR and alert noise
- Open standards — OpenTelemetry, not lock-in
- Automation gated by confidence, with human override
- Built to operate inside compliance boundaries
High-throughput data foundation. Built and supported a data warehouse capable of sub-second, high-throughput queries — the telemetry-scale foundation anomaly detection depends on.
Technologies we work in
- OpenTelemetry
- Prometheus
- Grafana
- Splunk
- Elastic
- Datadog
- PagerDuty
- CloudWatch
- Machine Learning
- Runbook Automation
Common questions
What is AIOps, in practical terms?
AIOps applies machine learning to IT operations data — metrics, logs, traces, and events — to detect anomalies, correlate related alerts, and automate responses. The practical outcome is fewer false pages, faster root-cause identification, and incidents that resolve themselves when the failure mode is known.
Does AIOps work in air-gapped or regulated federal environments?
Yes. We build AIOps on self-hostable, open-standards tooling (OpenTelemetry, Prometheus, Elastic) when SaaS platforms are not an option, and we design to the security and compliance controls your authorization requires, including NIST 800-53.
How is AIOps different from platform engineering / SRE?
Platform engineering builds and runs the paved road — the clusters, golden paths, SLOs, and on-call practices. AIOps is what reads the road's telemetry: machine learning applied to metrics, logs, and traces so anomalies surface early, fifty alerts collapse into one incident, and known failures fix themselves. The practices pair naturally — the platform produces the signal, AIOps makes sense of it.
How is this different from the monitoring we already have?
Traditional monitoring tells you when a threshold is crossed. AIOps learns what normal looks like for your systems, surfaces genuine anomalies, groups related symptoms into one incident, and can act on them automatically — reducing alert fatigue instead of adding another dashboard.