We build AI systems for enterprise IAM teams that automate access reviews, surface anomalies, and keep identity lifecycle clean across Okta, Ping, Entra, SailPoint, and CyberArk.
Book a discovery callColorado is home to a serious enterprise identity ecosystem. Ping Identity is Denver-headquartered. Optiv is Denver-headquartered. The Front Range has one of the densest concentrations of IAM, PAM, IGA, and security engineering talent in the United States. Running IAM at enterprise scale means the same pattern everywhere, too many entitlements, too little visibility into who actually uses what, access reviews that rubber-stamp everything because the reviewer has three hundred rows to approve by Friday, and a compliance audit that treats the whole thing like a checkbox. AI changes what is feasible here.
The five pains we hear most often before we build.
A manager gets a list of two hundred entitlements to review quarterly. They approve everything in an hour because there is no way to actually evaluate what each one grants or whether it is still needed. Risk stays where it was, audit passes, nothing improves.
Every promotion, transfer, and project adds entitlements. Nothing ever gets removed. Over five years an employee accumulates access they have not used in years, and no one has the tooling to notice.
Your SIEM surfaces logins that look strange on paper but are false positives. Real anomalies, privilege escalation combined with lateral movement combined with unusual hours, drown in the noise.
Provisioning and deprovisioning across a dozen downstream systems takes hours per employee per lifecycle event. HR changes land in IT as tickets that take days to close. The offboarded employee still has access to Slack on Friday.
SOC 2, ISO 27001, HIPAA, SOX, whoever your auditor is, the evidence package is assembled from screenshots, exports, and email threads in the weeks before the audit. It should be a click away every day.
The specific services that map to each pain.
An agent that triages access requests, flags outliers before they reach the approver, drafts justifications, and routes the non-obvious ones to the right reviewer with the context a human needs. Integrated with ServiceNow, Okta Workflows, Ping Admin Console, Jira, and whatever ticketing you run.
Learn more →Role mining and entitlement usage modeling that surfaces the entitlements nobody uses, the roles that have drifted out of spec, and the accounts whose behavior no longer matches their role. Anomaly detection on access patterns that catches the combinations your SIEM misses.
Learn more →Private LLMs trained on your policy documents, IAM runbooks, and historical ticket data, deployed on your infrastructure. Your security team and your access reviewers get a grounded copilot that answers from your actual rules, not a generic chatbot.
Learn more →Real-time sync between HRIS (Workday, UKG, BambooHR), your IAM platform (Okta, Ping, Entra, SailPoint), your PAM (CyberArk, BeyondTrust), and your ITSM. Joiner-mover-leaver events propagate in seconds, not days. Compliance evidence streams automatically into your GRC platform.
Learn more →Okta, Ping Identity, Microsoft Entra ID (formerly Azure AD), SailPoint IdentityIQ and IdentityNow, Saviynt, Omada, and all the major PAM vendors (CyberArk, BeyondTrust, Delinea). If you are on a less common platform, we write the integration. If there is an API, we can connect to it.
Yes, significantly. The problem with SIEM-only anomaly rules is that they look at individual signals in isolation. An ML model trained on your own access patterns combines signals (who, when, from where, what resource, what typical role does) so rare combinations rise to the top and common benign patterns stay quiet. The realistic expectation is an order of magnitude fewer alerts sent to a human, with the signal-to-noise ratio inverted.
Three layers. The agent and model run inside your perimeter. Every action the agent can take is scoped to a specific service account with a specific set of entitlements, and that account is itself monitored and reviewed. Every prompt and response is logged, and anomalies on the AI system are themselves fed back into the anomaly detection loop.
Either. Most enterprises deploy in their own cloud (AWS, GCP, Azure) because their IAM stack already lives there. On-prem is fully supported for regulated or air-gapped environments. Deployment is a container or Kubernetes workload inside your boundary, with no outbound calls required at runtime.
Typical pattern is a two to three week discovery sprint scoped to a specific pain (access reviews, JML automation, or anomaly detection). Then a six to twelve week build, milestone billed, demoed every two weeks. Most IAM AI engagements land in the fifty to two hundred and fifty thousand dollar range. The new client discount applies to the first engagement.
Primarily private enterprise today. For public sector engagements we partner with a cleared integrator on the execution side while we design the system.
Ready to turn access reviews into a one-click evidence pack and catch the anomalies your SIEM misses? Book a thirty minute discovery call, free for new clients, and we will scope it around your stack.
Book a discovery callServices that pair well with what you just read.
Purpose-built autonomous agents that execute workflows 24/7.
Learn more →Prediction, classification, anomaly detection and vision.
Learn more →Custom language models on your data, your infrastructure.
Learn more →Connect CRMs, ERPs and cloud tools with real-time sync.
Learn more →