From Research to Reality β Deployed at Scale
From government agencies to global enterprises, our network spans industries and borders. We collaborate with organizations that share our commitment to building smarter, more efficient systems powered by Agentic AI. Every partnership is a reflection of the trust we have earned and the impact we continue to deliver.
Weβre not building another AI tool. Weβre creating the infrastructure for a world where intelligent agents handle complexity so people can focus on what matters most. Every research paper, every engineering decision, every partnership answers one question: does this make the world work better?
From government agencies to Fortune 500 enterprises real outcomes, real deployments, real impact.
ELP has been a critical use case for the DMV, reducing our processing backlog and bringing ELP order processing up to date.
GovernmentJothi's commitment to community education is exceptional. His bootcamps and Agentic AI programs are equipping city residents.
Public SectorThe LA fire fraud prevention demo was the most impactful and provided deeper insights into fraud symptoms.
GovernmentThe Personal Narrative CUCP Re-Evaluation and Explainable Decisioning demonstrated significant value.
State AgencyProcessing UCC document images to extract insights and enable conversational intelligence has been highly impactful.
EnterpriseFinancial reconciliation for closing the books, budgeting and forecasting demonstrates the value of your platform.
State FinanceThe Building Agent simplifies regulatory and compliance certification and delivers significant value.
EnterpriseHear directly from the executives, public officials, and innovators who have put our platforms to work. Their experiences highlight measurable progress, operational efficiency, and meaningful transformation across diverse sectors.
Our AI agents decide, execute, and deliver measurable outcomes. From semiconductor fabs to federal systems, they run inside the most demanding environments on earth β not as prototypes, but as production systems already in daily use.
Rather than narrow domain tools, we engineer multi-agent platforms that learn, adapt, and execute autonomously across wildly different sectors. Factory floors, policy systems, logistics networks β a single architecture scales to all of them without rewrites or reinvention.
The Agentic AI Research Lab unites researchers, data scientists, operators, and policymakers under a shared mission β making autonomous AI practical and trustworthy. Less a network, more a coordinated push to turn breakthrough science into working systems people can actually rely on.
We co-develop with industry leaders, train on live operational data, and deploy where stakes run highest. Speed matters. Accuracy matters. Our systems are built to deliver both under pressure β the kind of pressure that breaks lesser software.
Every number here is verified by our partners and proven in production today. Rather than ask for trust, we publish the results and let them stand on their own. Performance isnβt a pitch β itβs what the data says.
No forced choices between cloud providers, model families, or orchestration frameworks. AWS or Google Cloud. GPT or Llama. LangChain or vLLM. Plug in what your team already uses β we handle the rest.
Problem Statement Compliance today is fragmented across multiple regulatory agencies, each issuing notices independently. Tenants, building owners, and facility management teams rely on scattered spreadsheets and email threads, resulting in missed renewals, delayed responses, and heightened penalty and public safety risks. No single platform consolidates regulatory data or automates compliance workflows end to end.
Solution An Agentic AI platform that unifies regulatory data from every agency and delivers role based, personalized guidance to tenants, building owners, and facility managers. It automates renewal reminders, surfaces critical safety alerts, and resolves complex queries in seconds through a conversational interface. Built on AWS Bedrock Agents with role aware knowledge bases, it scales securely across buildings, portfolios, and jurisdictions.
Problem Statement CA DMV reviews 600 to 1,000 personalized license plate configurations every day, each manually screened for offensive, threatening, or inappropriate content. Human review takes 5 to 10 minutes per application, varies across reviewers, and frequently misses sophisticated obfuscation like leetspeak, phonetic substitutions, and reversed text. Rising application volumes overwhelm review capacity, and static keyword filters cannot keep up with evolving language patterns.
Solution An Agentic AI platform that validates license plate configurations in seconds, detecting profanity, toxicity, obscenity, threats, and hate through layered pattern recognition including leetspeak and phonetic variations. Every decision is accompanied by Explainable AI reasoning and probability scores across categories, giving reviewers full transparency and auditability. Built on a serverless, auto-scaling architecture with multi-model LLM support, the solution has been recognized with the AAMVA award for innovation and is already reducing DMV processing backlogs at scale.
Problem Statement DMV staff rely on large volumes of policies, manuals, memos, emails, and institutional knowledge scattered across SharePoint, OneDrive, and internal channels. Each policy query takes over fifteen minutes and often produces inconsistent interpretations, slowing operations and deepening reliance on senior staff. Without a centralized intelligence layer, accurate retrieval, compliance, and audit readiness remain difficult to sustain.
Solution Policy Pro is a unified policy intelligence platform powered by RAG and Azure OpenAI, built on a centralized vector index that combines every policy, memo, manual, and email. It delivers fast, semantic answers to complex multi-step queries through a conversational interface, with real-time PII detection, full audit logging, and cited sources on every response. Staff resolve complex policy questions in seconds, reducing training overhead and accelerating citizen service while maintaining enterprise-grade security and traceability.
Problem Statement Youth re-entry programs struggle with fragmented health, legal, and social support data spread across Excel, Cloud SQL, and BigQuery. Inconsistent data structures make extraction, validation, and reporting difficult, leading to delayed care plans, missed services, and poor visibility into each youth's complete needs. The result is slower reintegration, higher recidivism risk, and staff burdened with manual reconciliation instead of direct support.
Solution An Agentic AI platform that unifies youth health, legal, and social support data into a single 30-field profile spanning medical IDs, court dates, mental health assessments, housing, and family support. It automates validation, flags missing records, and generates customizable re-entry care plans in seconds, with secure downloads and a conversational React interface for caseworkers. Built for HIPAA-grade privacy, it replaces days of manual reconciliation with coordinated care, giving every youth a faster, more dignified path forward.
Problem Statement EDD processes high volumes of unemployment applications every day, each requiring verification against multiple trusted sources and constant vigilance for fraudulent or proxy submissions. Manual review cannot keep pace with the scale, and simple rule-based filters fail to catch sophisticated patterns such as disproportionately high wages for minimal work hours, synthetic identities, and coordinated fraud rings. Delays and false approvals lead to significant financial loss, strained public trust, and staff burnout on repetitive, low-value verification work.
Solution An Agentic AI platform that reviews every application in real time, cross-checking applicant data against trusted databases and surfacing fraud signals through machine learning, anomaly detection, and proxy identification. Suspicious claims are flagged with risk scores and transparent reasoning, while legitimate claims are fast-tracked to maintain service speed. A continuous learning loop adapts to new fraud tactics as they emerge, enabling EDD officers to focus on high-risk cases and deliver a faster, safer, more trustworthy experience for California's workforce.
