Agents as Infrastructure
Hive
A self-service control plane for teams to package, deploy, govern and reuse containerized AI agents as enterprise infrastructure.
Open case studyI translate customer workflows into architecture, build the backend and intelligence layer, integrate enterprise systems, instrument the runtime, and turn one-off delivery into reusable platform capability.
Banking agentic helpdesk — reported mean ticket-resolution improvement.
Cloud migration and architecture optimization across prior enterprise delivery.
Backend, retrieval and caching improvements in production services.
Workflow automation replacing repetitive operational effort.
Problem → constraints → architecture → hands-on implementation → production hardening → measurable or verifiable evidence.
Agents as Infrastructure
A self-service control plane for teams to package, deploy, govern and reuse containerized AI agents as enterprise infrastructure.
Open case studyAI Helpdesk
One role-aware assistant across job insights, technical knowledge and supervisor workflows for field technicians in the flow of work.
Open case studyAgentic Helpdesk
Secure intent-aware support for cards and accounts using hybrid retrieval, graph context and guarded tool execution.
Open case studyAgentic Content OS
An agentic content operating system that turns research into strategy, governed creation, publishing and learning loops.
Open case studyInception to Offer
A closed-loop career system from role discovery and diagnostics to practice, mentoring, CV iteration, interviews and offer tracking.
Open case studyReliability Intelligence
A reliability layer that compresses raw agent traces into recurring failure shapes, release gates and actionable root-cause evidence.
Open case studyNo undifferentiated logo cloud. Every capability points back to systems, production concerns and concrete ownership.
Field Operations · Hive · ServiceXPro
Discovery, workflow mapping, integrations, deployment and reusable primitives
Hive · Field Operations · Banking · PillarQuill
Routing, tools, memory, structured artifacts and human escalation
Field Operations · Banking · Grant Copilot · Company Brain
Hybrid RAG, permissions, citations, graphs and ingestion pipelines
Hive · Placement OS · ServiceXPro · SaaS Baseline
APIs, modular boundaries, queues, caching, identity and data systems
Hive · Placement OS · PillarQuill · ISO Platform
RBAC, tenant isolation, credentials, auditability and policy enforcement
Trace Intelligence · Hive · Field Operations
Traces, semantic SLOs, eval gates, fallbacks, RCA and incident readiness
Hive · Field Operations · Banking
AWS, containers, Kubernetes, IaC, CI/CD and environment governance
PillarQuill · Placement OS · ServiceXPro
Problem framing, domain models, closed loops and end-to-end execution
A broader body of work across SaaS, compliance, evaluation, business automation and domain-heavy backend platforms.
AI CRM and operations platform for service businesses
Live productOrganizational memory → reasoning → role-authorized action
Architecture / prototypeMulti-standard workflows across ISO 27001, 20000, 9001, 13485 and 22000
Product architectureTask givers, evaluators, bidding, KYC and quality-control workflows
Product architectureSalon, appointments, billing, inventory, real estate and PostGIS workflows
Backend systemsDocument intelligence, hybrid RAG, citation validation and governed assistance
Enterprise AI prototypeAI-assisted content creation, publishing, analytics and compliance workflows
AI productAuth, RBAC, subscriptions, queues, caching, audit, quotas and analytics
Reference architectureEnterprise agent platform, AI orchestration, FastAPI, AWS/Pulumi, Kubernetes, IAM and observability.
Senior Engineer / AI PlatformPrincipal-level backend and GenAI delivery across enterprise systems, secure RAG and integration architecture.
Principal EngineerPython platforms, APIs, automation, CI/CD, cloud delivery and engineering leadership.
Senior Software Engineer / Team LeadEnterprise backend services, databases, automation and production delivery discipline.
Software EngineerI prefer the edge where business logic, model behavior and production reality collide.
I reduce ambiguity first. Then I define boundaries, failure modes, evidence and operational ownership before scaling the implementation. I remain hands-on through APIs, orchestration, data models, infrastructure and production diagnostics.