Enterprise operations experience, applied to AI automation.
I spent 11 years analyzing how large organizations actually work: the rules, handoffs, exceptions, and manual steps between a problem and a finished outcome. Now I use that background to build practical systems for service businesses.
Shariq Riaz
AI automation and systems engineer
Process
Analysis
Systems
Engineering
Client
Delivery
Enterprise background
The experience behind the build decisions
These figures describe my enterprise career before OrchestriAI, not OrchestriAI client volume.
- Enterprise IT experience
- 11+Healthcare and defense programs
- Enterprise projects
- 50+Business analysis and delivery work
- Program reach
- 130+Countries in enterprise programs
The value of that background is practical: I can separate a process problem from a software problem before either one becomes an expensive build.
I kept seeing capable people trapped inside avoidable manual work.
During my enterprise career, I supported implementations spanning 130+ countries, worked with Medicare risk-adjustment programs, and maintained a 98% satisfaction rating across 50+ projects.
The same pattern appeared in very different environments: information arrived in one place, decisions happened somewhere else, and people spent their day carrying context between systems. The work was necessary. The repetition usually was not.
I moved into engineering when the tools became capable enough to address those problems directly. The business-analysis background still drives the work. APIs and models matter, but only after the operation itself makes sense.
Career path
How the work evolved
The technology changed. The underlying job stayed the same: understand the operation, then build the right intervention.
- 0101Foundation
Enterprise process analysis
Eleven years documenting workflows, business rules, handoffs, and failure points across healthcare and defense programs.
- 0202Build depth
Independent engineering
Open-source work across MCP servers, agent orchestration, API infrastructure, and production automation.
- 0303Client work
OrchestriAI delivery
The analysis and engineering now come together in practical systems for service businesses.
What I bring to a project
One line of accountability from discovery through handoff, with business rules and operating constraints treated as part of the engineering work.
Working model
From operational problem to owned system
The implementation changes by project. The responsibility chain should not.
Understand the operation
Before codeManual work
Where time, context, or follow-up is being lost
Business rules
What can be automated and what still needs judgment
Build the right system
Scoped deliveryWorkflow
Clear triggers, decisions, actions, and exception paths
Connections
APIs, data, applications, and people working together
Leave it operable
Client ownershipHandoff
Testing, documentation, access, and support boundaries
Start with the process
A weak process does not become useful because an AI model was added to it. I map the work first, then decide what technology belongs in the solution.
Build only when it is worth operating
Volume, failure cost, maintenance, and human review all matter. If the result will create more work than it removes, I will say that before a build starts.
Keep ownership clear
You receive the agreed source, documentation, credentials, and handoff. Managed hosting is optional recurring work, not a lock-in mechanism.
Open-source projects
Selected original repositories from a public portfolio with 250+ GitHub stars across orchestration, MCP, and developer infrastructure.
maestro
Multi-agent orchestration system with 30+ specialized agents that coordinate on complex tasks.
View repositoryvertex-ai-mcp-server
Model Context Protocol server for Google Cloud Vertex AI. Connects AI tools to Google's ML infrastructure.
View repositorygemini-load-balancer
API key rotation and load balancing for Gemini API. Handles rate limits across multiple keys automatically.
View repositoryProfessional certifications
Nine credentials across business analysis, project delivery, healthcare IT, cloud, CRM, and analytics.
CBAP
Business Analysis
PMP
Project Management
PMI-ACP
Agile
CSM
Scrum
CPHIMS
Healthcare IT
Salesforce Admin
CRM
Power BI
Analytics
Tableau
Analytics
Azure Fundamentals
Cloud
Start with the evidence you need
The About page gives you the background. These pages show the actual offers, proof, and technical work behind it.
Bring me the workflow that keeps wasting time.
In a 30-minute call, we can separate the process problem from the software problem and decide whether there is a sensible build behind it.
Book a Free Call