Current stage: foundation and evidence-building. This is a live project record, not a claim that every planned certification, portfolio system, or commercial service is already complete.
Why I’m doing this
More than a certification project
The original program was designed to turn years of software-engineering experience into a stronger AI + cloud architecture specialization. I’m keeping that career path open, but I’m also using the same work to build a Cloud + AI Architecture practice inside Akil Digital.
The operating idea is simple: build real systems, preserve the decisions and failures, publish the parts that are useful, develop technically defensible case studies, and eventually connect that evidence to narrowly scoped architecture services.
Current
Architecture lab foundation
The private control workspace now contains a GitHub Codespaces environment, a small FastAPI baseline, AWS/Terraform tooling, and a Terraform AWS smoke-test workspace. The project also uses Akil Digital’s evidence-heavy tracking standard.
Next
AWS architecture depth
The next technical work moves through networking, identity, availability, data services, serverless/event-driven systems, security, disaster recovery, Terraform, and production-style architecture decisions.
Later
Production AI systems
The roadmap culminates in architecture-grade work around an enterprise RAG platform, a production agentic AI platform, and a commercial AI SaaS architecture.
How I’m measuring progress
Shipped evidence, not study hours
A lab is not finished because I watched a course or read a chapter. The standard is working evidence: architecture diagrams, Terraform, tests, security decisions, cost reasoning, observability, failure analysis, ADRs, deployment proof, and the ability to explain the tradeoffs.
Where older work exists without durable runtime evidence, I’m recording that honestly and re-validating it rather than upgrading the claim after the fact.
Portfolio system 1
Enterprise RAG Platform
A production-style retrieval system covering ingestion, retrieval quality, citations, evaluation, tenant/security boundaries, observability, cost, and infrastructure.
Portfolio system 2
Production Agentic AI Platform
An agent architecture focused on tool permissions, state, human approval, auditability, retries, failure recovery, tracing, and evaluation.
Portfolio system 3
AI Business Intelligence SaaS
A commercial-style multi-tenant AI architecture combining cloud infrastructure, RAG, agents, metering, security, background work, and operational controls.
Business direction
Turning architecture capability into an Akil Digital practice
I’m not starting by pretending to be a finished enterprise cloud consultancy. The business side will advance with the evidence.
The first service I expect to test is a tightly scoped Cloud + AI Architecture Assessment / Blueprint: requirements, architecture, major service decisions, security/reliability/cost risks, and prioritized recommendations. Broader implementation or fractional-architecture work comes later if the technical evidence and market demand justify it.
Follow the build as the evidence accumulates.
I’ll publish selected architecture notes, walkthroughs, diagrams, case studies, and lessons under Akil Digital Insights instead of exposing the raw private working repository.