A structured portfolio for AI, software engineering, automation and SaaS work. We separate verified customer evidence from illustrative solution patterns so the story stays credible.
The following entries establish the case-study architecture for publication. Until customer names, metrics and approvals are supplied, they remain solution patterns—not claims of completed client results.
Design a workflow that extracts, classifies, validates and routes business documents with AI and human review.
Build and evolve a product across web applications, APIs, cloud infrastructure and data services.
Connect customer workflows with AI assistance, approved tools and automation while keeping people in control.
Engineer digital commerce, billing, order management and delivery workflows around a consistent operational model.
Use a small set of agreed measures instead of generic marketing numbers. Select metrics that match the customer's actual business objective.
Use the portfolio to show the breadth of Zkandavel's engineering work without implying that every pattern below is a published customer engagement.
Agents, enterprise RAG, document intelligence, decision support and intelligent automation.
Explore AI →Product engineering, web, mobile, APIs, modernization, cloud and data foundations.
Explore Engineering →Marketing, CRM, commerce, billing, operations, bookkeeping and business platforms.
Explore ZKLabs →This release creates a practical publication gate so portfolio pages can become stronger as approved material becomes available.
Customer identity, project description, metrics and permission have been confirmed for publication.
Useful engineering or operational evidence that can be published without attributing it to a customer.
A representative architecture or capability used to explain what Zkandavel can build; not a client-result claim.
Customer context → business problem → scope → architecture → delivery → measurable outcome → technology → lessons learned → approval record.
Provide the project context, permitted customer attribution, before/after measures and publication approval. We can turn it into a concise case study without inventing results.