
Project evidence without invented metrics
These concise evidence notes describe public system patterns. Client names, commercial outcomes and numeric savings are excluded until source evidence and publication permission exist.

MCP Composer — agent workflow composition
Problem: tool-enabled agents become difficult to understand as integrations grow. Solution pattern: compose explicit MCP tools into inspectable workflows. Evidence: a public GitHub repository. Outcome: a reviewable implementation artifact; no business-impact metric is claimed.

Universal Document Parser — structured extraction
Problem: heterogeneous documents do not share one reliable schema. Solution pattern: ingestion, classification, schema mapping, extraction and validation. Evidence: a public GitHub repository. Outcome: a demonstrable document-to-JSON workflow; accuracy is not claimed without a benchmark dataset.

Model Router — provider selection
Problem: different requests may need different model capabilities, latency or cost constraints. Solution pattern: route requests through an explicit policy layer. Evidence: a public GitHub repository. Outcome: an inspectable routing implementation; production savings are not claimed.

Existing workflow and constraints
Each case records the manual or fragmented workflow, source formats, permissions, provider limits, review needs and material failure risks.

Architecture and implementation
The solution description separates deterministic services, model-assisted steps, data stores, tools, review points and operational controls.

Outcomes and metrics
Only evidence-backed measurements are published. Otherwise the case describes the verified workflow change and explicitly states that no numeric claim is being made.

Screenshots, demos and repositories
Where public material exists, the case links to source code, screenshots or demos and identifies Alper’s role and the project’s current status.

Lessons and related services
Every case closes with limitations, lessons and links to the relevant automation, agent or AI-systems service.

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