AS

Akaash Shanmugapandian

Lead Applied AI & Backend/Platform Engineer (Procurement AI)

Global engagements
50K+ suppliers/month harmonized
Status: OPERATIONAL
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09:00 AMThe Intake

Self-Serve Workbench + Execution Platform

Deep dive: the consultant pain, the system design, and what gets produced for the next step in the workflow.

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Problemstep_1

Messy Input Reality

“Client sent 50 exports with different schemas. I have 48 hours to build a spend cube.”

If this isn’t standardized + replayable, every downstream analysis becomes a one-off fire drill.

What the consultant does
  • Select a workflow template; upload raw extracts; map columns once.
  • Run jobs self-serve; monitor progress; rerun safely with parameter tweaks.
What they get (artifact)
  • A standardized spend cube foundation with deterministic outputs.
  • A repeatable run record (what ran, when, and what files came out).
System Design
Workbench UIAPI
System diagram placeholder: Self-Serve Workbench + Execution Platform

System design: intake → orchestration → execution → artifacts.

Flow
Upload -> Validate -> Queue -> Execute -> Artifact Delivery
Stack
Vue.jsFlask APIAzure Queues/BlobDatabricks JobsPostgres
Under the hood (sanitized)
  • Queue-backed orchestration dispatching Databricks workloads with capacity-aware scheduling.
  • Blob-first artifact contracts + progress tracking so non-technical teams can trust the runner.
Impact
  • Cuts “please run this notebook” bottlenecks and reduces time-to-first-usable-output.
  • Turns consulting workflows into reusable services (not scripts).
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