Three inspectable examples spanning research governance, review-queue design and operating-cost analysis.
Three places to start
Runnable synthetic work sample
3PL Cost Leakage Digital Twin
Task: Turn synthetic invoice and rate tables into a row-linked exception report.
Contribution: Project direction by Kamal; AI-assisted implementation and verification. The original case and its separate audit-priority scenario are retained.
Inputs → processing → output: Invoice + dated-rate CSVs → strict validation, unique-rate selection and fixed-point arithmetic → linked discrepancies, review states and JSON/display-CSV reports.
Technology: React · JavaScript ES modules · BigInt fixed-point arithmetic · CSV validation · Node test runner
What the reviewer can do: Load the fixture or two synthetic CSV files, change an amount, inspect excluded rows and export the report.
Limits: Bounded USD flat-rate comparison. No live logistics connection, real invoices, audited overcharges, recovered savings, client adoption or commercial freight-audit capability is claimed.
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Synthetic workflow prototype
AI Operations Control Tower
Task: Make missing intake evidence and human-review boundaries explicit before routing a request.
Contribution: Project direction by Kamal; AI-assisted implementation. The editable extension translates review questions into a transparent, bounded intake workflow.
Inputs → processing → output: Synthetic request + required-field policy → deterministic completeness and evidence checks → clarification items, field-state rows and human-review status.
Technology: React · Pure JavaScript evaluator · JSON schema + fixtures · In-memory state
What the reviewer can do: Edit the synthetic request, remove a required field, change required-field policy and inspect the resulting clarification state.
Limits: Author-configured policy, not a practitioner-validated top three. No deployed LLM, autonomous decision, backend ticketing service, client use or measured resolution-time gain.
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Synthetic dependency/scenario prototype
Data-Center Speed-to-Power War Game
Task: Separate screened research-computing demand, dependency evidence and fallback cost assumptions before a readiness decision.
Contribution: Project direction by Kamal; AI-assisted implementation of an evidence-led extension alongside the original readiness and timeline lenses.
Inputs → processing → output: Synthetic capacity, stakeholder evidence and cost assumptions → ratio, freshness, dependency and fallback checks → review gates and wait-versus-fallback comparison.
Technology: React · Pure JavaScript evaluator · JSON schema + fixtures · Explicit cost arithmetic
What the reviewer can do: Change screened demand, capacity, dependency readiness or fallback cost; inspect recalculated ratios and explicit review issues.
Limits: Research-computing allocation scope. No commercial data-center forecast, validated stakeholder ranking, procurement approval, calibrated probability or realized outcome.
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Supporting research
Provisional snapshot — pending final Iteration 15 replacement. Snapshot dated 2026-09-02. Awaiting independent audit; not the latest live analytical work or an independently certified release.
Media, platforms & capital allocation
A source-linked decision system connecting company histories, the current media landscape, transaction economics, streaming unit economics and governed capital allocation.
Contribution: Designed and governed by Kamal Babu Gokanakonda. AI-assisted research, modelling, coding, drafting and quality assurance under that direction.
Source-linked research · Scenario modeling · Decision and reversal traces
Overview, models, charts, evidence records, data views and the selected snapshot downloads.
Provisional snapshot — pending final Iteration 15 replacement. Independent release audit remains pending; scenario assumptions are not forecasts.
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