Public Proof Ledger.
Every factual assertion, operational metric, and case study on this website references an entry in this registry. Claims without verified evidence are blocked from the build.
TextileMode automated capacity scheduling and machine allocation across 27 industrial looms, replacing manual spreadsheet dispatch across 10 clerks down to 1 review operator.
Client operates 27 industrial looms managing PKR 3 crore ($107,000/mo) monthly production for 18+ months live in continuous production.
Continuous production management of PKR 3 crore ($107,000/mo) across 27 industrial looms with zero manual spreadsheet dispatch.
18+ months live in continuous production across 27 industrial looms.
Real-time lot yield tracking across 3,000+ daily raw hide skins processed, eliminating 14% grading discrepancies.
Replaced physical paper tally clipboards with deterministic tablet-first scan workflows, eliminating 14% grading discrepancies.
Replaced physical paper tally clipboards with deterministic tablet-first scan workflows, eliminating 14% grading discrepancies.
Real-time lot yield tracking across 3,000+ daily skins with tablet-first deterministic validation.
Automated double-entry reconciliation between bank feeds and invoices, compressing month-end close from 3 business days down to 45 minutes with a 98% automated deterministic match rate.
98% automated deterministic match rate across bank feeds and invoices with strict schema validation.
Automated PDF and invoice parser extracting line items with deterministic checksum validation on synthetic commercial data.
Synthetic commercial dataset with deliberate injection attempts and corrupted line totals.
Deterministic recovery when encountering malformed inputs, duplicated webhooks, or prompt injections.
Demonstrates strict schema trapping and human-in-the-loop escalation.
Arslan Rehmani operates at the intersection of corporate finance and full-stack software engineering as an ACCA-qualified finance professional turned developer.
Brings general ledger rigor and internal controls to automated software pipelines.
Clients own 100% of their accounts, API keys, source code, and deployment infrastructure. No proprietary lock-in.
Systems are deployed directly to client cloud accounts (AWS, Supabase, Vercel, GCP).
LLM agent outputs are never written directly to databases without programmatic schema validation and business rule checks.
Zod / Pydantic schemas enforce type, range, and relationship constraints.
Every automated action, API call, and human approval creates an immutable audit trail with instant revert capability.
Action logs record input hash, agent output, supervisor sign-off, and DB transaction ID.
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