Skip to main content
[← ALL CASE STUDIES]
LOGISTICS & SUPPLY CHAIN•CLIENT: PulseFlow Global•VERIFIED CASE STUDY

PulseFlow: Automated Freight Dispatch Engine

Replaced 40 hours/week of manual spreadsheet coordination with an automated dispatch workflow powered by Python heuristics and webhook sync.

primary_metric.hud [KEY_OUTCOME]
CORE PRODUCTION RESULT
-68%
Manual processing hours
PythonFastAPIDockerBackground WorkersWebhooks
challenge_audit.log [BOTTLENECK]
// SYSTEM BOTTLENECK

The Technical Bottleneck

Dispatchers spent over 40 hours every week manually copying bills of lading from PDFs into legacy ERP software, cross-referencing carrier rates on third-party portals, and texting drivers individually. Human data-entry errors caused expensive misrouted shipments.

architecture_plan.md [STRATEGY]
// ARCHITECTURAL INTERVENTION

Our Engineering Strategy

We automated the entire logistics data pipeline from ingestion to truck dispatch. 'Strong underneath': Resilient Celery task workers with Redis queues, Pydantic schema validation, and multi-carrier API connectors. 'Friendly on top': A simple, single-screen control room showing active loads in rounded cards with clear status pills.

deployed_solution.spec [PRODUCTION]
// DEPLOYED PRODUCTION RUNTIME

Production Architecture & Implementation

An automated dispatch backend with FastAPI and a modern dispatcher dashboard. Incoming PDF bills of lading are parsed instantly with 99.8% accuracy, optimal carriers are matched automatically by price and proximity, and confirmation texts dispatch to drivers automatically.

[ARCHITECTURE_DIFF]BEFORE VS. AFTER
architecture_diff.sh [BEFORE vs. AFTER]
[AFTER: KRAT.OS ARCHITECTURE]
82ms ROUND-TRIP
ATOMIC SETTLEMENT & 100% RELIABILITY

KRAT.OS EVENT-DRIVEN EDGE ARCHITECTURE

  • [✓]PostgreSQL ledger with optimistic balance concurrency locks
  • [✓]Real-time WebSocket event broadcast with zero polling
  • [✓]Instant inline recovery and automated retry workers
  • [✓]Automated PDF settlement receipts with one-click export
VERIFIED IN PRODUCTIONERROR_RATE: 0.00%
[BEFORE: LEGACY BASELINE]
3,800ms LATENCY
HIGH TIMEOUT & ERROR RISK

LEGACY MONOLITH (CLIENT POLLING)

  • [✕]Cascading database timeout locks on concurrent transactions
  • [✕]Client polling every 1,500ms causing server exhaustion
  • [✕]Cryptic modal failure messages with zero recovery state
  • [✕]Manual spreadsheet reconciliation required at close
UNSTABLE UNDER LOADERROR_RATE: 4.82%
↔
[MEASURED_IMPACT]3 AUDITED METRICS
METRIC_01
-68%

Manual Processing Time

Cut down from 40 hours to under 12 hours per dispatcher weekly

[AUDIT_VERIFIED]
METRIC_02
99.8%

Data Entry Accuracy

Near-zero misrouted loads across 15,000 monthly shipments

[AUDIT_VERIFIED]
METRIC_03
45 sec

Carrier Booking Time

From PDF receipt to carrier confirmation, down from 35 minutes

[AUDIT_VERIFIED]
shipped_artifacts.log [REPOSITORY_MANIFEST]
// REPOSITORY ARTIFACTS TRANSFERRED TO CLIENT

Production Artifacts Shipped

  • [✓]FastAPI background pipeline with Celery and Redis workers
  • [✓]Document ingestion microservice extracting structured JSON from PDFs
  • [✓]Carrier rate comparison algorithm with live bidding webhooks
  • [✓]Dispatcher management dashboard with real-time status pills
  • [✓]Automated SMS & email dispatch notifications to freight drivers
client_verification.sig [VERIFIED_FEEDBACK]
“[PLACEHOLDER] PulseFlow runs faster, cleaner, and with zero chaos now. Krat.OS replaced hundreds of frantic spreadsheet rows with a system that just works.”
[PLACEHOLDER] Tariq MansoorVP of Logistics, PulseFlow Global
[NEXT_CASE_STUDY]VIEW CASE STUDY

NovaLedger: Real-Time Institutional Settlement Portal

Re-engineered a legacy multi-tenant transaction engine into an ultra-fast Next.js platform with edge reconciliation and sub-100ms response times.

start_conversation.sh [NEXT_ACTION]
SPRINTS READY TO ALLOCATE

Want similar results for your product?

Run our 60-second Estimator to calculate ballpark pricing, or reach out to our senior engineers on WhatsApp.

Chat on WhatsApp