Automation that runs your
revenue engine.
I design and build n8n, Python, and Docker systems for GTM, RevOps, and supply chain & logistics teams. High-leverage architecture over manual execution.
<0 min lead response
0/7 autonomous uptime
n8n · Python · Docker
Ingest.py
n8n Engine
PostgreSQLCore Competencies
GTM Automation
Architecting lead routing, enrichment pipelines, and personalized outreach sequences at scale. Connecting CRMs, intent data providers, and execution channels.
RevOps Automation
Synchronizing billing systems, contracting platforms, and CRM data. Ensuring revenue data integrity and automating commission calculations.
Supply Chain & Logistics
Building bespoke inventory tracking, dynamic routing alerts, and vendor API integrations. Reducing manual data entry across fragmented logistics tools.
Dynamic Capacity-Based Pricing Engine
A logistics client needed to adjust quotes based on real-time fleet capacity and market rates. I engineered a solution using Python for the pricing logic, orchestrated by n8n, and deployed via Docker.
LatencyReduced quote generation time from 15 minutes to <2 seconds.- Margin ProtectionAutomated rules prevented underpriced bookings during peak demand.
def calculate_dynamic_rate(base_rate, current_capacity, market_multiplier):
"""Calculates the final rate based on real-time capacity."""
if current_capacity < 0.2:
# Critical capacity threshold
surge_factor = 1.5
elif current_capacity < 0.5:
surge_factor = 1.2
else:
surge_factor = 1.0
final_rate = base_rate * surge_factor * market_multiplier
return round(final_rate, 2)Frequently asked questions
- Who is Rajan Shrestha?
- Rajan Shrestha is an AI & automation consultant and industrial engineer who builds n8n, Python, and Docker systems for GTM, RevOps, and supply chain & logistics teams. He specializes in connecting a company’s existing tools so data entry happens once and downstream steps run without a human in the loop.
- What does Rajan build?
- Automation systems that replace manual, repetitive operations — capacity-based pricing engines, lead routing and follow-up, ad-budget allocation, route optimization, and cross-tool ops dashboards. Each is designed as architecture that runs continuously rather than a one-off script.
- What tools and technologies does he use?
- n8n for orchestration, Python for custom logic like scoring and optimization, and Docker so each system can be deployed on a client’s own infrastructure. The wider stack includes PostgreSQL, LangChain, TypeScript, and AWS.
- How much does an automation build cost?
- Every engagement starts as a fixed-price build. The exact number depends on the number of integrations, whether the logic needs custom Python, and how much data needs migrating — you get a firm quote after a short scoping call, not an hourly estimate. Most clients then add an optional monthly retainer for ongoing optimization.
- Which industries does he work with?
- Go-to-market (GTM), revenue operations (RevOps), and supply chain & logistics — areas where the automation patterns repeat, such as pricing logic, lead routing, ops reporting, and route optimization.