Replace Generic AI Logistics Tools with Custom Automation
Off-the-shelf AI logistics tools provide generic features on a per-seat subscription. Custom-built solutions integrate directly with your TMS and WMS for a one-time build cost.
Syntora develops custom AI solutions for the logistics industry, focusing on problems like route optimization and load matching. Unlike off-the-shelf tools, Syntora engineers systems that integrate directly with existing TMS and WMS platforms, addressing specific operational constraints and data formats. This approach ensures a tailored, production-grade service for unique logistics challenges.
A custom system is not a simple script; it is a production-grade service designed to handle your specific operational constraints, data formats, and business rules. It connects to the exact software you already use, pulling data from your sources and pushing results where your team can see them, without changing their workflow. Building such a system begins with a detailed assessment of your existing operations, data sources, and desired outcomes. Syntora would work with your team to define the technical requirements and architect a solution tailored to your specific logistics challenges. The scope of a custom build depends on factors like data complexity, integration points, and the desired level of automation.
What Problem Does This Solve?
Most small logistics companies hit a wall with off-the-shelf software. The load matching module in a standard TMS suggests carriers based on lane history, but it cannot read incoming emails to see a carrier's real-time availability. Your dispatchers still spend hours manually emailing 10 carriers to find one who can actually take a load, defeating the purpose of the tool.
Generic platforms like OptimoRoute are designed for field service, not freight. They assume all stops are equal and cannot prioritize a delivery to a high-value client or account for a 2-hour unloading window at a specific warehouse. This forces your planners to manually adjust every single route, wasting the time the software was supposed to save.
These tools fail because they treat your business process as a template. They force you into their predefined workflow instead of adapting to yours. They cannot access data from your other systems, like real-time driver locations from Samsara or inventory levels from your WMS, without expensive and brittle API connections that require constant maintenance.
How Would Syntora Approach This?
Syntora would start an engagement by establishing direct API connections to your relevant source systems. For a freight brokerage, this often means read-access to your TMS, like AscendTMS or MercuryGate, your email inbox via Microsoft Graph API, and your carrier database in Supabase. We would pull historical data, such as the last 12 months of load history and carrier communication, to create a baseline dataset for modeling and system training. The client would provide necessary API credentials and access to relevant data sources. This discovery and data collection phase typically takes 2-4 weeks.
The core logic for a custom system would be a Python service tailored to your specific needs. For route optimization, we would use the Google OR-Tools library to model your exact operational constraints, including driver hours-of-service, vehicle capacity by weight and volume, and customer-specific time windows. For load matching, Syntora would engineer a system that parses inbound carrier availability emails using the Claude API to extract structured data like truck type, availability date, and current location. We've built document processing pipelines using Claude API for financial documents, and the same pattern applies to logistics documents. This structured data would then be cross-referenced with your open loads from the TMS, and a Python algorithm, often using pandas, could rank available carriers and draft reply emails.
The delivered system would be deployed as a serverless function on AWS Lambda, triggered by an API Gateway endpoint. Syntora would implement structured logging with structlog feeding into AWS CloudWatch, and alerts would be configured for API error rates or processing times. Typical build timelines for the engineering phase of systems like these range from 6 to 12 weeks. Deliverables include a fully deployed, production-ready system, comprehensive documentation, and full ownership of the code in a private GitHub repository.
What Are the Key Benefits?
Automate Routes in 90 Seconds, Not 3 Hours
Our Python-based engine processes hundreds of stops and dozens of constraints, delivering daily plans before your first driver clocks in.
Pay Once, Not Per User Per Month
A one-time project cost with minimal monthly hosting on AWS, typically under $50. No recurring SaaS license that penalizes you for growing your team.
You Own the Code and the System
You get the complete Python source code in your own GitHub repository. There is no vendor lock-in; the system can be modified by any developer.
Know It's Broken Before Your Team Does
We build in monitoring from day one using AWS CloudWatch. You get a Slack alert if error rates spike, not an angry call from a dispatcher.
Connects to the Tools You Already Use
Direct API integrations with your TMS, WMS, and telematics systems like Samsara. Data flows automatically without manual CSV exports or imports.
What Does the Process Look Like?
System Audit & Access (Week 1)
You provide read-only API keys for your TMS, WMS, and other relevant systems. We map your current manual workflow and confirm the data points for automation.
Core Logic Build (Weeks 2-3)
We write the Python services for the core logic, such as route planning or carrier matching. You receive a staging environment link to test outputs with real data.
Integration & Deployment (Week 4)
We connect the new service to your production systems and deploy it on AWS Lambda. Your team uses the system for the first time with our direct support.
Monitoring & Handoff (Weeks 5-8)
We monitor system performance and handle any issues. At the end of week 8, you receive a full runbook detailing the architecture and maintenance procedures.
Frequently Asked Questions
- How is a project like this scoped and priced?
- Pricing is based on the number of systems to integrate and the complexity of your business logic. A route optimizer with standard constraints takes less time than a demand forecasting model needing 24 months of sales data. After a 30-minute discovery call, we deliver a fixed-price proposal with a detailed timeline, typically a 4-6 week build.
- What happens if an external API like our TMS goes down?
- The system is built with resilience in mind. We use retry logic with exponential backoff for transient API errors. If a critical system is down for an extended period, the service logs the failure and sends a Slack alert. It will not crash; it waits for the service to recover and processes the backlog.
- How is this different from hiring a freelance developer on Upwork?
- A freelancer builds what you ask for. We build what your business needs. Our process includes discovering the operational problem behind the technical request. We build, deploy, and monitor production systems, which is a different skillset from just writing a script. You are not hiring a coder; you are hiring an engineering partner who owns the outcome.
- Can this system be updated as our business changes?
- Yes. Because you own the code, modifications are straightforward. A common update is adding a new delivery constraint or integrating a new carrier network. We can scope these as small, follow-on projects. The system is designed with modular Python functions, so changing one piece of logic does not require rebuilding the entire application.
- What kind of data do we need to provide?
- For route optimization, we need at least 6 months of delivery history including addresses, service times, and vehicle information. For demand forecasting, we need a minimum of 18-24 months of clean sales or order data. We assess your data quality in the first week before the main build begins to ensure the project will be successful.
- Is there a user interface for this system?
- Typically, no. These systems run as background services. They pull data from one system, process it, and push the result into another. For example, a route plan appears directly in your TMS or is emailed to drivers. This avoids creating another screen for your team to check and keeps them working in tools they already know.
Ready to Automate Your Logistics & Supply Chain Operations?
Book a call to discuss how we can implement ai automation for your logistics & supply chain business.
Book a Call