AI Consulting for Logistics
Your TMS Has the Data. Your Team Still Does the Work by Hand.
RTS Labs builds production AI systems that cut manual work across freight, fleet, and warehouse operations — and integrate with the systems you already run.
Ships production systems, not slide decks. Senior engineering oversight. Built around your stack.
Strategy
Production
Source Systems
Core · warehouse · docs · email
Siloed
Extraction & RAG
Documents → structured, searchable data
Models & guardrails
Scoring · evaluation · monitoring
In production
Integrated · audited · owned by your team
Live
Trusted to Build for Freight, Fleet & Warehouse Teams
Why It Matters
The Demo Works. The Pilot Doesn't Ship.
Logistics AI rarely fails because the model is wrong. It fails because it never reaches the workflow. Here’s where it breaks down — and how we’re different.
Where Most Teams Are Stuck
Pilots built on exported CSVs that die the moment they meet your live TMS, WMS, and EDI
Carrier names, location codes, and accessorial terms inconsistent across systems
AI that lives beside the workflow instead of inside it — so dispatchers never adopt it
No approvals, permissions, or audit logs — so ops can’t let software act
Where RTS Labs Takes You
We find the workflow, then validate the data before we build
Integrated into your live TMS, WMS, ERP, and EDI — not a sandbox
Approvals, permissions, and audit logs so ops can let it act
Real architecture that scales past phase 1 to the network
Sound Familiar?
The Problems We're Actually Called In to Solve.
No hype, no transformation theater. These are the operating pains we hear from CIOs, CDOs, COOs, and heads of risk every week — and the ones our builds are designed to remove.
“Chasing status across five systems.”
Your team chases updates across email, calls, portals, and spreadsheets. The status exists somewhere — finding it is a full-time job.
“The TMS has the data — people do the work.”
Your TMS has the data, but your people still do the work manually. Re-keying, copy-pasting, and reconciling between systems that don’t talk.
“Exceptions surface too late.”
By the time a delay surfaces, the detention clock is running and the customer is already calling.
“Margin leaks you catch after the fact.”
Margin leaks through bad data, slow decisions, and manual handoffs. You see it in the freight audit — after the money’s already gone.
“Customers want answers you can't give fast.”
Customers want real-time answers your systems can’t give. Service teams burn hours assembling status your stack already knows.
“Another tool nobody adopts.”
Every new tool is one more place to log in. The AI demo looked great — until it hit your real workflow and nobody adopted it.
Where We Apply It
Use Cases We Ship Into Production.
Specific financial workflows — not generic “AI solutions.” Filter by the part of the business you’re trying to move.
Operations Copilots
Dispatcher Copilot
Pain
Dispatchers assemble load context that lives across five systems.
We Build
Surfaces load context, flags risk, and drafts the next action inside the dispatcher’s existing screen.
Impact
Less time hunting for context, faster decisions on the loads that matter.
For: VP of Operations
Operations Copilots
Customer Service Copilot
Pain
CSRs burn hours assembling status your stack already knows.
We Build
Answers “where’s my shipment?” with live status, drafted replies, and proactive delay notices.
Impact
Faster customer responses and consistent answers, every shift.
For: Head of Customer Service
Operations Copilots
Carrier Operations Assistant
Pain
Carrier history and performance are scattered across systems.
We Build
Pulls carrier history, compliance, and performance into one view for faster, smarter decisions.
Impact
Smarter carrier decisions without the manual lookup.
For: Carrier Ops
Agentic Automation
Exception Management Agent
Pain
High-volume exceptions eat hours of manual back-and-forth.
We Build
Detects delays and dwell, gathers context, and routes a recommended resolution for approval.
Impact
Exceptions caught early — with a human approving every action.
For: Operations
Agentic Automation
Shipment Status Agent
Pain
Customers ask for status before your team can push it.
We Build
Monitors tracking signals and pushes proactive updates before the customer has to ask.
Impact
Fewer status calls, more proactive notices.
For: Customer Service
Agentic Automation
Rate Quote Agent
Pain
Spot quotes are assembled by hand, slowly.
We Build
Assembles spot quotes from rate data and lane history so reps respond in minutes, not hours.
Impact
Faster quotes and more wins, without the manual lookup.
For: Pricing · Sales
Predictive Intelligence
ETA & Delay-Risk Prediction
Pain
Delays are visible only after the damage is done.
We Build
Scores each load’s risk of running late so teams act on the ones that matter first.
Impact
Earlier intervention and fewer surprise costs.
For: Operations
Predictive Intelligence
Margin Leakage Detection
Pain
Cost drifts above plan and you see it after the freight audit.
We Build
Flags loads and lanes where cost is quietly drifting above plan — while you can still act.
Impact
Margin protected before the money’s gone.
For: Finance · Ops
Data & Integration
System Integration Layer
Pain
AI can’t run on fragmented systems and dirty data.
We Build
Connects TMS, WMS, ERP, CRM, EDI, telematics, email, and documents into one usable flow.
Impact
AI that works on your real data, inside your real systems.
For: CIO · CTO
Data & Integration
Data Pipelines & Quality
Pain
Carrier names, codes, and accessorials are inconsistent across systems.
We Build
Cleans, standardizes, and moves operational data so it’s trustworthy enough to act on.
Impact
A foundation that turns “the data’s a mess” into “we shipped it.”
For: Data Engineering
Operations Copilots
Dispatcher Copilot
Pain
Dispatchers assemble load context that lives across five systems.
We Build
Surfaces load context, flags risk, and drafts the next action inside the dispatcher’s existing screen.
Impact
Less time hunting for context, faster decisions on the loads that matter.
For: VP of Operations
Operations Copilots
Customer Service Copilot
Pain
CSRs burn hours assembling status your stack already knows.
We Build
Answers “where’s my shipment?” with live status, drafted replies, and proactive delay notices.
Impact
Faster customer responses and consistent answers, every shift.
For: Head of Customer Service
Operations Copilots
Carrier Operations Assistant
Pain
Carrier history and performance are scattered across systems.
We Build
Pulls carrier history, compliance, and performance into one view for faster, smarter decisions.
Impact
Smarter carrier decisions without the manual lookup.
For: Carrier Ops
Agentic Automation
Exception Management Agent
Pain
High-volume exceptions eat hours of manual back-and-forth.
We Build
Detects delays and dwell, gathers context, and routes a recommended resolution for approval.
Impact
Exceptions caught early — with a human approving every action.
For: Operations
Agentic Automation
Shipment Status Agent
Pain
Customers ask for status before your team can push it.
We Build
Monitors tracking signals and pushes proactive updates before the customer has to ask.
Impact
Fewer status calls, more proactive notices.
For: Customer Service
Agentic Automation
Rate Quote Agent
Pain
Spot quotes are assembled by hand, slowly.
We Build
Assembles spot quotes from rate data and lane history so reps respond in minutes, not hours.
Impact
Faster quotes and more wins, without the manual lookup.
For: Pricing · Sales
Predictive Intelligence
ETA & Delay-Risk Prediction
Pain
Delays are visible only after the damage is done.
We Build
Scores each load’s risk of running late so teams act on the ones that matter first.
Impact
Earlier intervention and fewer surprise costs.
For: Operations
Predictive Intelligence
Margin Leakage Detection
Pain
Cost drifts above plan and you see it after the freight audit.
We Build
Flags loads and lanes where cost is quietly drifting above plan — while you can still act.
Impact
Margin protected before the money’s gone.
For: Finance · Ops
Data & Integration
System Integration Layer
Pain
AI can’t run on fragmented systems and dirty data.
We Build
Connects TMS, WMS, ERP, CRM, EDI, telematics, email, and documents into one usable flow.
Impact
AI that works on your real data, inside your real systems.
For: CIO · CTO
Data & Integration
Data Pipelines & Quality
Pain
Carrier names, codes, and accessorials are inconsistent across systems.
We Build
Cleans, standardizes, and moves operational data so it’s trustworthy enough to act on.
Impact
A foundation that turns “the data’s a mess” into “we shipped it.”
For: Data Engineering
How We Partner
From Your Highest-Cost Workflow to a Production System.
A focused phase-1 build you can put in front of real users — not a multi-year program that never ships.
1
Find the Workflow With the Most Pain
We map where manual work, delays, rework, and cost leakage actually show up — and pick the one with the clearest payback.
Clarity on where AI is worth it first
2
Validate the Data and Systems
We review your TMS, WMS, ERP, CRM, EDI, telematics, documents, and reporting flows to confirm what’s feasible — before building.
No surprises once development starts
3
Build a Phase-1 Production Solution
We design, build, integrate, test, and deploy a focused AI workflow your team can actually use — with the right controls in place.
Real usage, not a sandbox demo
4
Measure, Improve, and Scale
We track adoption, accuracy, cycle time, cost savings, and exception reduction — then extend the win across more lanes and teams.
Compounding value past phase 1
Why RTS Labs
The Team That Builds It — Not Just the One That Recommends It.
Plenty of firms will write you a strategy. Fewer will stand up the production system, own the data and security, and stay past the demo. Here’s how we compare to the usual options: big consulting firms, offshore dev shops, and AI prototype vendors.
Big Consulting Firms
Offshore Dev Shops
AI Prototype Vendors
Ships Production Systems, Not Slide Decks
Senior Engineers on Your Account
Owns Data, Security & Governance
Phase 1 Live in Weeks, Not Quarters
Stays Past the Demo — Eval & Monitoring
See It in Action
AI’s Role in Transforming Supply Chains.
Join our Founder/CEO, Jyot Singh, on Ticker News as he explores how AI transforms last-mile delivery and how generative AI enhances demand forecasting using sentiment analysis.
Why Logistics Leaders Trust Us to Build
A Delivery Team, Not a Deck Factory.
No vanity metrics. These are the things that decide whether an AI system survives contact with production, compliance, and your risk team.
Production Over Prototypes
We ship systems people use every day — measured by outcomes, not demos.
Senior Engineering Oversight
Experienced engineers own the architecture and the result, end to end.
Real Integration Experience
We connect to the operational systems logistics actually runs on.
Data Engineering Depth
We make fragmented, messy operational data trustworthy enough to act on.
Custom Software Background
APIs, internal tools, and integrations — built to fit, not forced to fit.
Security-Conscious Architecture
Permissions, controls, and guardrails designed in from the start.
Evaluation & Monitoring
We measure accuracy and watch systems in production — not just at launch.
Human-in-the-Loop by Design
People keep authority over the decisions that matter most.
Phase-1 First, Then Scale
A focused, scalable architecture that grows from one workflow to the network.
Proof
What It Looks Like When It Ships.
One representative engagement, plus the template we use to capture results. Metrics appear as slots — we populate them with your real, audited figures.
Logistics · Exception Management
Catching Exceptions Before the Customer Calls
A mid-market 3PL surfaced delays only when a customer called; dispatchers reconciled status across TMS, email, and carrier portals by hand. RTS Labs built an exception agent that monitors tracking signals, gathers context, and routes a recommended resolution for one-click approval.
“RTS Labs helped us move from chasing status by phone and email to a production exception agent — cutting manual touches per load and giving our ops team visibility we never had.”
— VP of Operations, Mid-Market 3PL
Case Study Template · Agentic Automation
Spot Quotes in Minutes, Not Hours
Problem
Reps assembled spot quotes from rate data and lane history by hand.
What RTS Built
A rate-quote agent that assembles quotes from rate data and lane history.
Metrics to Collect
Quote turnaround · win rate · rep hours returned
Case Study Template · Data & Integration
Freight Invoice Reconciliation
Problem
Invoices were matched to rate confirmations and accessorials by hand.
What RTS Built
An agent that matches invoices and flags only the true discrepancies.
Metrics to Collect
Audit exceptions · recovery · hours saved
Our Clients' Words
Testimonials
S2 Software
- James Barham | Software Operations Director
CSuite Accelerator
- Ephraim Schachter | Founder & CEO
Dumpster Dudez
- Brian Johnson | President
National Center for Teacher Residencies
- Chris Lozier | COO
Blue Ocean Brain
- Gemma Brooks | COO
Momentum Holdings
- Carol Trotter | Momentum Holdings
Questions, Answered Straight
Before You Talk to Us.
What is AI consulting for logistics?
AI consulting for logistics helps transportation, freight, warehouse, and supply chain companies use AI to solve real operational problems, then build and ship the systems that run them. The goal isn’t to “use AI,” it’s to cut manual work, surface delays earlier, protect margin, and help teams decide faster, all integrated into the TMS, WMS, and ERP you already run.
What are the best AI use cases for logistics companies?
The best AI use cases are tied to a clear, expensive workflow. Strong starting points include shipment-status copilots, exception and dwell detection, freight-invoice and accessorial matching, spot-quote generation, late-load risk scoring, demand and capacity forecasting, document extraction from BOLs and PODs, and predictive fleet maintenance. A good first project has clean-enough data, a measurable outcome, and a workflow where speed or accuracy matters.
Which logistics workflows deliver the fastest payback?
The fastest-payback workflows usually already have their data sitting in your TMS, so AI just surfaces and acts on it. Three consistent winners: a dispatcher copilot that flags at-risk loads and drafts the next action inside the existing screen, an exception agent that detects delays and routes a resolution for approval, and customer status automation that answers “where’s my shipment?” with live data and proactive delay notices.
Does logistics AI integrate with our TMS, WMS, and ERP?
Yes, and this is exactly where most AI vendors fail. We connect to TMS, WMS, ERP, CRM, EDI, telematics, email, and documents using live data connections, not CSV exports. Before building anything, we audit your data flows and confirm what’s feasible. Without integration, AI becomes another dashboard nobody opens. With it, AI lives inside the workflow your team already uses.
Is our operational data clean enough for AI?
You don’t need perfect data to start, but you need access to the right data. Carrier names, location codes, and accessorial terms are inconsistent across most logistics stacks, and no model survives dirty, fragmented data. We assess data quality early, fix the foundation as part of the work, and confirm feasibility in discovery before promising an outcome.
How long does it take to implement AI in logistics?
A focused phase-1 build typically ships in 8 to 12 weeks, after a 2 to 4 week discovery. The sequence is discovery and use-case selection, data assessment, build and integration, user testing, production deployment, then monitoring and improvement. The fastest projects solve one specific operational problem instead of trying to transform the whole operation at once.
How much does AI consulting for logistics cost, and how do you measure ROI?
Cost depends on the workflow, the number of systems involved, and the state of your data, so we scope it in a fixed-price discovery before any build commitment. ROI is tracked against operating metrics set with you in phase 1: dispatcher time saved, exception cycle time, ETA accuracy, detention and freight-audit leakage, manual hours removed, and inbound status calls deflected.
Why do logistics AI pilots fail to reach production, and why is this different?
Logistics AI rarely fails because the model is wrong. It fails because the pilot ran on exported data that didn’t match live systems, the output lived beside the workflow so nobody adopted it, or there were no controls so ops leaders wouldn’t let it act. We address all three before writing code: live data connections validated in discovery, AI built inside existing tools, and human approval plus audit logs on every action.
Is agentic AI safe for logistics operations?
Yes, when it’s controlled rather than autonomous. Agentic AI means giving a system a clear job, access only to approved systems, defined permissions, and rules for when to act, ask, or escalate. Every consequential action runs through a human approval gate with a full audit trail, role-based permissions, and monitoring, so your team keeps authority over money and customers.
How is RTS Labs different from a big consulting firm or offshore dev shop?
Big consulting firms write the strategy and hand the build to a junior team you never met. Offshore shops build what you spec, not what your workflow needs. RTS Labs is delivery-first: the senior engineers who scope your project build it, we integrate into your production stack with controls and monitoring, and we stay through go-live and past it. We own the outcome, not just the tickets.
Should we build a custom AI solution or buy off-the-shelf logistics software?
It depends on the workflow. Off-the-shelf tools work well for common, standardized tasks, but they’re built for the average operation, and your TMS configuration, carrier naming, EDI schemas, and exception workflows aren’t average. Custom AI fits when the work is unique or the data spans many systems. Most logistics companies land on a hybrid: keep the platforms that work, then build AI around the gaps that create the most friction. RTS Labs builds that connective layer and integrates it into your live stack.
Does logistics AI work for freight brokers, 3PLs, and asset-based carriers?
Yes, and the highest-value use cases differ by model. Freight brokers gain most from rate-quote automation, carrier selection, and customer status agents. 3PLs prioritize exception management, warehouse-to-transport handoffs, and client reporting. Asset-based carriers see the fastest returns from ETA prediction, detention risk scoring, and driver communication. Discovery identifies which workflows fit your specific operation before we scope the build.
Let's Talk
Find Your Highest-Value AI Workflow — and Ship It.
Tell us where the manual work and cost leakage live. We’ll review your workflow, systems, and goals before the call so we can talk specifics — no pitch deck.
A senior engineer on the call — not a salesperson
A straight read on feasibility, data, and effort
A practical first step, whether or not you work with us