Full-Time
Senior Implementation Project Manager
Professional Services
Project44
501-1,000 employees
AI-powered platform for real-time supply chains
No salary listed
Bengaluru, Karnataka, India
In Person
In-office 5 days weekly; IST time zone.
 Business & Strategy (1) |
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- 5-8 years of experience in a customer facing role or at an enterprise SaaS company
- Experience in the field of freight forwarding and supply chain management strongly preferred
- Proven track record of working for internally operating clients with a cross-functional and geographically dispersed team
- Must have excellent English, proficiency in French, Spanish or German are nice to have
- Strong communication and problem-solving skills, customer focus and results orientation
- In-depth knowledge and experience implementing Enterprise software applications such as ERP, CRM or SCM
- Monitoring projects progress and oversee project contributors, ensuring scope, planning, milestones, budgets and overall goals are met
- Strong client service and/or management consulting experience
- Willingness to work during customer’s operating hours outside of standard office hours
- Manage several engagements concurrently, potentially between 10 – 15 clients depending on project size
- Deliver our software solutions in time and on budget, being on top of project status and always driving issue resolution forward
- Manage various multi-national projects simultaneously and engage cross-functionally with stakeholders such as product management, engineering or network resources to achieve your project missions
- Manage financial metrics and enable pre-sales activities to support account expansion plans
- Present solutions effectively to diverse stakeholder groups and to engage effectively with senior executives of large enterprises on both technical and business topics
- Adhere to project44’s company standards, guidelines, policies and procedures
- Proficiency in French, Spanish or German
- Experience in the field of freight forwarding and supply chain management
Project44 provides an AI-powered platform that gives real-time visibility into goods moving through the supply chain. It tracks inventory in transit, containers, ships, ports, and terminals to help manage exceptions, reduce detention and demurrage fees, and improve on-time delivery. The platform ingests data from carriers and ports, uses AI to forecast delays and suggest smarter routing, and presents a global dashboard for proactive decision-making. It differentiates itself by combining end-to-end tracking with AI-driven risk detection and prescriptive insights across the network, helping turn supply chains into cost savings and more reliable operations.
Company Size
501-1,000
Company Stage
Late Stage VC
Total Funding
$892.5M
Headquarters
Chicago, Illinois
Founded
2014
Get referred to Project44
See people who can refer or advise you
Simplify's Take
What believers are saying
- Q1 FY27 new ARR grew 34%, with 67% from multi-year deals.
- Positive operating free cash flow in FY26 gives project44 real financial credibility.
- Autopilot and theft-prevention AI target urgent pain: freight spend, cargo loss, and manual work.
What critics are saying
- MyCarrier arbitration and SMC3 litigation expose brittle partner economics and IP leakage risk.
- FourKites and other visibility rivals keep pressure on pricing, features, and enterprise renewals.
- The LSP44 split signals product complexity; misexecution here breaks cross-sell and sales motion.
What makes Project44 unique
- Project44’s 1.5 billion annual shipments create a logistics data graph competitors cannot match.
- Decision44 and Mo embed context-aware AI directly into execution, not dashboards.
- LSP44 split separates shipper and carrier workflows, sharpening product focus and monetization.
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Benefits
Hybrid Work Options
Growth & Insights and Company News
6 month growth
↑ 0%1 year growth
↓ -1%2 year growth
↓ -1%project44 introduces Mo AI analyst to deliver faster, data-driven supply chain decisions. July 27, 2026 project44 has introduced Mo, a conversational AI supply chain analyst embedded within its Decision Intelligence Platform to help organizations access faster, more accurate operational insights using their own logistics data and business rules. Unlike generic AI tools or traditional analytics platforms, Mo leverages project44's real-time logistics data graph alongside a customer's shipment history, carrier performance, standard operating procedures, and key performance indicators to provide context-aware responses without requiring manual data exports or dashboard navigation. The AI analyst is designed to transform tasks that previously took hours or days into responses delivered within minutes while offering greater accuracy and lower latency. Internal testing showed Mo answering operational queries in approximately 2.5 minutes compared with roughly 18 minutes with Claude Opus, while avoiding significant calculation errors. Machine Learning & Artificial Intelligence At launch, Mo supports truckload, less-than-truckload, and ocean freight data, with additional capabilities planned through 2026 as part of project44's vision for customer-specific sovereign AI models. "We've spent more than a decade building the world's largest, most accurate, real-time logistics data graph. But connecting the data, cleaning it, and building rules to govern it was just the beginning. Mo takes all of this context and customer data and makes it simple and accessible, solving hours-long tasks in seconds. Multiply that across every question a supply chain team asks in a day and, suddenly, teams are going to see a fundamental transformation in how they work on a daily basis," said Jett McCandless, founder and CEO of project44.
project44 launches conversational AI analyst to turn real-time supply chain data into faster, more confident decisions. July 23, 2026 Mo reasons across each customer's own data and business rules, drawing on the context project44 provides through the world's largest, most accurate, real-time logistics data graph to answer questions teams can act on immediately. CHICAGO, July 23, 2026 - project44, the Decision Intelligence Platform for the modern supply chain, announced Mo, a conversational AI supply chain analyst built directly into the project44 platform. Mo reasons across a customer's own data and business rules, drawing on the world's largest, most accurate, real-time logistics data graph, so the answers operators get are more reliable, grounded in the data feeding them. Supply chain teams have more data than ever, but they can't get fast, reliable answers without manually pulling reports, navigating dashboards, or exporting data into a separate system. Most of the solutions that exist today, whether generic AI or traditional analytics platforms, are generalized to work the same way across every customer, so getting to a specific answer means building a one-off workaround for every question. That process delays decisions past the window where action still matters, and it bottlenecks through analyst experts, so most of the team can't self-serve an answer. Every extra step slows the decision down and chips away at trust in the answer itself. Mo's answers are grounded in a customer's own shipment data, business rules, and carrier history, not generic industry benchmarks or a vanilla LLM's training data. Tuned with a semantic understanding of the language and relationships that govern supply chain data, Mo reasons across a customer's SOPs, strategies, and KPIs alongside the world's largest, most accurate, real-time logistics data graph, to accurately understand the minutiae of a specific operations. Compared to a human-led process, Mo turns a task that used to take hours or days of manual analysis into an answer in minutes. Compared to a Claude Opus, Mo delivers more accurate answers, with lower latency and more consistent results. In internal testing, Mo answered operational questions using a customer's own real-time data in about 2.5 minutes, versus roughly 18 minutes for a Claude Opus after manually exporting data, filtering rows, and building context by hand. On accuracy, Claude OpusFS answers were wrong or overstated on most claims tested, including a late-rate calculation off by nearly 18 points and a claim that 40% of shipments were untracked when the actual figure was 0.8%. Mo needed none of the manual setup and returned accurate answers consistently. That grounding is what makes Mo reason rather than retrieve: it builds context over time from a customer's shipment history, weighs multiple variables instead of returning a single number, and signals when its confidence is low instead of guessing. The result is a reasoning architecture, not a chatbot wrapper, that's materially more accurate than Claude Opus, and one that puts supply chain insight in reach of a customer's whole team, not just their power users. project44 customers are already realizing measurable value from the data and context Mo is built on. In detention and demurrage alone individual enterprise customers are seeing results like a $6 million reduction in D&D exposure. In working capital, individual customers have found they can free up, $200K with a single question to Mo. In disruption management, faster access to the right signal has helped customers avoid costs tied to exceptions and delays, with results like $600K $1.8 million in savings tied to a single initiative. Mo scales this impact across every customer and every question, turning what used to take a dedicated analysis into something any team member can act on directly. At launch, Mo can query into Truckload (FTL), Less-than-Truckload (LTL), and Ocean Freight data with additional functionality being added through the rest of 2026. Mo is also a step toward a broader vision at project44: giving customers the inputs to build their own models on top of project44's semantics and context, what Project44 call sovereign models. Rather than relying on a single, one-size-fits-all AI, customers will be able to shape models grounded in their own data and business rules, with project44's data graph as the foundation. "We've spent more than a decade building the world's largest, most accurate, real-time logistics data graph. But connecting the data, cleaning it, and building rules to govern it was just the beginning. Mo takes all of this context and customer data and makes it simple and accessible, solving hours-long tasks in seconds. Multiply that across every question a supply chain team asks in a day and, suddenly, teams are going to see a fundamental transformation in how they work on a daily basis," said Jett McCandless, founder and CEO of project44. Mo is already delivering on its early promise. Ford, one of Mo's early access customers, has been using it to get answers faster, without waiting for reports or analysis from other teams first. "Supply chain decisions move fast, and the teams making them need answers that are just as fast. Mo will help us get there, putting accurate, data-grounded answers directly in the hands of the people who need them, without the back-and-forth of pulling reports or waiting on analysis," said Doug Cantriel, Head of North America Transportation at Ford Motor Company. About project44. project44 is the Decision Intelligence Platform for the modern supply chain. Its context-based AI transforms fragmented logistics management into unified intelligence, bringing certainty to global supply chain operations. With intelligent transportation management, end-to-end visibility, yard management and last mile solutions, and a preferred carrier network of 280,000 carriers, project44 connects over 1.5 billion shipments annually for over 1,000 leading brands in manufacturing, automotive, retail, life sciences, food and beverage, CPG, and oil, chemical and gas. Learn more at project44.com.
Project44 has split into two separate businesses to better serve distinct customer segments in the logistics technology market. The original company will continue serving enterprise shippers with decision intelligence, TMS, and visibility solutions. Meanwhile, LSP44 launches as a dedicated, profitable business focused on logistics service providers. CEO Jett McCandless says the split recognises that shippers and LSPs buy fundamentally different technologies. LSP44 leverages project44's decade-long infrastructure, including 280,000 carriers and 1.5 billion shipments processed, to offer AI agents with operational context. The platform claims 18% cost reductions in carrier procurement and 60% fewer shipment tracking calls. Project44 has reduced headcount from 1,200 to 582 employees through AI adoption. LSP44 launches with immediate profitability, distinguishing it from venture-funded competitors.
Project44 CEO Jett McCandless emphasised the importance of contextual data in AI deployment during a recent discussion with FreightWaves. The supply chain visibility platform has developed an Agentic Workflow Manager that orchestrates multiple AI agent providers, including Happy Robot and Vooma, alongside its own first-party capabilities from its LunaPath acquisition. McCandless argued that AI agents require rich contextual information to function effectively, citing shipment-level data, carrier relationships, and historical analytics as crucial inputs. The company processes approximately 75,000 LTL dispatches daily and recently automated over 2,000 dispatch matches that would have required manual intervention. Project44 has improved its platform data quality by roughly 2.5 times over three years, contributing to a net promoter score shift from negative 34 to positive 39. McCandless noted that whilst AI agents enhance operations, they cannot entirely replace traditional EDI or API integrations.
Supply chain visibility isn't just about tracking anymore. July 6, 2026 The U.S. Postal Service just admitted something uncomfortable: it can't reliably tell you where your package is. During a Senate committee hearing last week, Postmaster General David Steiner described a system where wedding invitations arrive after the wedding and bills show up past their due date. His fix? Deploying Bluetooth Low Energy beacons and bidirectional cameras to track containers through USPS facilities in real time. "This is not rocket science technology," Steiner told the Senate Committee on Homeland Security. "This is not technology that doesn't exist. This is technology that exists that other companies use." He's right. And that gap between what's possible and what most organizations actually do with visibility technology tells a bigger story. The supply chain visibility market has matured past the point of simply answering "where is my shipment?" The real question in 2026 is what happens next, and the best platforms are starting to answer that on their own. Visibility used to be a dashboard. Now it's an operating system. For years, supply chain visibility meant tracking numbers and status pages. You'd punch in an order ID, see "in transit," and hope for the best. If something went wrong, you found out when a customer called to complain. That model broke under the weight of modern supply chains. Companies now manage thousands of SKUs across dozens of carriers, multiple modes of transport, and warehouse networks spanning continents. A single shipment from a factory in Shenzhen to a retail store in Dallas might touch five carriers, two ports, three warehouses, and a last-mile delivery provider. Knowing the shipment left the port isn't enough when the container is sitting on a chassis at the rail yard 200 miles from its destination. The global supply chain management software market is projected to hit roughly $184 billion by the end of 2026, according to industry estimates. A significant chunk of that growth is coming from visibility platforms that have evolved well beyond basic tracking. Gartner's latest supply chain technology trends report, published just last week, identified what it calls "physical AI" (the combination of sensors, IoT, and AI-driven decision-making) as one of the top technology priorities for supply chain leaders this year. From tracking to triggering. The shift happening right now is from passive visibility to active execution. Instead of a dashboard that shows you a red dot on a map, the new generation of platforms detects the problem, assesses the impact, and either recommends or automatically takes corrective action. Consider what this looks like in practice. A temperature-controlled pharmaceutical shipment is moving from a distribution center in Memphis to a hospital system in Chicago. Traditional visibility tells the shipper the truck left Memphis at 6 a.m. and should arrive by 2 p.m. Modern visibility, by contrast, monitors the trailer's internal temperature every 30 seconds via IoT sensors, cross-references the route against weather data and traffic patterns, and calculates a dynamic ETA that updates continuously. If the temperature rises above the acceptable threshold, the system doesn't just flag it on a dashboard. It alerts the driver, notifies the receiving dock, checks whether a backup shipment can be rerouted from a closer warehouse, and updates the customer's order management system. All within minutes, often before any human even knows there's a problem. Project44, a visibility platform that Gartner has named a Magic Quadrant Leader for five consecutive years, rebranded its core offering as a "Decision Intelligence" platform in 2025. The naming is intentional. The company processes over 1.5 billion shipments annually for brands in CPG, automotive, retail, and manufacturing, and it's positioning the platform not as a tracking tool but as an operational brain that ingests visibility data and outputs decisions. They're not alone. FourKites, Overhaul, and Tive are all pushing in the same direction, layering predictive analytics and automated workflows on top of raw location data. What's actually making this possible. Three technology trends converged to make this shift work: IoT sensors got cheap and connectivity got better. Five years ago, putting a cellular-enabled sensor on every pallet was cost-prohibitive for most shippers. Today, smart labels from companies like Sensos cost a fraction of what they used to, require minimal infrastructure, and track shipments across all modes of transport (ocean, air, rail, and road). The falling cost of sensors means companies can move from tracking containers to tracking individual cases or even items. Cloud integration layers matured. The dirty secret of supply chain visibility has always been data fragmentation. Your TMS knows about transportation. Your WMS knows about warehouse inventory. Your OMS knows about customer orders. But nothing talked to anything else. Modern visibility platforms sit on top of these systems, pulling data via APIs and normalizing it into a single view. This isn't glamorous work, but it's what makes the "decision intelligence" layer possible. You can't make smart decisions with partial data. AI moved from prediction to prescription. Early AI applications in supply chain were predictive: here's when your shipment will probably arrive, here's the likelihood of a delay. Useful, but still reactive. The newer applications are prescriptive: given this delay, here's what you should do about it, and here are three options ranked by cost, speed, and risk. Some platforms are starting to execute those decisions automatically for low-risk scenarios, with human approval required only for high-stakes exceptions. The USPS problem is everyone's problem. What makes the USPS story relevant beyond government logistics is that their visibility gap mirrors what most companies face, just at a different scale. Steiner pointed out that the Postal Service struggles with visibility at handoff points, specifically when third parties handle package pickups or when customers drop packages at a post office themselves. Once the Postal Service's own infrastructure takes over, tracking works reasonably well. This is the universal challenge. Supply chains break at handoffs. The shipment leaves your warehouse with full visibility, then enters a carrier's network where tracking is spotty, then arrives at a cross-dock facility where it sits unscanned for hours, then gets loaded onto a last-mile vehicle with a different tracking system entirely. Each handoff is a visibility black hole. The USPS is attacking this with Bluetooth beacons embedded in test packages to identify bottlenecks, bidirectional cameras to track container movements within facilities, and reinforced scan compliance through employee training. These are fundamentally the same tools that private sector companies are deploying, just applied to a government agency that processes 127 billion pieces of mail annually. What this means for supply chain leaders. If you're running a supply chain operation and your visibility technology still consists of tracking numbers and periodic status updates, you're not just behind the technology curve. You're leaving money on the table. Companies with strong real-time visibility respond 2 to 3 times faster to disruptions than those relying on manual tracking and phone calls. That speed translates directly to lower detention and demurrage charges, fewer expedited shipments, better inventory positioning, and higher customer satisfaction scores. But the technology alone isn't enough. The organizations getting the most value from visibility platforms are the ones that have done the unsexy integration work first: connecting their TMS to their WMS to their OMS, establishing clean data pipelines, and defining the business rules that tell the system what actions to take when exceptions occur. Start with the handoffs. Map every point in your supply chain where a shipment moves from one system, carrier, or facility to another. Those are your visibility gaps. Then work backward from there: what data do you need at each handoff, what system provides it, and what should happen automatically when something goes wrong? The goal isn't to build the perfect dashboard. It's to build a system that makes the dashboard unnecessary for 90% of your shipments, because the platform is handling exceptions before anyone needs to look at them. Conclusion. Supply chain visibility has quietly undergone a transformation from a reporting function to an operational one. The USPS investing in Bluetooth beacons and cameras, Project44 rebranding as a Decision Intelligence platform, Gartner highlighting physical AI as a top trend: these are all signals pointing in the same direction. The next generation of visibility technology doesn't just tell you what happened. It tells you what to do about it, and increasingly, does it for you. The companies that treat visibility as a strategic capability (not just a compliance checkbox or a customer service tool) will have a measurable advantage in speed, cost, and resilience. And the ones still refreshing a tracking page and hoping for the best? They'll keep finding out about problems the old-fashioned way: when the customer calls.