![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 130 45" xmlns="http://www.w3.org/2000/svg"><g d="M 60.5 22.618 C 60.5 15.571 65.567 12.063 71.082 12.063 C 76.597 12.063 80.085 15.1 80.352 19.523 L 75.912 19.523 C 75.583 17.311 73.735 15.866 71.052 15.866 C 67.505 15.866 64.762 18.343 64.762 22.618 C 64.762 26.894 67.505 29.578 70.962 29.578 C 73.556 29.578 76.12 27.72 76.12 25.803 C 76.12 25.302 75.792 24.977 75.285 24.977 L 71.261 24.977 L 71.261 21.646 L 76.925 21.646 C 77.815 21.635 78.673 21.982 79.306 22.608 C 79.938 23.234 80.294 24.088 80.293 24.978 L 80.293 32.939 L 76.328 32.939 L 76.328 29.785 C 75.643 31.819 73.438 33.175 70.754 33.175 C 65.418 33.175 60.5 29.667 60.5 22.619 Z M 82.468 11.5 L 87.029 11.5 L 87.029 15.864 L 82.469 15.864 L 82.469 11.5 Z M 82.648 32.936 L 82.648 18.076 L 86.821 18.076 L 86.821 32.936 L 82.647 32.936 Z M 103.365 27.896 C 102.768 31.11 99.907 33.174 96.211 33.174 C 91.113 33.174 88.49 29.224 88.49 25.479 C 88.49 21.734 90.815 17.842 95.912 17.842 C 101.01 17.842 103.335 21.705 103.335 25.095 C 103.335 25.655 103.305 26.098 103.275 26.422 L 92.485 26.422 C 92.783 28.516 94.125 29.813 96.211 29.813 C 97.88 29.813 98.983 29.164 99.311 27.896 Z M 92.544 23.827 L 99.191 23.827 C 99.072 22.177 97.909 20.849 95.912 20.849 C 94.034 20.849 92.902 21.763 92.544 23.827 Z M 114.663 21.495 L 112.189 21.495 C 110.132 21.495 109.208 23.175 109.208 25.711 L 109.208 32.936 L 105.035 32.936 L 105.035 18.076 L 109.208 18.076 L 109.208 20.552 C 110.013 18.694 111.146 18.075 112.964 18.075 L 114.663 18.075 Z M 114.722 25.505 C 114.722 20.875 117.405 17.839 121.757 17.839 C 123.635 17.839 125.096 18.546 125.931 19.637 L 125.931 11.617 L 130.074 11.617 L 130.074 32.936 L 125.931 32.936 L 125.931 31.315 C 125.096 32.465 123.546 33.172 121.817 33.172 C 117.405 33.172 114.722 30.135 114.722 25.506 Z M 126.169 25.505 C 126.169 22.999 124.709 21.348 122.503 21.348 C 120.297 21.348 118.836 22.998 118.836 25.505 C 118.836 28.011 120.297 29.633 122.503 29.633 C 124.709 29.633 126.169 27.982 126.169 25.505 Z M 44.932 22.5 C 44.932 25.455 44.352 28.38 43.224 31.11 C 42.098 33.839 40.446 36.319 38.362 38.41 C 36.281 40.498 33.808 42.155 31.085 43.287 C 28.364 44.418 25.447 45 22.5 45 L 22.5 22.5 L 44.93 22.5 Z M 6.57 38.41 C 2.361 34.185 -0.002 28.464 0 22.5 C 0 16.533 2.363 10.81 6.57 6.59 C 10.771 2.373 16.478 0.002 22.43 0 C 28.38 0 34.085 2.37 38.292 6.59 L 22.431 22.5 Z" fill="transparent" height="45px" id="jc8cRiL4c" width="130.07400012207032px"><path d="M 0 11.118 C 0 4.071 5.067 0.563 10.582 0.563 C 16.097 0.563 19.585 3.6 19.852 8.023 L 15.412 8.023 C 15.083 5.811 13.235 4.366 10.552 4.366 C 7.005 4.366 4.262 6.843 4.262 11.118 C 4.262 15.394 7.005 18.078 10.462 18.078 C 13.056 18.078 15.62 16.22 15.62 14.303 C 15.62 13.802 15.292 13.477 14.785 13.477 L 10.761 13.477 L 10.761 10.146 L 16.425 10.146 C 17.315 10.135 18.173 10.482 18.806 11.108 C 19.438 11.734 19.794 12.588 19.793 13.478 L 19.793 21.439 L 15.828 21.439 L 15.828 18.285 C 15.143 20.319 12.938 21.675 10.254 21.675 C 4.918 21.675 0 18.167 0 11.119 Z M 21.968 0 L 26.529 0 L 26.529 4.364 L 21.969 4.364 L 21.969 0 Z M 22.148 21.436 L 22.148 6.576 L 26.321 6.576 L 26.321 21.436 L 22.147 21.436 Z M 42.865 16.396 C 42.268 19.61 39.407 21.674 35.711 21.674 C 30.613 21.674 27.99 17.724 27.99 13.979 C 27.99 10.234 30.315 6.342 35.412 6.342 C 40.51 6.342 42.835 10.205 42.835 13.595 C 42.835 14.155 42.805 14.598 42.775 14.922 L 31.985 14.922 C 32.283 17.016 33.625 18.313 35.711 18.313 C 37.38 18.313 38.483 17.664 38.811 16.396 Z M 32.044 12.327 L 38.691 12.327 C 38.572 10.677 37.409 9.349 35.412 9.349 C 33.534 9.349 32.402 10.263 32.044 12.327 Z M 54.163 9.995 L 51.689 9.995 C 49.632 9.995 48.708 11.675 48.708 14.211 L 48.708 21.436 L 44.535 21.436 L 44.535 6.576 L 48.708 6.576 L 48.708 9.052 C 49.513 7.194 50.646 6.575 52.464 6.575 L 54.163 6.575 Z M 54.222 14.005 C 54.222 9.375 56.905 6.339 61.257 6.339 C 63.135 6.339 64.596 7.046 65.431 8.137 L 65.431 0.117 L 69.574 0.117 L 69.574 21.436 L 65.431 21.436 L 65.431 19.815 C 64.596 20.965 63.046 21.672 61.317 21.672 C 56.905 21.672 54.222 18.635 54.222 14.006 Z M 65.669 14.005 C 65.669 11.499 64.209 9.848 62.003 9.848 C 59.797 9.848 58.336 11.498 58.336 14.005 C 58.336 16.511 59.797 18.133 62.003 18.133 C 64.209 18.133 65.669 16.482 65.669 14.005 Z" fill="rgb(34, 34, 34)" height="21.67500002336502px" id="GsCWlry0t" transform="translate(60.5 11.5)" width="69.57400012207032px"/><path d="M 22.432 0 C 22.432 2.955 21.852 5.88 20.724 8.61 C 19.598 11.339 17.946 13.819 15.862 15.91 C 13.781 17.998 11.308 19.655 8.585 20.787 C 5.864 21.918 2.947 22.5 0 22.5 L 0 0 L 22.43 0 Z" fill="rgb(59, 76, 255)" height="22.5px" id="s1UzGfaC9" transform="translate(22.5 22.5)" width="22.43199999999979px"/><path d="M 6.57 38.41 C 2.361 34.185 -0.002 28.464 0 22.5 C 0 16.533 2.363 10.81 6.57 6.59 C 10.771 2.373 16.478 0.002 22.43 0 C 28.38 0 34.085 2.37 38.292 6.59 L 22.431 22.5 Z" fill="rgb(59, 76, 255)" height="38.41px" id="WrlFnrcj0" width="38.29200073579659px"/></g></svg>)
Every day, enterprise brands leave sales on the table, flood the wrong channels, or overcommit inventory to underperforming programs. Forecasts are off. Demand signals are missed. Returns data isn’t factored in. And while teams try to reallocate manually, aging stock grows and margin erodes.
01
02
03
SKU-Level AI-powered Forecasting
Demand forecasting is the foundation for all downstream logic of the entire planning process. It sets the tone for a sophisticated, forward-looking product and powers all downstream modules by enabling SKU- and channel-level personalization.
TOOLTIP
Predict demand at the SKU × channel × week level using AI-driven models — enabling smarter procurement, allocation, and pricing
Intelligent Supply Routing
Convert insights into operational decisions, bridge forecasting and execution, transforming predictions into concrete logistics and inventory actions. Directly drive ROI by minimizing overstock and enable velocity-based channel decisions, making its value instantly clear: put inventory where it will sell
TOOLTIP
Recommend routing for inventory across channels using rules based on margin, aging, and demand forecasts — reduce idle stock and maximize ROI
Forecast Accuracy Insights
Build transparency and trust in the AI-powered model, helping you validate performance, align assumptions, and track improvements
TOOLTIP
Measure how predicted demand compares to actuals to improve planning precision and build trust in the model
How does Gierd optimize inventory allocation across marketplaces?
Gierd’s Supply & Demand Core connects demand forecasting, pricing, and allocation into a closed-loop optimization system. It continuously predicts sell-through by SKU, channel, and timeframe — and auto-routes supply based on margin, aging stock, and program performance. Instead of static rules or guesswork, Gierd dynamically aligns inventory with where it earns most.
Can Gierd prevent us from overcommitting to low-margin or high-return SKUs?
Gierd’s Supply & Demand Core connects demand forecasting, pricing, and allocation into a closed-loop optimization system. It continuously predicts sell-through by SKU, channel, and timeframe — and auto-routes supply based on margin, aging stock, and program performance. Instead of static rules or guesswork, Gierd dynamically aligns inventory with where it earns most.
What types of data does Gierd use to make supply and demand decisions?
Gierd ingests and normalizes real-time data across all major marketplaces — including sales velocity, listing changes, promotions, returns, cancellations, and fulfillment performance. It integrates that with AI-driven forecasts and promotion models, enabling smarter decisions across supply routing, stock prioritization, and pricing impact. All actions are tracked in a unified audit trail.
Can Gierd help detect when forecasts are wrong — and course-correct?
Absolutely. Gierd’s Forecast Accuracy Engine tracks deltas between projected and actual demand at SKU, channel, and program level. When discrepancies emerge, it automatically adjusts future predictions and flags contributing factors (e.g., delayed promotions, listing suppression, misaligned supply). This allows teams to fix forecast errors before they cascade into aging inventory or missed sales.
What if we’re already using an ERP or WMS — how does Gierd fit in?
Gierd isn’t a replacement — it’s an intelligent layer on top. Unlike ERPs or WMS tools, which manage fulfillment and static planning, Gierd actively orchestrates allocation based on real-time commercial signals. It plugs into your existing stack via API, extracts primary data from marketplaces, and enhances it with orchestration logic. Your core systems stay intact — but get smarter.
Stop reacting to demand.
Start commanding it — across every channel, every SKU, in real time.
