AI-Native Supply Chain Decision Automation

An AI-native supply chain planning platform has machine learning, optimization, and agentic AI inside the planning engine from the start. Solvoyo has run optimization and machine learning in its engine since 2005. On Solvoyo, the AI and the plan are the same system, so no handoff sits between a separate planning tool and an AI layer, and no data-preparation step comes before the AI can act. The practical result is faster time-to-value and AI that reasons across the whole decision, in every function at once.

Solvi is an agentic AI planning agent. A copilot or chatbot answers questions about your data; an agent acts on it. Solvi works inside live planning workflows with full awareness of upstream and downstream impacts. It detects an issue, diagnoses why it happened, quantifies what it will cost, recommends the specific decision, and, where you have authorized it, executes the response. Because Solvi runs on Solvoyo's concurrent model, every action reflects the latest enterprise-wide state.

Solvoyo uses three layers of AI on one platform. Predictive AI senses demand shifts, disruptions, and emerging exceptions early. Optimization evaluates trade-offs and computes the best feasible plan within real constraints. Generative and agentic AI explain recommendations in natural language and drive prioritized actions through to execution. All three run on a single concurrent model, so prediction, optimization, and action stay aligned.

Solvoyo matches the machine learning model to the forecasting problem. Gradient-boosted trees (LightGBM, XGBoost) work on engineered demand features such as lags, rolling statistics, and calendar effects. Deep learning models (Temporal Fusion Transformer, DeepAR) handle complex patterns. Foundation models (TimeGPT, Moirai) cover items with little or no history. Every model is benchmarked against a simple statistical baseline that it must beat, so the method selected for each SKU is the one proven most accurate.

Solvoyo builds the forecast for a new product from analog items and product attributes, without waiting for sales to accumulate. Attribute-based machine learning regression and like-item modeling predict the launch curve from comparable products, and foundation models can generate a zero-shot forecast with no history at all. As real sales arrive, the forecast self-corrects, so new and short-lifecycle items are planned accurately from launch.

Solvoyo models promotional demand separately from the baseline. Gradient-boosted trees read discount depth, mechanic (BOGO, %-off), and display or feature placement, and a price-elasticity layer sits on top of them. Cross-product elasticity models then capture how a promotion on one item steals demand from substitutes (cannibalization) and lifts complements (halo). The output is net, cannibalization-adjusted category lift. The lift on the promoted SKU alone would be inflated.

Solvoyo detects out-of-stock and low-availability periods from availability flags, then unconstrains the suppressed demand. It corrects or imputes true demand for those periods before any model trains on the history. Without this step, a stockout reads as a genuine drop in demand and teaches the model to under-order the same item again. The corrected history shows what customers wanted to buy, including the sales an empty shelf prevented.

Yes. Solvoyo generates quantile (probabilistic) forecasts at P10, P50, and P90 that describe the full range of likely demand. The quantiles feed directly into safety-stock and min/max calculations, so the buffer for each item is sized to the service level you target and to the demand uncertainty that item carries. P10 marks the low end of likely demand and P90 the high end.

Solvoyo forecasts slow-moving and intermittent items, which have sparse, lumpy demand with long gaps between sales, with specialized methods such as Croston, TSB, and SBA. Zero-inflated and quantile models built for intermittency run alongside them. These methods size reorder quantities to the true demand rate of each long-tail SKU. Standard forecasting causes over- and under-stocking on these items by smoothing sporadic sales into a misleading average.

Every Solvi recommendation shows its trigger, assumptions, trade-offs, and quantified impact, which gives planners, finance, and auditors a clear decision trail. Human-in-the-loop control applies at every step, and automation expands only as confidence grows. Client data is isolated within Solvoyo's own cloud infrastructure, and no consumer personal data is used to drive planning decisions.

Yes. Client data stays inside Solvoyo's own AWS cloud environment and is not shared outside it. Solvoyo's generative AI runs on Amazon Bedrock, which processes prompts and planning data entirely within the AWS environment. Your data is never sent to third-party model providers and is never used to train foundation models. Data is tenant-isolated and encrypted in transit and at rest, so your planning data serves your own plans only and never trains public models or reaches any other party.

Solvoyo uses role-based authorization. Every user, and every action Solvi takes, is scoped to a defined set of workflows, locations, categories, and decision types. Planners can view and act only within the workflows they are authorized for, and Solvi automates only the decisions you have explicitly cleared. The same authorization boundaries apply to manual overrides and automated actions alike. Every action is logged with its author, timestamp, and rationale, which makes the log a complete, auditable record of who changed what, when, and why.

Solvoyo's AI differs in four practical ways. It is AI-native: the intelligence and the planning model are one system, and optimization and machine learning have run inside the engine since 2005. It is concurrent: a change in any dimension instantly recomputes its impact everywhere else. It is agentic: Solvi closes the loop from insight to executed action. And it is unified: the same AI reasons across demand, inventory, fulfillment, transportation, retail, and pricing in a single platform.

No. Making your data AI-ready is part of what Solvoyo does. Automated data diagnostics profile your input data at onboarding and continuously afterward, and they flag gaps, inconsistencies, and anomalies before those can distort a plan. Machine learning fills missing data points and corrects unreliable values, whether your current process runs on an ERP or on spreadsheets. Solvoyo integrates with your ERP through web service APIs, cloud integration platforms, or direct database transfers. Data quality improves inside the platform and needs no separate prerequisite project, so companies at any level of digital maturity see value quickly and reach autonomous planning without a perfect data foundation.