AI-Native Decision Automation, Powered by Solvi

An AI-native platform is built with machine learning, optimization, and agentic AI inside the planning engine from the start — not added to a legacy system afterward. On Solvoyo, the AI and the plan are the same system, so there are no handoffs between a separate "planning tool" and an "AI layer," and no data-preparation step before the AI can act. The practical result is faster time-to-value and AI that reasons across the entire decision rather than optimizing one silo at a time.

Solvi is an agentic AI planning agent, not a chatbot. A copilot 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 it runs on Solvoyo's concurrent model, every action reflects the latest enterprise-wide state, not a stale snapshot.

Solvoyo combines three layers of intelligence in 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. They operate on a single concurrent model, so prediction, optimization, and action stay aligned rather than living in separate tools.

Solvoyo applies machine learning models matched to each forecasting problem — gradient-boosted trees (LightGBM, XGBoost) on engineered demand features such as lags, rolling statistics, and calendar effects; deep learning models (Temporal Fusion Transformer, DeepAR) for complex patterns; and foundation models (TimeGPT, Moirai) for items with little or no history. Every model is benchmarked against a simple statistical baseline it must beat, so the method selected per SKU is the one proven most accurate — not the most complex.

For new products, Solvoyo builds the forecast from analog items and product attributes rather than 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 instead of being guessed.

Promotional demand is modeled separately from baseline using gradient-boosted trees that read discount depth, mechanic (BOGO, %-off), and display or feature placement, combined with a price-elasticity layer. Cross-product elasticity models then capture how a promotion on one item steals demand from substitutes (cannibalization) and lifts complements (halo) — so the output is net, cannibalization-adjusted category lift, not just the inflated lift on the promoted SKU.

Solvoyo detects out-of-stock and low-availability periods using availability flags, then unconstrains the suppressed demand — correcting or imputing 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. Stockout-corrected demand ensures the forecast reflects what customers wanted to buy, not what an empty shelf allowed.

Yes. Rather than a single-number point forecast, Solvoyo generates quantile (probabilistic) forecasts — P10, P50, and P90 — that describe the full range of likely demand. These quantiles feed directly into safety-stock and min/max calculations, so buffers are sized to the service level you target and to the actual demand uncertainty each item carries, rather than to a flat assumption applied uniformly across the assortment.

Slow-moving and intermittent items — sparse, lumpy demand with long gaps between sales — are forecast with specialized methods such as Croston, TSB, and SBA, alongside zero-inflated and quantile models built for intermittency. This avoids the over- and under-stocking that standard forecasting causes on long-tail SKUs, sizing reorder quantities to the true demand rate rather than smoothing sporadic sales into a misleading average.

Every Solvi recommendation shows its trigger, assumptions, trade-offs, and quantified impact — giving 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. Combined with tenant-isolated data and encryption in transit and at rest, your planning data is used only to serve your own plans, never to train public models or reach any other party.

Through role-based authorization. Every user — and every action Solvi takes — is scoped to a defined set of workflows, locations, categories, and decision types, so 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, and every action is logged with its author, timestamp, and rationale — a complete, auditable record of who changed what, when, and why.

Four practical differences. It is AI-native rather than retrofitted — the intelligence and the planning model are one system. It is concurrent rather than sequential — a change in any dimension instantly recomputes its impact everywhere else. It is agentic rather than advisory — Solvi closes the loop from insight to executed action instead of stopping at a chart or an answer. And it is end-to-end on one platform rather than a stitched-together suite — so the same AI reasons across demand, inventory, fulfillment, transportation, retail, and pricing in a single platform.