The AI Decision Engine for commerce.
The reasoning layer above your stack.
Replenit orchestrates workflows, skills, and memory across every upstream and downstream signal to make commerce decisions beyond human capability, on owned models and the infrastructure you control.
The decisioning layer
Why commerce needs an AI Decision Engine
Every operation runs on millions of decisions a day. Rules and dashboards cannot keep up, and each tool optimizes in isolation.
Millions of decisions a day
Every operation runs on millions of decisions a day, most of them micro, all of them time-sensitive. No team can reason over that volume by hand.
Rules and flows hit a ceiling
Fixed rules and static flows cap out at your team's bandwidth. They fire on a calendar, not on context, and they never adapt on their own.
Fragmented signals, no outcome
Signals are scattered across your stack and each tool optimizes in isolation, so micro-decisions never add up to a business result.
An AI Decision Engine is the reasoning layer between business context and business execution. It does not just automate tasks. It decides what should happen next.
Capabilities
What the engine actually does
It turns scattered signals into scored, executable decisions, one entity at a time, at scale.
Product-to-product intelligence
The engine reasons over category relationships, complementary products, and consumption patterns to power substitute and cross-sell: the right product for the right customer, with the reasoned fit or gap attached.
Enriched customer and product memory
Persistent, per-entity memory that every decision reads and updates. When context is missing, the engine synthesizes it to close the gap, so intelligence compounds over time.
Decisions mapped to your outcomes
Marketers set the goal (revenue, retention, margin, AOV, or churn) and the engine picks the workflow that fits each context, driving every decision toward that outcome, per entity, at scale.
Architecture
Where it sits in your stack
It layers over your current stack. Nothing gets ripped out or replaced.
- CDP: Segment, Tealium, mParticle
- Warehouse: Snowflake, Databricks, BigQuery
- Commerce & ERP: Shopify, custom
- CEP & ESP: Braze, Klaviyo, Iterable
- CDXP: Bloomreach, Adobe, Salesforce
- Custom: API & webhooks
An engine built differently
Most decision engines are thin wrappers bolted onto a single tool. Replenit is engineered end to end, the right model for every decision, on owned infrastructure, with memory that compounds.
The right intelligence for every task
Each part of a decision is routed to the expert or model best suited to solve it. Only the capabilities that decision needs are activated, nothing wasted.
Control over quality and economics
Proprietary decision models, Gemma-based models, and self-hosted fine-tuned LLMs, not a wrapper around someone else's API. Replenit owns reasoning, consistency, and cost per decision.
Proprietary decision models
reasoning · scoring
Gemma-based models
generation
Fine-tuned LLMs
content · copy
Embedding & ranking
retrieval
Agents deployed in parallel
Our swarm engine spins up many specialised agents simultaneously instead of running them one after another, collapsing wall-clock time and slashing cost per task. Concurrency is engineered into the core, and it's the moat that's hardest to copy.
Decisioning at commerce speed
Google TPU infrastructure processes large workflow datasets across many models and skills at once, keeping decisions and personalised content relevant in fast-moving markets.
Decisions with continuity
Every decision builds on enriched memory: what happened, what's happening, what's already been done, and what comes next. When context is missing, agents synthesise it to close the gap.
Most engines are islands
Other decision engines live inside a single tool, spinning micro-decisions that never add up to a result. Replenit connects across your stack and drives every decision toward one business outcome.
Technical FAQ
The questions engineers actually ask
No. Replenit is a decision layer that reads from your data stack and writes decisions into the tools you already run. It layers over your current stack, so nothing gets ripped out or replaced.
No. Replenit runs proprietary decision models, Gemma-based models, and self-hosted fine-tuned LLMs on owned infrastructure. Because the models are owned rather than rented, Replenit controls reasoning quality, consistency, and cost per decision.
A Mixture-of-Experts design routes each decision to only the model it needs, a swarm engine runs specialized agents in parallel instead of serially, and Google TPU infrastructure processes large workloads at once. In practice that is roughly 1.48M decisions per second at 11 ms p99, with a low cost per task.
Yes. Every decision carries its rationale and is explainable, auditable, and traceable, with human override controls and risk classification built in. Replenit is designed to be EU AI Act ready.
Products, customers, and orders, pushed through the API. Around 12 to 24 months of order history is recommended for the best timing models. The engine enriches products and customers automatically, and that enrichment stays internal to your tenant.
Put the engine to work on your stack.
See how Replenit reasons over your data and drives every decision toward the outcomes you set, with the security and cost control your team requires.
Built for enterprise security and compliance