Replenit
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AI Decision Engine for Commerce

It orchestrates workflows, skills, and memory across every upstream and downstream signal, making commerce decisions beyond human capability.

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REPLENITAI DECISION ENGINE
  • Upstream · signals in
  • Downstream · actions out
  • Workflows · orchestrated
  • Memory · context retained
  • Skills · specialized

The decisioning layer

Why do you need an AI Decision Engine?

Every operation runs on millions of decisions a day, most of them micro, all of them time-sensitive. The engine reasons over each one in context and turns that endless stream into measurable business outcomes.

DECISIONS PROCESSED · 8,200/SEC

12,480,000

LIVE

R4R5C7R11C12R14R2R9RPL-820U2REPLENITAI DECISION ENGINEPCB REV C

BUSINESS OUTCOME

Revenue uplift

+$2.4M

+18.2%

With an AI Decision Engine, you can

  • Turn fragmented data into decisions

    Scattered signals across your stack become one scored, executable next move.

  • Decide the next best action

    For every customer, account, product, service, or workflow, one at a time, at scale.

  • Prioritize the goals that matter

    Revenue, retention, margin, engagement, service quality, risk, or efficiency.

  • Choose the right workflow automatically

    The engine picks the path that fits the context instead of a fixed rule.

  • Personalize every dimension

    Timing, channel, content, offer, recommendation, or escalation, per entity.

  • Connect to your existing systems

    It layers over your current stack. Nothing gets ripped out or replaced.

  • Build owned workflows end to end

    From signal detection to execution and feedback, the flow is yours.

  • Learn from every outcome

    Results feed back in, so each decision sharpens the next.

An AI Decision Engine is the reasoning layer between business context and business execution.

It doesn't just automate tasks. It decides what should happen next.

Build end to end

Build decision workflows end to end

Connect upstream context, compose workflows over modular skills and shared memory, and activate downstream, all orchestrated by one AI Decision Engine at the center of your stack.

REPLENIT

AI Decision Engine

Signals in

Upstream

Connect every source, event, and system so the engine sees full context before it decides.

Automated

Workflows

Assemble multi-step flows the engine runs on its own, sensing, deciding, and acting across systems with no manual handoffs.

Domain-specific

Skills

Industry skills you add for domain expertise: the specialized judgment to decide correctly in your field.

Context retained

Memory

Persistent per-entity memory every decision reads and updates, so the engine compounds learning.

Actions out

Downstream

Push decisions into the systems and channels that execute them across your operation.

Built on the Replenit Decision Engine

Meet Maestro

The end-to-end AI CRM manager for retailers. Maestro puts the Decision Engine to work, owning the full customer lifecycle, deciding the next best action for every customer, and executing across every channel, autonomously.

  • One operator

    Owns the lifecycle

    Onboarding, retention, replenishment, and winback, run end to end by a single AI manager.

  • Per customer

    Decides 1:1

    A scored next best action for every customer, recalculated continuously as context changes.

  • Every channel

    Executes end to end

    Launches across email, SMS, push, and on-site, with no manual handoffs between tools or teams.

  • Always improving

    Learns and compounds

    Remembers every outcome per customer and gets sharper with each decision it makes.

See all case studies
Beauty & Cosmetics

+235%

Post-Purchase Revenue

L'Occitane Elevates Post-Purchase Revenue by 235%

Upstream
Inhouse ERP
Downstream
Insider
Read the L'Occitane case study

What makes it different

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.

01Mixture of Experts

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.

  • Better model selection
  • Higher accuracy
  • Lower compute
  • Faster outputs

GATING NETWORK

task · next best action

GATING

pick model

  • Reasoning LLM

    transformer

  • Gemma model

    generative

  • Gradient boosting

    tabular ML

  • Embedding + rank

    retrieval

  • Fine-tuned SLM

    distilled

02Owned & fine-tuned models

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.

  • Controlled outputs
  • Predictable performance
  • Lower dependency
  • Better unit economics

Replenit stack · owned models

proprietary

owned

Proprietary decision models

reasoning · scoring

google gemma

owned

Gemma-based models

generation

self-hosted

owned

Fine-tuned LLMs

content · copy

owned

owned

Embedding & ranking

retrieval

Not an LLM wrapper

External general-purpose API

✕ no dependency

$0.011

COST / DECISION

03Swarm engine · our moat

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.

  • Massively parallel
  • Lower cost per task
  • No serial bottleneck
  • Engineering moat

SWARM ENGINE

PARALLEL AGENTS

256

deployed at once

COST / TASK

$0.006

vs. serial agents

04TPU-accelerated processing

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.

  • Lower latency
  • High-volume
  • Efficient AI workloads
  • Timely decisions

TPU SYSTOLIC ARRAY

THROUGHPUT

1,480,000

decisions / sec

LATENCY

11 ms

p99 per decision

05Persistent memory & enrichment

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.

  • Continuous context
  • Self-enriching
  • Fewer blind spots
  • Compounding learning
enrich

PAST

NOW

NEXT

06Isolated vs. connected

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.

  • Cross-stack
  • Outcome-driven
  • Not single-tool
  • One system of record

OTHER ENGINES

one tool

micro-decisions

one tool

micro-decisions

one tool

micro-decisions

✕ no business outcome

REPLENIT

ENGINE

connected

Business outcome

revenue · retention

Get started

Let's talk about what you'll build with the AI Decision Engine

Book a demo and we'll map it to the stack you already run. Or discover Maestro, Replenit's AI CRM manager for retailers, powered end to end by the AI Decision Engine.

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