The Inference Control Layer for Enterprise AI · Patent Pending

Control how AI
executes across
your organization.

MEMStorage sits between enterprise applications and AI models, determining when trusted knowledge can be reused, when deterministic logic applies, when lightweight validation is sufficient, when fresh inference is required, and when human review is necessary.

Launch Enterprise Demo → Explore Architecture
memstorage · execution engine
Incoming AI request
"Summarize the renewal terms in the Hudson St. lease."
↓
MEMStorage Execution Engine
Confidence97%
FreshnessValid
PolicyApproved
RiskLow
✓Use Trusted State
✕Skip fresh model inference
2,340 tokens saved $0.018 saved 850ms faster
Illustrative execution decision trace.
Execution, Compared

How enterprise AI works today
vs. with MEMStorage.

Without a control layer
Enterprise AI Today
  • Every request triggers expensive inference.
  • Repeated questions are recomputed.
  • Sensitive data may unnecessarily leave enterprise boundaries.
  • Limited governance and auditability.
  • Costs, latency, and risk grow with usage.
With the inference control layer
Enterprise AI with MEMStorage
Fresh Inference invokes a full model workflow. Lightweight Validation may use a smaller, constrained, or deterministic verification method.
  • Execute AI only when necessary.
  • Reuse trusted enterprise knowledge.
  • Enforce governance before inference.
  • Produce an auditable execution record.
  • Designed to reduce unnecessary cost, latency, and operational risk.

MEMStorage is the policy-driven decision layer that governs AI execution before infrastructure, orchestration, or foundation models are invoked.

Today’s AI asks, “Which model should execute?”
Enterprise AI should ask, “Should AI execute at all?”
Why This Matters

The Hidden Cost of AI.

AI is becoming critical infrastructure. Every unnecessary inference consumes compute before any business value is created — and the load compounds as adoption grows.

Compute demand
Redundant model calls consume capacity that real workloads need.
Energy consumption
Every avoidable inference draws power that produced no new answer.
Infrastructure costs
Token and GPU spend scale with requests — not with value delivered.
Latency
Users wait on full model runs for answers the enterprise already holds.
Operational risk
Ungoverned execution expands the surface for errors and exposure.

MEMStorage helps enterprises reduce unnecessary inference by making execution an intelligent, governed decision before compute is consumed.

Smarter AI isn’t just about better models. It’s about executing intelligence only when it’s truly needed.

Built with enterprise AI signals
Accepted Member
Enterprise cloud infrastructure program
The Pitch
JPMorgan Chase / Deel Finalist
Selected startup showcase
Partner Ecosystem
Enterprise commerce AI infrastructure
Enterprise AI Architects
Architecture Feedback
Governance · Cost Control · AI Execution
Enterprise AI Execution Control

One control layer.
Every enterprise AI system.

Across financial services, healthcare, insurance, retail, manufacturing, and internal knowledge, MEMStorage governs AI execution the same way. Configure a workflow below and watch the execution decision replay.

configuration
Industry
Workflow
Risk levelMedium
LOWMEDIUMHIGHCRITICAL
Data sensitivity
Scenario inputs
Policy controls
Ask a question

Do not enter confidential, regulated, personal, financial, or health information. This public simulator uses example data only.

execution replay
Configure the workflow and run the replay to watch MEMStorage select an execution path, step by step, before any model is called.
Execution path changed
Execution Decision
—
Why this route?▾
Evidence & trusted sources▾
Execution avoided▾
Inference avoided
—
Model calls
—
Tokens avoided
—
Est. cost saved
—
Latency
—
Confidence
—
Data exposure
—
Audit ID
—
Illustrative values based on selected policies and a simulated workload. Actual impact depends on enterprise workloads and model pricing.
compare outcomes · A vs B
VariableOutcome AOutcome B
Before MEMStorage
  • Every request reaches an LLM
  • Policies are buried in prompts
  • Human escalation is inconsistent
  • Known answers are repeatedly regenerated
  • Sensitive data can reach external models
  • Decisions are difficult to explain
  • AI costs grow unpredictably
  • Agent actions can compound without control
After MEMStorage
  • Execution path selected before model invocation
  • Policies are explicit and configurable
  • Human escalation follows defined thresholds
  • Trusted knowledge is reused
  • Sensitive data follows controlled execution paths
  • Every decision creates an audit record
  • AI spend becomes measurable and controllable
  • Agent actions are governed before execution
Production Example: SHOPLINE

One platform.
Real deployments.

See how MEMStorage powers AI execution for ecommerce merchants.

MEMStorage × SHOPLINE

Merchant support, order operations, and refund workflows are governed by business policy. Trusted knowledge is reused, escalations are enforced, and every execution decision is logged for review.

policy console live execution audit trail operations dashboard
Open the SHOPLINE Console →
Product

AI execution needs a control plane.

Observability tells you what your AI did. MEMStorage decides what your AI should do before compute is consumed, with a record of every decision.

01 · Decisions
Execution Decision Engine
Every request receives an explicit execution decision before any model runs.
  • Execute
  • Reuse
  • Validate
  • Human Review
02 · Trust
Trusted State Registry
A system of record for the knowledge your AI relies on.
  • What AI used
  • When it was validated
  • Who approved it
  • Why it was trusted
03 · Policy
Policy Governance
Execution rules your organization controls, enforced before inference, not audited after.
  • Policy-aware routing
  • Confidence thresholds
  • Freshness controls
  • Human override
04 · Cost
Cost Intelligence Dashboard
See what every AI decision cost and what execution was avoided entirely.
  • Spend by execution path
  • Avoided inference tracking
  • Per-team attribution
  • Budget guardrails
AI Spend · Monthly
Without control$8,400
With MEMStorage$2,100
Avoided Inferences
1.2M
Requests resolved without a model call
Policy Compliance
99.8%
Decisions within execution policy

Spend and avoided-inference figures from the 2.1M-query SEC EDGAR commercial lease benchmark (~55% trusted-state reuse). Compliance figure illustrative of dashboard reporting. Results vary by workload, repetition rate, and deployment pattern. See the methodology →

Architecture

One layer above every model.
One step before compute.

MEMStorage is provider-agnostic infrastructure. Applications send requests to the control layer; models only run when the decision engine determines fresh inference is required.

Enterprise Apps
Applications AI Agents Workflows
↓
MEMStorage Inference Control Layer
Execution decided here, before any model runs
Execution Engine Policy Engine Confidence Scoring Validation Routing Trusted State Audit Trail
↓only when required
LLMs
Claude GPT Gemini Llama
↓
Enterprise Data
SAP Salesforce SharePoint Snowflake Databricks Internal APIs
Category

Beyond observability.
Before inference.

Observability
Tells you what happened after your AI ran.
Routing
Decides which model handles the request.
Inference Control
Decides whether AI should run at all.
LogsRoutes ModelsControls Execution
Helicone✓——
Portkey✓✓—
LangSmith✓——
MEMStorage✓✓✓

Comparison reflects primary product focus as publicly positioned; individual features vary by plan and release.

Benchmark

Cost reduction is the symptom.
Control is the infrastructure.

75%
Inference cost reduction
on benchmark workload
<1ms
Trusted execution
decision target
2.1M+
Benchmark requests
processed
40–70%
AI cost optimization
range by workload

Measured on the SEC EDGAR commercial lease benchmark with ~55% trusted-state reuse. Results vary by workload, repetition rate, and deployment pattern. See the methodology →

Enterprise Governance

Every AI decision becomes explainable.

Enterprise AI is moving from experiments into production, where every execution decision creates cost, security, and governance implications. Your teams should be able to answer:

Q1
Why did AI run?
Q2
What data was trusted?
Q3
What policy approved it?
Q4
Could execution have been avoided?

MEMStorage creates an auditable decision layer before inference, a system of record for AI execution.

Get Started

See your workload through
the control layer.

Run a benchmark on your own AI workload, or walk through the architecture with the founding team.

Book Demo → Investor Deck

We started by trying to reduce inference. We discovered enterprises need to control execution.