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.

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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.
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.

Financial services, healthcare, insurance, retail, manufacturing, internal knowledge — MEMStorage governs AI execution the same way across all of them. Configure a workflow below and watch the execution decision replay.

configuration
Industry
Workflow
Risk levelMedium
LOWMEDIUMHIGHCRITICAL
Data sensitivity
Policy controls
Ask a question
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 Decision
—
Inference avoided
—
Est. cost saved
—
Latency
—
Confidence
—
Policy applied
—
Audit ID
—
Before MEMStorage
  • Every request reaches an LLM
  • Policies buried inside prompts
  • Little auditability
  • Repeated inference for known answers
  • Higher, unpredictable AI costs
  • Inconsistent escalation
After MEMStorage
  • Execution path selected first
  • Explicit enterprise policies
  • Full audit trail on every decision
  • Trusted knowledge reused
  • Lower AI spend
  • Consistent governance
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 governed by business policy — trusted knowledge reused, escalations enforced, and every execution decision 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.