MCP-Native Optimization

Your AI Agent's
Optimization Copilot

Sematryx gives your AI agents a powerful tool to offload optimization problems. It analyzes each problem, selects and configures the right solver, explains why, and learns from every solve. Your agents save tokens, stay focused, and get clear explanations they can reason about and present to users.

No black boxes. No guesswork. No sidetracks.

100 free solves/month
$0.01–$0.05 per solve
No subscriptions
Connect Your Agent →Get API Key Free

Works with

Claude Desktop
Cline
Python SDK
REST API
NewPatent Pending

Optimize in Plain English—No Code Required

Describe your optimization problem in natural language. Our AI agent guides you through problem formulation, collects parameters, and executes optimization—all without writing a single line of code.

Zero technical barrier—works for anyone with an optimization problem
AI agent asks clarifying questions and validates inputs in real-time
Dynamic domain extension—handles novel problems automatically
Perfect for marketing managers, operations teams, and domain experts
Try Conversational OptimizationSee How It Works
Conversational Optimization
U
You
"I want to optimize my marketing budget across Google, Facebook, and LinkedIn to maximize ROI"
AI Agent
"I understand you want to optimize your marketing budget. What is your total marketing budget?"
U
You
"$50,000"
Optimization Complete
Optimal Allocation:
Google: $28,500 (57%)
Facebook: $15,000 (30%)
LinkedIn: $6,500 (13%)
Total ROI: 242% • Expected Revenue: $171,000
No code needed

Agent Use Cases

What agents solve with Sematryx

When an AI agent hits a combinatorial or continuous optimization problem, it calls Sematryx. Here's what that looks like in practice.

ML / Training

Hyperparameter Tuning

AI agents use Sematryx to find optimal learning rates, batch sizes, and regularization — without hand-coding grid search or random sampling.

lr=0.00312, dropout=0.18 → 2.3% better validation accuracy
Finance / Operations

Resource Allocation

AI agents use Sematryx to distribute budgets, staff, inventory, or compute across competing objectives with hard constraints.

Budget allocation across 8 channels → 23% higher ROI within cap
Ops / Logistics

Scheduling & Routing

AI agents use Sematryx to build conflict-free schedules and minimize routing cost across multi-variable constraint systems.

Staff schedule across 40 shifts → 18% fewer overtime hours
Infrastructure

Model & System Configuration

AI agents use Sematryx to tune infrastructure configs, model serving parameters, and pipeline settings for throughput or cost.

Replica count + batch size → 40% cost reduction at same latency
AI-Native

Your Agents' Optimization Backend

AI agents shouldn't struggle with complex optimization math. Sematryx works as a tool your agents can call—send a problem, get back an optimized solution with explanations the agent can reason about and present to users.

  • ✓Claude, GPT, Cursor invoke Sematryx via MCP protocol
  • ✓Results include natural language rationale, not just numbers
  • ✓Offload compute-heavy optimization to hosted infrastructure
Learn about MCP integration →
MCPAgent → Sematryx → Response
Agent Request
"Optimize portfolio for 8% return, max 30% single position"
↓
Sematryx Response
allocation: [0.35, 0.28, 0.22, 0.15]
"Balanced risk-adjusted return while satisfying the 30% max position constraint..."
↓
Agent to User
"I've optimized your portfolio. Here's the recommended allocation and why..."

Under the Hood

Sematryx IntelligencePatent Pending

Three core capabilities that make Sematryx different from traditional optimization tools.

Agentic Core

Uses Meta-Policy Learning to dynamically select and coordinate multiple optimization solvers based on problem topology and landscape characteristics.

Meta-Strategy SelectionMulti-Agent CoordinationDynamic Constraint Handling

Interpretable Layer

Delivers transparency via a dedicated Explainability Engine that generates audit trails, decision rationales, and visual diagnostics.

Decision RationaleFull Audit TrailsVisual Diagnostics

Adaptive Memory

Leverages Vector Memory (Qdrant) and Knowledge Graphs (Neo4j) to recall past optimizations and improve continuously.

Vector Context RetrievalKnowledge Graph MappingTransfer Learning

One API Call

Define your problem, set constraints, get an optimized solution. No infrastructure to manage.

Full Explainability

Every decision documented and traceable for audits and regulatory compliance.

Compliance Built-In

Domain libraries pre-configured for finance, healthcare, and other regulated industries.

Gets Smarter

Continuous learning means better results over time, automatically.

Natural Language Interface

Describe problems in plain English. No code or optimization expertise required. Patent pending.

Simple, Pay-Per-Solve Pricing

Start free with 100 solves/month. Pay only for what you use — no subscriptions, no commitments.

Free

$0forever

Get started with optimization basics

  • 100 solves/month
  • Public learning pool
  • Community support
  • No credit card required
Get Started
Most Popular

Pay-as-you-go

$0.01– $0.05/solve

Pay only for what you use

  • Unlimited solves
  • Price scales with complexity
  • Private Learning Store
  • Priority support
  • Credit packs save ~50%
Buy Credits

Enterprise

Custom

For large organizations

  • Volume discounts
  • Dedicated support & SLA
  • SSO & audit logs
  • Custom integrations
Contact Sales

Cost Per Solve

ComplexityDimensionsMax EvaluationsCost
Small≤ 10≤ 1,000$0.01
Medium≤ 50≤ 5,000$0.03
Large≤ 100≤ 10,000$0.05

Save ~50% with Credit Packs

5,000 solves for $75 (just $0.015/solve). Prepaid credits never expire.

Buy Credit Pack

Questions? Check out our pricing FAQ or contact us.

📬

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