Features — SentinelMesh

Core routing

Intelligent multi-model routing

A bandit algorithm, Q-learning, and a world model work together to pick the best model for every single request — balancing cost, quality, and latency in real time, and improving as it sees more traffic.

Cost-aware routing with a quality floor you set
Pin a specific model when you want deterministic behavior
Every decision logged with its full reasoning trace

router.route_with_metadata(prompt)

→ strategy: fast_cheap
→ model: deepseek-chat
→ cost_usd: 0.000031
→ confidence: 0.91

Agent deployment

Deploy an agent from a sentence

Describe what you want in plain language and the engine configures a real, working agent — name, model, system prompt, and settings — no flowchart required to get started.

Draft agents activate automatically after one successful test
Every run logged with the real model, cost, and latency used
Built-in tool use: search, HTTP calls, and any of the 12 live integrations

See how the prompt agent works

You type:

"Reply to refund emails — check policy first, escalate anything over $200."

Visual canvas

A real workflow canvas, when you want it

Open any agent's canvas to see and edit its steps as connected nodes — AI calls, integration actions, conditions, delays — with full manual control. Saving deploys a real, executable workflow, not a diagram.

Drag-and-drop nodes with real backend execution behind each one
Conditional branching and multi-step chains with real data passed between steps
Run it and see every step's real result inline

Trigger

→

AI Step

→

Integration

Model ownership

Progressive LLM independence

Every request through SentinelMesh becomes potential training data. Over time, knowledge distilled from external models transfers into models you own outright — reducing dependence on any one vendor's pricing.

Confidence-based routing — internal models only answer when ready
Export trained model weights at any time
One call deletes all your training data (GDPR Article 17)

Maturity stages:

Bootstrap → Learn → Route → Earn Independence → Full Control

And everything else

Built in, not bolted on

Semantic Caching

Matches queries by meaning, not exact strings, using embedding similarity — cache hits cost $0.00.

12 Live Integrations

Slack, GitHub, Postgres, Notion, Salesforce, and more — real API calls, not simulated responses.

Security & Guardrails

PII detection, content filtering, and encrypted data in transit and at rest, built into every request.

Real-Time Observability

Request-level tracing, per-model performance, and cost breakdowns in one dashboard.

14 Models Routed

GPT, Claude, Gemini, Mistral, DeepSeek, Cohere, Llama, and more, behind one API.

Memory & Context

Stateful conversations with configurable context windows across sessions.

PII Protection

Automatic detection and redaction of sensitive data before it reaches any model.

Multimodal AI

Vision and voice support for models that handle images and audio natively.

Self-Learning System

Routing quality improves continuously from real production traffic, not a static ruleset.

Cost Tracking

Real-time spend visibility down to the individual request and model.

Function Calling

Native tool-use support so agents can call your functions and APIs directly.

Streaming Responses

Token-by-token streaming for responsive, real-time user experiences.

The alternative

SentinelMesh vs. building it yourself

Capability	SentinelMesh	Building it yourself
Time to production	Minutes, with one API key	Months of infrastructure work
Cost optimization	Automatic, ~40% typical reduction	Manual tuning, limited savings
Model management	14 models, automatic routing	Manual switching, vendor lock-in
Custom training	Automated pipeline included	Built from scratch, high upfront cost
Observability	Real-time dashboards, built in	Build and maintain your own stack
Integrations	12 live connectors, ready to use	Build and maintain every integration
Maintenance	Handled for you	Ongoing engineering time required

The complete AI infrastructure platform