AI Agent Pipeline Intelligence
Every guardrail, classifier, and validator surrounding your core reasoning model, running in under 50ms instead of 150ms. No changes to your orchestration layer, your model partnerships, or your deployment topology.
Your AI agent pipeline runs a reasoning model in the center, surrounded by classification, validation, and compliance models that each steal 100–200ms from the latency budget. Six specialist LFMs run those surrounding tasks in under 50ms per stage. Drop them into your existing pipeline alongside your current reasoning model, TTS provider, and orchestration framework. See all five layers working together in the Enterprise AI Agent demo. New guardrail rules or classification categories adapt via LEAP in minutes.
7 specialist models
How It Works
One specialist model per guardrail stage,
returning latency headroom to your agent pipeline
Every Agent Action Validated in Under 50ms
Your AI agent issues refunds, resets passwords, modifies accounts. Between the reasoning model’s decision and the tool call’s execution, you need a validation layer. Cloud validators add 200–500ms. Keyword filters miss context. A specialist LFM intercepts every action and classifies it as allow, deny, or hold-for-approval in under 50ms. It distinguishes a routine password reset from a social engineering attack semantically, not by keyword. The validator runs at middleware speed inside your existing orchestration layer. No architecture changes. New threat patterns or policy rules adapt via LEAP same-day.
PII Screening That Runs in the Pipeline, Not as a Separate Pass
Every message in your agent pipeline passes through multiple models. If PII leaks past intake into reasoning, logging, or analytics, it compounds compliance risk downstream. Pattern-based screening catches formatted identifiers but misses colloquial variations and multi-language inputs. A specialist LFM screens PII semantically in under 50ms, directly between intake and your reasoning model. Catches spelled-out SSNs, obfuscated identifiers, and context-dependent patterns that regex cannot. One pipeline stage, no architecture changes. Adapt to new PII patterns via LEAP in minutes.
Output Compliance Before the Customer Sees It
Your reasoning model generates a response. Before it reaches the customer, compliance must check for brand violations, regulatory non-compliance, hallucinated commitments, and off-policy language. Post-hoc detection finds problems after the message was already delivered. Adding a cloud compliance layer means another 150–200ms round-trip per response. A specialist LFM checks every output in under 50ms pre-delivery: policy violations, off-brand language, and regulatory flags are caught before the response leaves your pipeline. New compliance rules for product launches, policy changes, or regulatory updates deploy via LEAP same-day.
Workflow Selection Before the Orchestrator Makes Its First Decision
In a multi-model agent architecture, the first classification sets the latency floor for everything downstream. Whether you call it intent routing, workflow selection, or procedure dispatch, the task is the same: understand what the customer needs and select the right handler. Cloud NLU adds 200ms before reasoning, context retrieval, or response generation even begin. A specialist LFM classifies intent in under 50ms with 95%+ accuracy across billing, technical, account, escalation, and custom workflows. New workflows for product launches, policy changes, or seasonal campaigns deploy via LEAP in minutes. The classifier keeps pace with your product, not the other way around.
Escalation Decisioning in Real Time
Your agent pipeline needs to decide mid-conversation: continue autonomous resolution, or escalate to a human? Batch analytics detect churn signals a day after the customer has already left. Post-call analysis finds frustration after the damage is done. A specialist LFM detects six signals in real time: churn risk, escalation urgency, upsell potential, satisfaction shifts, competitor mentions, and sentiment polarity, all in 25ms per message. The orchestrator can trigger escalation, adjust the agent’s tone, or flag a retention opportunity while the conversation is still active. Real-time signals mean real-time decisions.
Five Layers, One Pipeline, Under a Second
Individual guardrail models are useful. Seeing them compose into a complete pipeline is the proof point. The Enterprise AI Agent demo chains five specialist models in sequence: intent classification, PII screening, agent reasoning, pre-flight validation, and compliance filtering. Total pipeline latency under one second. Each layer streams results in real time so you can see exactly which model caught which risk. This is how the pieces fit together in a production agent architecture: every guardrail running at pipeline speed, every layer independently fine-tunable via LEAP.
Unified Audio for Voice Agent Pipelines
Voice agent pipelines add two more latency-sensitive stages: speech-to-text at the front and text-to-speech at the back. LFM2-Audio-1.5B handles both STT and TTS in a single model, reducing the number of inference calls in the voice path. Combined with a specialist 350M intent classifier, the voice layer adds minimal overhead to your existing pipeline. The audio model complements your current voice infrastructure, adding a unified STT+TTS option that runs alongside your existing orchestration and reasoning stack.
Try each model
All Demos
Agentic Pre-Flight
Validate AI agent tool calls for security risks before execution at 15ms
Every AI agent tool call validated at 15ms — faster than the tool call itself
Redaction Gateway
Detect and redact PII with semantic understanding — regex vs cloud vs LFM comparison
Regex misses 40% of PII. Cloud LLMs take 500ms. LFM catches everything in under 50ms
Compliance Filtering
Pre-delivery message compliance — block violations before they’re sent
Pre-delivery compliance — block violations before they’re sent, not 48 hours later
Intent Classification
Sub-20ms semantic routing for contact centers and chatbots
15ms semantic routing replaces regex (70% accuracy) and expensive cloud NLU
Customer Signal Detection
Real-time churn, upsell, and escalation signals from every customer touchpoint
25ms signal detection turns every support ticket into a retention, upsell, or routing decision
Enterprise AI Agent
5 models, 5 layers, <1 second — the full security stack for AI agent operations
5 specialist LFMs in sequence: <1s total, fraction of cloud cost, data never leaves your VPC
Text Classification
Sub-50ms semantic classification for gaming, AdTech, and content safety
Sub-50ms classification enables real-time content moderation that cloud LLMs can’t serve
Voice Support Agent
Full voice support pipeline: speak a question, get intent routed and answered in real time
One audio model replaces separate STT and TTS services. Combined with a 350M intent classifier, the full voice pipeline runs on a single GPU.
Ready to deploy in your environment?