Single-tenant network & compute isolation, customer-managed keys, and configurable retention options.
Batching, scheduling, KV-cache optimization, quantization, and concurrency to improve utilization.
Policies, guardrails, firewalling, and traceability to support compliance and production operations.
A Secure LLM Enclave provides single-tenant isolation and audit-ready observability in your VPC. The Inference Control Plane adds runtime controls that improve cost and latency predictability — plus policy, safety, and governance enforcement at the point of inference.
Traction Layer AI makes self-hosted inference practical at scale by combining a secure enclave deployment model, a runtime control plane for cost/latency, and integrated security & governance — all inside your VPC.
Higher GPU utilization, smarter batching/caching, and routing reduce infra spend and wasted tokens.
Pre/post-inference protections and deterministic policy enforcement — within your perimeter.
SLO-oriented scheduling, multi-model concurrency, and safe fallback patterns stabilize latency.
Model traceability, interaction logs, and integration points for SIEM/SOC workflows and compliance.
Four integrated layers provide production controls from enclave isolation to routing, policy, and tuning.
We’re built for regulated environments that need strong controls, auditability, and predictable performance within a dedicated cloud/VPC footprint.
Software companies shipping AI features and agentic workflows on open models — delivering reliable customer experiences across any vertical.
Predictable unit economics, tenant isolation patterns, routing by cost/quality/latency, guardrails at scale, and observability for customer-facing SLAs.
For providers, payers, life sciences, and healthcare-adjacent insurance organizations deploying AI for clinical documentation, prior auth, claims, member support, research workflows, and internal copilots.
Protected data controls, deterministic guardrails, traceability for decisions, output validation, incident-driven rule growth, and safe deployment lifecycle patterns.
For banking, capital markets, insurance, mortgage, and fintech teams deploying AI for customer support, underwriting, document intelligence, risk, and internal copilots.
Data residency & isolation, policy enforcement, PII/financial data controls, audit trails, SIEM/SOC integration, predictable latency for high-volume workflows.
Deploying open-source and open-weight LLMs/SLMs for plant operations, quality, maintenance, supply chain, and knowledge — including copilots for technicians and frontline teams.
Site-level isolation, policy-gated routing for cost/latency, IP & OT-data protections, and audit-ready traceability for regulated and safety-critical workflows.
Quick answers to common questions buyers ask when evaluating a control plane for self-hosted models.
Traction Layer AI complements and operationalizes inference engines by adding a secure enclave model, routing, policy controls, auditability, and runtime optimization capabilities as a unified control plane.
It’s designed “open-source first” but can route to commercial LLM APIs when policy allows — enabling a mix of models based on cost, latency, and risk requirements.
Yes — the architecture supports single-tenant deployment patterns and customer-managed isolation, keys, and retention options.
Deployment platforms focus on serving/workflows; GPU clouds provide capacity. Traction Layer AI provides runtime controls for predictable inference economics, security, governance, and auditability.
Share your workloads and requirements (latency targets, compliance needs, model mix). We’ll map how the Secure LLM Enclave + Inference Control Plane fits your architecture.
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