LLM Autopilot Guard.

The problem

Running AI workloads locally on enterprise devices is hard because hardware is wildly heterogeneous — different CPUs, GPUs, NPUs, thermals, and battery behavior — and the same policy behaves differently across device classes. Under token surges, latency spikes and devices can overheat or drain their batteries, degrading the very productivity the AI was meant to boost.

For the operators governing these fleets, the pain concentrates in a trust gap: they need a clear, auditable "why" behind each autonomous tuning change. Setting thresholds that balance quality, latency, heat, and battery across diverse hardware is genuinely uncertain, and teams struggle to prove value without strong adaptive-versus-baseline evidence.

The solution

LLM AutoPilot Guard is an enterprise platform for governing local or on-device AI workloads so they don't overheat hardware, drain batteries, or hurt productivity. It acts as an autonomic governor layer above local runtimes, dynamically controlling model behavior from live battery, thermal, CPU, and memory signals while balancing latency, quality, energy, and thermal safety together.

Every profile or knob change is explainable and visible in a decision timeline and dashboard, so IT and product teams can trust and audit it. A built-in adaptive-versus-baseline experiment runner produces measurable KPIs, and a conservative value-estimation feature translates measured deltas into modeled business ranges — always with explicit disclaimers separating measured evidence from assumptions.

How it works

Live telemetry feeds an LLM governance agent — Llama 3.2 3B Instruct, quantized and running locally via Ollama — which recommends a runtime profile (performance / balanced / efficiency / survival) and knob overrides in strict JSON with a rationale and confidence score. A deterministic safety governor validates or overrides every recommendation before it is applied, enforcing hard thermal and battery limits so the final decision never violates policy.

The design is runtime- and model-agnostic via a structured agent contract, allowing alternatives like Qwen2.5 3B or Phi-3.5. Prompt iteration added hard guardrails, enum-restricted schemas, a minimal-change rule to prevent profile thrash, and confidence-gated fallback. Targets include ≥98% JSON schema validity, 100% policy compliance after enforcement, and adaptive-mode reductions of ≥20% in time above thermal threshold and 10–15% in battery drain per hour — with the same local LLM class serving as both the governed runtime and the decision agent.

Who it's for

The platform is primarily B2B, sold to enterprise IT and endpoint platform teams, with OEM partners as a secondary channel and a B2B2C expansion path through device partners. The primary operator personas are IT admins, endpoint ops teams, and AI platform engineers who set policy, monitor fleet health, and analyze results; security and compliance stakeholders are key influencers who approve governance boundaries.

The downstream beneficiaries are employee end users — knowledge workers, field engineers, support agents — running local AI assistants who get better responsiveness, lower heat, and longer battery, often while on battery and moving between workloads.

Why it matters

The target segment — enterprise on-device AI runtime optimization and governance — is expected to grow at roughly 20–30% CAGR over three to five years, driven by AI PC adoption, privacy-first inference, and cost pressure to shift workloads from cloud to edge. Energy-awareness is becoming a procurement differentiator: solutions that prove better battery and thermal stability win trust.

Monetization is B2B subscription and enterprise licensing priced per managed endpoint, with premium analytics and integration services. Success is measured in reduced endpoint complaints about AI slowness and overheating, improved perceived stability under stress, and faster pilot-to-expansion decisions grounded in clear adaptive-versus-baseline evidence — differentiation built on sustained real-world experience under stress rather than peak benchmark speed.

At a glance

Project

LLM Autopilot Guard

Built by

Shruti Holla

One-liner

LLM Autopilot Guard is an enterprise system for governing local or on-device AI workloads so they do not overheat hardware, drain batteries, or hurt productivity.