AI Assist™ for the Enterprise

Private, accurate GenAI on every endpoint; without cloud risk, runaway cost, or stalled adoption. AI Assist gives you a private, accurate AI copilot on every endpoint and the operating model to scale it safely. It turns AI into measurable productivity, without unpredictable usage fees or cloud exposure. When you are ready to move from pilots to outcomes, schedule the workshop and we will put it into production on a timeline you can govern.

The Executive Take

Most enterprises are stuck between “shadow AI” risk and expensive, low‑ROI pilots. AI Assist turns AI from an experiment into an operating capability. It puts a trustworthy, on‑device assistant in every employee’s workflow and backs it with a data “refinery” and an adoption engine so value shows up fast, at predictable cost, and within your governance perimeter.

What is AI Assist?

AI Assist is an enterprise bundle that delivers local, offline‑capable GenAI on endpoints (via AirgapAI™), refines your knowledge into precise AI optimized structures for higher accuracy (via Blockify®), and includes the deployment, training, and support to make usage stick at scale.

Why It Matters Now

  • Competitiveness: Knowledge workers are already using AI tools; you can either govern and scale it or chase it. On‑device deployment eliminates the data‑egress compromise. Total security, total peace of mind.
  • Cost discipline: Usage‑based cloud LLM fees are volatile. Local AI creates a fixed, controllable cost curve that aligns to budgets and device refresh cycles.
  • Risk and sovereignty: Keeping AI on endpoints closes the cloud leakage vector, supports air‑gapped and SCIF operations, and simplifies governance and audit.
  • Accuracy and trust: Blockify, a patented data refinery that transforms documents into structured “Critical Question/Trusted Answer” units reduces hallucinations and creates consistent answers employees can rely on.

What Makes It Different

  • Runs where the work happens: AI Assist lives on the endpoint, using the latest advances in modern silicon already on the device, and works without an internet connection.
  • A better knowledge substrate: Your documents are distilled into high‑signal knowledge units (IdeaBlocks) so retrieval is precise, compute is lower, and answers are consistent.
  • Built for the enterprise motion: More than 900 out of the box persona‑based workflows, curated datasets, standard desktop management packaging, global support, and measurable adoption metrics make it easy to scale.

Outcomes by Role

CEO: Speed, Trust, and Lift Without Brand Risk

  • Accelerate time‑to‑proposal, time‑to‑resolution, and time‑to‑insight across sales, service, engineering, and operations.
  • Reduce reputational exposure from AI “leaks” and hallucinations; answers are traceable with citations and approvals.
  • Strengthen employer brand: employees get a safe, role‑specific copilot they can actually use, even offline.

CFO: Predictable Economics and Fast Payback

  • Replace variable AI token consumption with a fixed per‑seat model that leverages sunk investment in endpoint laptops and desktops.
  • Lower rework and escalation costs via more accurate answers and approved, persona‑scoped datasets.
  • Tangible payback levers:
    • Hard savings: reduced third‑party GenAI spend; fewer contractor hours for document summarization and RFP/RFI responses; network egress cost avoidance.
    • Soft to hard conversion: time saved per workflow (drafting, Q&A, troubleshooting) tied to adoption and wage rates.
    • Risk cost avoidance: fewer disclosure incidents; smoother audits; less legal review churn due to inconsistent content.

CIO: Deploy Once, Scale Safely

  • Endpoint‑first architecture: Package with Microsoft Intune/MECM or bake into standard images; no external registration or call‑home.
  • Security by design: Operates within your device controls (disk encryption, application allow‑listing), identity, and network segmentation. No external data dependency required.
  • Governable knowledge: Persona‑scoped access, AI and data guardrails, and dashboards on adoption, accuracy, and content health. Integrates with your ITSM and support model.

Business Case Snapshot

  • Cost model: Fixed per‑user license (MSRP $2,140 per user, one time payment) for the assistant; optional server component for large‑scale knowledge refinement and centralization. No usage‑metered fees for queries.
  • Typical value drivers:
    • Knowledge tasks compressed by minutes per action (policy lookup, runbook troubleshooting, proposal drafting).
    • Accuracy uplifts that halve back‑and‑forth cycles with HR, Legal, and Compliance.
    • Field and plant uptime gains via offline access to SOPs and policy Q&A.
  • Payback framing:
    • Productivity: (minutes saved per workflow × workflows per user per week × adoption rate × fully loaded wage) vs. license cost.
    • Cost avoidance: reduction in external LLM and contractor spend; fewer support tickets; reduced network/egress.
    • Risk adjustment: probability‑weighted savings from preventing data egress and mis‑disclosure.

Risk, Compliance, and Sovereignty

  • Data stays local: AI Inference and retrieval occur on the device; nothing needs to reach an external service to function.
  • Persona boundaries: Access to datasets is provisioned by role, and answers include citations back to approved sources for auditability.
  • Deployment options align to security posture: Operates fully offline for high‑security and air‑gapped sites; server component can be hosted on‑prem or in your cloud under your controls.
  • Governance you can run: Approval workflows for new datasets, quarterly content reviews for accuracy and freshness, and usage analytics that surface gaps and wins.

What Employees Actually Get

  • A native AI assistant that sits on their desktop, with role‑specific Quick Start Workflows that solve real tasks:sales cover letters, incident postmortems, policy Q&A, troubleshooting steps, and more.
  • Ability to switch among approved datasets during a chat: product catalog, HR policies, runbooks, etc; with citations and controls.
  • Multi‑AI-Agent collaboration when needed (for example, sales + legal + product) to converge on compliant, customer‑ready content.

How We Ensure Adoption

  • A structured enablement program delivered to teams in four short sessions: orientation, job‑specific workflows, advanced techniques, and a roadmap co‑created with the business.
  • Micro‑content and weekly office hours to keep usage high and questions answered.
  • Dashboards that track active use, time‑to‑answer, dataset health, and impact stories for executive review.

Implementation in Weeks, Not Quarters

  1. Strategy and guardrails: Use‑case inventory, role mapping, hardware right‑sizing, and security decisions.
  2. Packaging and curation: Build deployable role‑based content packs; set software update channels that match your environment (file share, image, or cloud if permitted).
  3. Pilot and UAT: Representative users per persona, performance and security validation, and sign‑off.
  4. Phased rollout: Wave‑based deployment with checkpoints and rollback thresholds.
  5. Run and improve: Global support aligned to your ITSM, plus ongoing content governance and adoption reviews.

What’s Under the Hood

  • AirgapAI Endpoint assistant: Local chat and retrieval that automatically uses available CPU/GPU/NPU; lightweight install with broad compatibility.
  • Blockify Data refinery: An optional component that converts your documents into atomic “Critical Question/Trusted Answer” units, deduplicates across sources, and improves precision 78X (7,800%) while reducing compute.
  • Enterprise Scale Enablement: End to end services to make deployment, education, adoption, and support entirely seamless

KPIs Your SteerCo Can Run

  • Adoption and engagement by persona and function
  • Accuracy and citation health of answers
  • Time‑to‑answer and time‑to‑first‑draft
  • Reduction in AI‑related tickets and escalations
  • Content freshness and coverage across top workflows
  • Avoided cloud AI consumption and contracting spend

Board-Level Questions, Answered

  • How do we avoid data leakage? By keeping AI inference on the device and secured within your network. Also controlling data by provisioning datasets by role, ideally with no external call‑home is required to function (provided by AirgapAI).
  • How do we manage accuracy and bias? Knowledge is refined into trusted Q&A units with citations; updates follow a governed approval and refresh process.
  • How do we control costs? Fixed per‑seat licensing, no token metering, and compute efficiency from distilled inputs; leverages existing endpoint laptops and desktops.
  • What about our most sensitive sites? Operates fully offline and supports air‑gapped and SCIF environments without feature degradation.
  • Will IT be buried in support? The solution is bundled with end to end runbooks, L1/L2/L3 support, and ITSM integration, plus analytics to spot and fix friction quickly.

Early Places to Win

  • HR service delivery and onboarding
  • Sales proposals, RFP/RFI response, and call prep
  • Engineering runbooks and incident documentation
  • Field operations and safety SOPs where connectivity is limited
  • Legal and compliance clause analysis and policy reasoning
  • Public sector and healthcare where auditability and offline matter

Commercials in Plain English

  • Per‑user software license for the endpoint AI assistant (AirgapAI)
  • Patented Data Ingestion and Optimization Technology (Blockify)
  • Global support coverage included, with enterprise SLAs

Your Next Step

  1. Executive workshop (90 minutes): align outcomes, risk posture, pilot personas, and success metrics
  2. Fast pilot (4–6 weeks): two to three personas, curated datasets, and the first two enablement sessions
  3. Scale plan: expand datasets, decide on centralization, and schedule advanced enablement