Give your team AI that compounds. Not tools that sit idle.

A process analysis engagement starts with a diagnostic session to identify where AI creates the most value. Scope and pricing depend on which processes are targeted. SylvaQ offers both project-based engagements and ongoing advisory retainers. Book a 30-minute call for a scoped estimate.

Most businesses start AI at the task level — drafting emails, summarizing documents. That's the lowest rung of the value ladder, and it's also what any employee can do alone with a free tool. The processes that deliver the highest ROI are at the process level: where AI handles entire workflows autonomously and humans supervise exceptions, not every step. High-impact candidates include customer query classification and first-response handling (where AI can resolve 60–70% of volume without human intervention), contract and document review with exception flagging, demand forecasting and inventory decisions, recruitment pipeline qualification, and operational monitoring with automated escalation. SylvaQ starts every engagement with a process audit — mapping where time actually goes, what decisions are made repeatedly, and which processes carry the highest cost per hour of human involvement.

"Ready" is the wrong frame — AI readiness is a spectrum, not a threshold you cross. The five dimensions that actually determine readiness are: (1) strategy clarity — does your organization know why it's adopting AI and which outcomes it's targeting; (2) data readiness — is your information accessible and structured enough for AI to use; (3) technology infrastructure — do you have environments to deploy, not just experiment; (4) people and culture — do team members understand AI well enough to adapt their processes; and (5) governance — do you have a policy covering which tools are permitted, what data can be used, and who is accountable for AI outputs. Governance is consistently the most underestimated gap. SylvaQ's AI Readiness Scorecard maps your organization across all five dimensions and identifies which gaps block adoption versus which you can work around while moving forward.

Team-in-a-Box (TIAB) is a meta layer that sits on top of most commercially available LLMs — ChatGPT, Claude, Gemini — and transforms them from generic chat tools into a persistent AI operating system tailored to a specific leader and organization. It requires a subscription to the underlying LLM of your choice; TIAB is the structured layer that makes that LLM context-aware and operationally useful over time. TIAB can run cloud-based, but performs best when installed locally as a file-based system: all organizational context lives as structured files on your own machine, with only active queries leaving the device. TIAB is not ITAR-compliant — any system communicating with a commercial LLM API transmits data outside the device — but local installation maximizes security within that constraint. Designed for executives managing commitments, contacts, decisions, and priorities across multiple active projects.

Most clients see measurable time savings within the first 30 days on the first 1–2 processes targeted. Full team adoption across multiple workflows typically takes 60–90 days. SylvaQ focuses on quick wins first — visible ROI before expanding scope.