High Voltage AI. Wired & running.

Consultants advise and leave. We design, build, deploy, AND run it — same team owns the whole lifecycle. You get a roadmap, working code, and ongoing operations under a single monthly engagement.

Engagements are month-to-month with a 30-day notice. Add a solution, pause one, or scale the team up for a launch sprint. You're never locked into a multi-year contract.

Yes. Source code, prompts, model weights you've fine-tuned, integration logic — all yours, with full source access from day one. We don't lock you in.

Two options. We can pass through actual model costs at our negotiated rates (typical), or roll them into a fixed monthly with a usage cap. Most clients prefer pass-through for transparency.

A board-ready roadmap document: prioritized use cases, ROI per case, phased timeline (0–3 / 3–6 / 6–12 months), risk + readiness scoring, vendor recommendations, and a "first 90 days" action plan. Plus a 90-minute readout with your leadership.

Light. We need ~4–6 hours total from each key stakeholder across the sprint (interviews, workshops). The bulk of the work happens on our side — research, analysis, modeling.

We make specific recommendations — but we have zero financial relationships with model vendors. If OpenAI is best for your use case we'll say so. If open-source on your own infra is better, we'll say that. No referral fees, no kickbacks.

Yes — and we'll say so explicitly. A "do not build" list is often more valuable than the build list. Some AI use cases sound great in a deck but don't survive a real ROI model.

Absolutely. Many clients run the strategy sprint with us, take the roadmap to their internal team, and execute. We're happy to be the strategy partner without the build engagement.

High-volume, rule-based work with clear inputs and outputs: invoice processing, expense approvals, support-ticket triage, customer onboarding, reconciliations. We target processes burning 10+ staff-hours weekly first.

Every automation has explicit confidence thresholds. Below them, the workflow pauses and routes to a human reviewer with all the context. Audit logs capture every decision, model output, and override.

Usually they shift into review, exception-handling, and higher-value work. We help write the new role definitions during rollout. The few times this is genuinely workforce-reducing, we say so up front and you decide.

Almost always, yes. We connect via API where available, browser automation where not. We've integrated with mainframes from the 1980s and SaaS apps from last quarter. The harder it is, the more value there usually is.

3–6 weeks from scoping to running in production. The first automation in a new business is usually the slowest — subsequent ones build on the same infrastructure and ship faster.

BI tools show you what happened. Predictive analytics tells you what's about to happen — churn risk by customer, demand spikes by SKU, revenue likely to slip. We add the predictive layer on top of your existing dashboards, not replace them.

12 months of clean data is comfortable. 6 months is workable. Less than that, we use industry priors and improve as your data accumulates. We tell you up front what confidence to expect.

Depends on the metric and the noise floor. Most clients see 85–95% accuracy on stable metrics (demand, recurring revenue), 70–80% on noisier ones (churn, new customer LTV). We always report confidence intervals, not just point estimates.

Yes. The natural-language query layer is the whole point — your team types "what was our top-margin product last quarter by region?" and gets a chart back. No SQL, no Looker training.

Everyone's data is messy. We start with a 1-week data audit, build a thin cleaning layer in your warehouse (or on top of it), and document the assumptions. We don't need pristine data to start — we just need to know what we're working with.

CX AI runs across the whole customer lifecycle, not just a chat window — smarter search, personalized homepage, abandoned-cart recovery, churn-risk outreach. The chat agent in Solution 07 is one slice of it; this is the full system.

Disclosed where required (GDPR, CCPA), invisible everywhere else. The goal is that the experience feels good — relevant search, useful follow-ups — not creepy. We err on the side of less personalization, not more.

Web (your site), email, SMS, WhatsApp, and in-app messaging — wired into your CRM, helpdesk, and commerce platform. The same customer profile drives behavior across all of them.

Smart search and lifecycle email: 2–3 weeks. Unified customer profiles and churn detection: 4–6 weeks. Full system: typically 8–10 weeks. We ship in phases so value starts compounding early.

The trick is embedding it inside what they already use — not making them open a new tab. Notes appear in their meeting tool, search lives inside Slack, draft replies appear in Gmail. Adoption usually hits 70%+ in the first month when it's invisible.

Yes — both, with proper SSO and admin controls. Plus Slack, Notion, Zoom, Teams, HubSpot, Salesforce, and most other common business tools.

Meeting bot disclosure is enabled by default. Transcripts stay in your environment, never train shared models, and access is role-permissioned. Sensitive meetings can be excluded by calendar tag or attendee list.

Off-the-shelf Copilot / Gemini are great for individuals; we go further. We custom-train on YOUR documents, plug into your specific stack, and run the operational side — model upgrades, policy enforcement, prompt curation. Often we use Copilot or Gemini under the hood.

Granularly. Role-based access, per-folder permissions, sensitive-content classifiers that prevent specific document types from being fed to models. Mirrors your existing access-control structure.

Detects sentiment in real time. Escalation triggers (anger, mention of cancellation, legal language) hand the conversation to a human with full context. The AI is also explicitly trained to apologize, slow down, and offer to escalate when it's not making progress.

Yes — sub-second response time, voice quality at the level of consumer products you've used in the last six months. Choose from several voices or have us clone a brand voice. Tone, pacing, and "let me check on that" filler all configurable.

Mid-conversation, the agent sends a Lightning invoice link via SMS or in-chat. Customer taps, confirms in their wallet, payment lands in 1–2 seconds, and the agent confirms the order in real time. End-to-end without the customer leaving the conversation.

It says so — explicitly, never inventing answers — and offers to escalate or take a message. Every "I don't know" gets logged so we can fill the knowledge gap, and the agent learns to handle it next time.

No — modern AI voice is convincingly human, and we tune brand-specific tone (warmer, brisker, more technical) per use case. Customers regularly thank "Sarah" or "Mike" by name. That said: we always disclose it's AI when asked.

Google Drive, Notion, SharePoint, Confluence, Dropbox, internal databases (Postgres, MySQL, MongoDB), CRMs (Salesforce, HubSpot), helpdesks (Zendesk, Intercom), and most other business systems. Anything with an API or a stable export.

The assistant respects your existing permission structure. A sales rep asking a question can only see information they'd see in Drive directly; the same question from a VP returns more. Permissions are checked at retrieval time, every query.

Yes — and usually should. Sales-bot is trained on quotes, pipelines, and product specs. Support-bot is trained on policies and troubleshooting. HR-bot is trained on benefits and PTO. Same platform, different personas and data scopes.

Your environment, always. AWS, GCP, Azure, or on-prem. We deploy into a dedicated tenant you control. We're never the data custodian and never train shared models on your content.

Three things: (1) permissioned retrieval that respects your existing access controls — ChatGPT doesn't know who's allowed to see what. (2) Connections to live systems, not just static docs. (3) Operational discipline — monitoring, evals, prompt versioning. ChatGPT-with-docs is a demo; this is production.