Supervity

• HQ: San Jose, CA —Digitamize Inc. (D.B.A. Supervity)
• Enterprise AI Employee Creation Platform which creates and deploys self-improving AI Employees for governed company operations
• Deployed in 13 countries globally and rapidly expanding – customers include Coca-Cola, Genentech, Daikin, NEC, Department of Defense, WVDEP, Keurig Dr. Pepper, Cushman & Wakefield, and many others
• Recognized by Gartner, HFS Research, Shared Service Global Network Technology Of The Year
• Patent-Pending in AI Automation Learning

Not all AI is built the same. These nine things separate a Supervity AI Employee from a chatbot, a copilot, an RPA bot, or a build-your-own agent platform — and are why it reaches production where they stall.

1. AI Employees that own the outcome

Supervity AI Employees execute governed business work end-to-end — across systems, documents, and human reviewers — and are accountable for the result, delivering measurable touchless rates, lower cost-to-process, and faster cycle times.

2. Works with any application and any data type

AI Employees operate inside the systems you already run — modern SaaS, legacy applications, and custom portals — through APIs where they exist and, where they don't, directly through the user interface using computer vision. Structured or unstructured, modern or legacy: no connectors, no replatforming, and no system of record left behind.

3. Multi-modal — strong OCR to read anything, plus a Voice AI Employee

AI Employees ingest and act on any input: PDFs, emails, spreadsheets, scanned images, and handwriting through strong OCR — and can operate by voice as a Voice AI Employee for inbound and outbound calls. When a case needs judgment, it surfaces to the Auto Workbench with full context, where a human reviewer decides and the AI Employee executes.

4. An Intelligent Context Engine that thinks like your business

Most AI can generate output or chain a few steps. Supervity AI Employees operate with the context of the business unit itself — its policies, approvals, exceptions, evidence requirements, system state, and prior outcomes. That context is proprietary and compounding: it grows with every transaction and every resolved exception — something competitors can't replicate, because they can't access your operational history.

5. A Run-Time Cost Optimization Engine that keeps AI economical at scale

Supervity is model-agnostic by design — AI Employees combine Supervity-trained SLMs with frontier models and run on any LLM (GPT, Claude, Gemini, Llama, or your own sovereign model), on any cloud. The Run-Time Cost Optimization Engine routes each step to the most efficient model, operator, or deterministic path automatically, and the trained SLMs run routine work at 40–60% lower inference cost than frontier-only — so you pay only for the intelligence each task actually needs. Switching or adding models requires zero redeployment, so the economics hold at production volume, not just in demos.

6. Governance built for production — not bolted on

RBAC, Auto Policies, Human-in-Command review, and a full audit trail are core to every AI Employee. Business units govern exceptions through the Auto Manager Console while IT and security maintain enterprise controls — which is why Supervity AI Employees run real, consequential work in production instead of stalling in pilots.

7. Self-improving AI Employees that get better over time

Supervity AI Employees are not static automation. Every exception handled, every correction made, and every approval recorded feeds back into the Intelligent Context Engine, so straight-through-processing rates rise over time — without manual retraining or redeployment. A static tool hands the exception back and forgets; an AI Employee learns the resolution for next time.

8. Every Auto App makes the next one faster

AI Employees deploy on one shared foundation, so each business unit you activate inherits the context, policies, and learnings of the last. Your second Auto App deploys faster than your first, your third faster still — and cross-functional intelligence compounds. Competitors restart from zero with every new use case; Supervity gets cheaper and faster to expand over time.

9. ROI Assurance — the outcome, guaranteed

At Auto Enterprise, Supervity commits to outcomes contractually: AI-first transformation milestones and a Total-Cost-of-Operations improvement scoped during deployment. Miss a milestone and the Forward-Deployed Engineering engagement continues at no additional cost until it's met. No other vendor in the AI agent market makes this commitment.

Best customers include Mid-market to large enterprises (500–50,000+ employees) running high-volume, exception-heavy, policy-driven operations in Shared Services, Customer Operations, Sales, IT, and Domain Specific Operations. Key areas include Shared Services: Finance, Procurement, Shared HR, IT — across fragmented systems including ERPs, CRMs, portals, inboxes, and documents. Best fit where manual workflows are measurable and painful, governance and auditability matter, and leadership is committed to AI-first operations rather than AI-assisted workflows.

• CFO / Controller / Finance Operations
• CIO / Enterprise Architecture
• Chief Transformation Officer
• Head of GBS / Shared Services
• Chief Procurement Officer / Procurement Operations
• Chief AI Officer / AI Governance Leader

Supervity AI Employees are governed multi-agent systems that run a whole business function — they read the documents, operate across your systems, apply your policies, and clear the exceptions. The point is that they learn: every exception a person resolves trains the system, so more of the work runs touchless every month and less of it ever reaches a human. You own the policies and the approvals; the AI Employee runs the operation end-to-end, with a complete audit trail.