Frequently asked questions

Expertise mining is the discipline of extracting the undocumented, role-based expertise that lives inside experienced employees and turning it into validated, machine-readable operating knowledge. It captures the judgment behind the work: the decisions, exceptions, trusted sources, handoffs, approvals, and risks, not just the official steps. Process mining mines logs; expertise mining mines people.

RAG and enterprise search retrieve knowledge that has already been written down, and they are good at that. The problem is that the most valuable expertise was never written down in the first place. Expertise mining extracts that hidden judgment and structures it, so retrieval has something reliable to work with.

Process mining reads system logs, which show what happened but not why. A log can tell you a manager approved a refund; it cannot tell you why she approved this one and rejected a similar one yesterday. That decision lives in the person, and expertise mining is how you capture it.

No. Agent builders give you a runtime and assume you already know what the agent should do. In real enterprise work that is almost never true: the company knows the goal but rarely has the decision logic, exceptions, or handoff rules written anywhere. Verti extracts that expertise first, so the agents you build are trustworthy on real cases, not just demos.

Yes. Mined expertise speeds up onboarding, lowers key-person risk, makes succession less vague, and makes process improvement more honest, all before any agent is involved. The expert should not be the company's hidden storage layer. Capturing the expertise is valuable on its own, and it is also what makes agents possible later.

A working AI agent stands on two legs: knowledge and execution. Almost the entire market builds execution and skips knowledge, so agents get only the documented ~20% of how work gets done and break on everything else. MIT's 2025 study found 95% of enterprise GenAI deployments produced no measurable profit impact. The missing leg is expertise.

Expertise Mining extracts the knowledge and judgment that lives in people. Company Expertise connects that knowledge to the company's real data, systems, and tools. Governed Agent Operations runs specialist agent teams safely with permissions, guardrails, and audit. The Continuous Learning System improves everything as work changes, with humans approving every material change. On top of the four sits Company GPT, the member workspace where your whole team uses them daily, never sold standalone. Pull any layer out and the chain breaks.

An Expertise Package is the validated, structured, machine-readable output of expertise mining. It includes the expertise definition, business outcome, scope, core and supporting operations, decision logic, trusted and unreliable sources, tool inventory, exceptions, risks, handoffs, output standards, open questions, evidence references, and an Agent Design Blueprint. It is operating knowledge for agents, not a transcript or a prose report.

It isn't. It is the same platform doing the same work. Verti captures how your best people actually decide, then puts governed agents on top of it. We have built this across insurance, healthcare, retail, e-commerce, finance, maritime, pharma, and B2B SaaS. The industry changes; the platform does not.

Humans stay in control. Tool boundaries (read, draft, write, approval) are enforced by policy, not suggested by a prompt. The Continuous Learning System can detect issues and propose changes, but no material change ever ships without human approval: evidence capture, review, validation, approval, measurement, and rollback.

Yes. Verti is on-prem-capable: secure, governed, and enterprise-controlled. Agents work inside your existing enterprise systems within explicit permissions and guardrails, operations are versioned and auditable, and expertise packages carry evidence references, validation status, and version history.

Expertise is a version, not a monument. The platform learns from corrections, rejected actions, new exceptions, stuck onboarding, and tool failures, then proposes refreshes that a human approves before they go live. It does not change itself in secret, and it does not let the knowledge freeze.

Start with one process: a single role or decision where your operation depends too much on one or two people who just know. Mining one real expertise well shows results quickly and de-risks a broader rollout. You do not start with the whole company; you start with one real piece of work, then do it again. The entry is safe by design: a free PoC on your real cases, and payment starts only after you see measured results.

We deliver a new live process roughly every month, each with a measured before and after. In recent deployments a risk-analysis team went from eight people to three, renewals dropped from six minutes to one, and screening a hundred job applicants went from eight hours to thirty minutes. Efficiency gains typically run 13% to 93% per process, and they compound toward as much as 4x the output from the same team and headcount over roughly 8–12 months.

We start from real cases, not abstract job descriptions. Your experts walk through how actual work moved (the decisions, the exceptions, the sources they trust, and why) and then review and validate the structured output. The goal is not to copy a person, but to find the reusable expertise inside their work and make it safe to use.