TurboTax Live, the Virtual Expert Platform, and the seasonal labor model behind them — the growth engine and (directly) Consumer Group's world.
Sources: research/SOURCE_INDEX.md — [S24][S25][S2].
VEP is the connective platform that routes customers to the right expert (AI or human) at the right moment, and gives experts the tools/context to help. It powers TurboTax Live and QuickBooks Live and is central to Intuit's "AI-driven expert platform" strategy. (VEP was created within Consumer Group leadership — see the leadership dossier; onboarding new businesses like Credit Karma onto VEP is active work.)
Five capability layers, per Intuit's own descriptions:
1. Matching & routing — AI models do real-time matching of customer → expert, scoring on the customer's specific need vs. each expert's profile, credentials, current capacity, and past performance. This is the "matchmaker" core (the Connect team's domain).
2. Document intelligence — AI extracts, classifies, and auto-places data from customer tax documents into the correct form fields — the expert reviews instead of transcribing. (The consumer-facing "90% form automation" rides the same capability.)
3. Expert augmentation ("AI on the expert's shoulders") — during a live voice/chat session the platform feeds the expert real-time article recommendations, auto-complete suggestions, and response snippets; afterward it auto-generates call summaries (questions, actions, resolutions) and applies quality scoring / resolution-likelihood prediction. Experts also get a real-time summary of the customer's journey and prior touchpoints.
4. Virtual collaboration — screen sharing, co-browsing, document co-review; what makes a single-session Full Service prep possible.
5. Digital expertise layer — upstream of any human: AI predicts contact reasons, offers personalized self-help, resolves straightforward issues, and decides when to escalate to a human — the AI↔HI handoff logic.
The design thesis: built as configurable, reusable platform capabilities any Intuit business can adopt (hence CK onboarding = configuring these five layers for credit/money journeys, not building new tech). Underneath: ML + NLP + knowledge engineering — and post-GenOS, these capabilities increasingly ride the GenOS stack (file 11 §c2). Intuit's framing: "augmented intelligence that sits on the shoulders of its experts" — automate the admin, keep the human on judgment and reassurance.
Lineage & direction (don't get this backwards): VEP originated on the TurboTax/QuickBooks side — Notarainni created it with the assisted-tax strategy; it has only ever run tax + bookkeeping journeys. Credit Karma is being onboarded ONTO it (the active migration program): Salesforce = the member/case data foundation underneath, VEP = the experience on top, and CK's credit/money member journeys are net-new design work on existing rails.
Verified: Intuit runs a lakehouse + data-mesh architecture (domain-owned data products; Spark Streaming/Flink real-time; Databricks + Redshift access). VEP's stated capabilities imply its data needs: matching requires live expert profile/capacity/performance data; the expert's "customer journey summary" implies an event/timeline store; summaries/QA imply captured transcripts. GenOS's data cognition layer is the LLM↔data grounding bridge.
Hypothesized data classes (Analysis — validate inside): (1) customer/member context (identity, product state, journey events); (2) case/CRM + entitlements from Salesforce (which SKU → what service owed); (3) expert workforce data (credentials, schedules, real-time availability, quality history — the matching engine's supply side); (4) interaction data (transcripts, summaries → QA + training); (5) document/return data (the expert's work surface; §7216's permitted core); (6) knowledge content (articles, tax-knowledge engine, snippets).
The flywheel (Analysis): events → journey timeline → matching consumes low-latency features from both sides → sessions generate interaction data → retrains matching/quality models. Every session improves the next match — a data moat competitors can't rent.
Why "Salesforce first" is technically forced (Analysis): the matcher can't route a CK member without identity, entitlement, and case history — exactly the data being consolidated into Intuit's Salesforce instance. The migration isn't adjacent to VEP onboarding; it builds VEP's supply of member-context data. And the compliance boundary runs through here: CK data + tax data mingling in one routing/context layer demands consent-aware data scoping per member and per direction (§7216 outbound / GLBA-FCRA inbound — file 06).
Validate inside: homegrown vs. vendor workforce management; formal feature store or not; real-time vs. batch journey context; where exactly the consent gates sit.
The assisted model runs on a flexible, largely remote, seasonal workforce:
- Who: credentialed experts — CPA, EA, or practicing attorney — with an active PTIN and typically 2+ seasons / 30+ returns of experience.
- How they work: 100% remote, minimum ~20 hrs/week across 3+ days, scheduled around operating hours on a first-come basis during the season.
- Pay: hourly (rate varies by state) plus a performance-based season-end bonus.
- Training: up to ~17 days (paid, remote, self-led + live).
This lets Intuit scale a human workforce up for the ~10-week peak and down afterward — matching the seasonal demand curve (file 03) without carrying year-round headcount.
Intuit's direction is AI-augmented experts: AI handles routine lookups/drafting and triage so human experts focus on judgment and reassurance — raising expert productivity while keeping the human accountable. This is the practical shape of "expert-in-the-loop AI" and the bridge between files 08 and 11.
The sharp version: agents will do the expert's work; humans keep the expert's accountability — and the ratio quietly inverts.