Screening Time Eliminated
AI Hiring Automation.
The Persona
Sarah: Head of Talent
200-person SaaS company · 30 open roles this quarter
Before
After
Annual Saving
Detailed Advisor Workflows
AI Wealth Advisory
Operations Agents.
The Persona
James: Head of Wealth Advisory
Mid-size RIA · 12 advisors · 800+ HNW accounts · $1.2B AUM
Agent Tracks
Detailed Use Cases
Output Quality
Review Model
The wealth module below applies the expanded playbook directly to the site experience. Instead of one high-level compliance example, it now shows the three operational agent tracks advisors actually run across client prep, post-call execution, and KYC handling, with long example outputs for each scenario.
"I have a call with Richard. Prepare me for that."
Preparation time falls from hours to minutes, but the bigger gain is quality: fewer blind openings, fewer repeated questions, and better continuity across long-running client relationships.
Scans email conversations, CRM records, prior meeting transcripts, planning notes, and portfolio context.
Weights recent interactions more heavily while still surfacing older items that explain relationship history or unresolved concerns.
Flags contradictions such as stale CRM goals, missing follow-ups, or risk tolerance changes implied by recent conversations.
Recommends the specific collateral, talking points, and confirmation questions the advisor should bring into the meeting.
Meeting brief with talking points, personal context, household details, and sentiment cues.
Open commitments, missed follow-ups, and opportunity flags ranked by urgency.
Recommended collateral such as retirement scenarios, concentration analyses, fee comparisons, or estate planning summaries.
Data quality notes that separate confirmed facts from inferred signals and stale fields.
Source mapping for every major insight across CRM, email, transcripts, and portfolio systems.
Confidence labels that keep advisor prep factual instead of speculative.
Entity resolution across spouses, households, trusts, and related contacts.
Contradiction detection when transcript intent and CRM fields no longer match.
An advisor has six client meetings between 8:30 AM and 1:00 PM and asks SarvaX to prepare every call before the first meeting starts.
Calendar schedule with six meetings across retirement, estate, concentration, review, referral, and service-recovery topics.
CRM records containing household profile, current AUM, planning goals, open opportunities, and last-contact history.
Recent email threads with pending questions, promised follow-ups, and sentiment changes.
Prior transcripts that capture objections, risk comments, family updates, and advisor commitments.
Portfolio and document systems with holdings, cash balances, proposal decks, planning reports, and signed forms.
Meeting-specific opening language for each client.
Collateral checklist for each session, including scenario reports, fee comparisons, estate summaries, and concentration analysis.
Pre-call confirmation list showing which data is stale, inferred, or still needs advisor validation.
Priority ranking so the advisor knows where relationship risk is highest before the day begins.
SarvaX produces six distinct briefs instead of a single generic agenda. Richard Cole is flagged as a retirement-income conversation, not a standard portfolio review, because his transcript shows an age-62 retirement question while CRM still says age 65.
Maya and Dev Patel are identified as a concentrated-stock meeting with household alignment risk. The system notes that Maya is focused on downside protection while Dev remains attached to the position, which means the advisor should frame the call around protection, concentration level, and tax budget rather than a blunt sell recommendation.
Eleanor Grant is treated as an estate-planning follow-up with sensitive family context. SarvaX surfaces the attorney-introduction gap, beneficiary concerns, and a recent family death so the advisor can enter the conversation with the right tone and sequence.
Owen Brooks is flagged for a cash-allocation conversation because a business-sale distribution left an unusually high cash balance. Email engagement suggests interest in municipal bond content, but the system marks that as a signal to explore rather than a recommendation to assume.
Lena Torres, a referred prospect, is classified as a liquidity-event discovery conversation. SarvaX recommends a consultative first meeting and warns against using the referrer's personal financial details as a comparison point.
Harold and June Mercer are marked as a service-recovery risk because a fee comparison was promised but does not appear in sent email or the document vault. The brief recommends acknowledging the miss at the start of the call and arriving with the comparison prepared.
The system favors the portfolio platform over older CRM notes when concentration percentages conflict.
Email click behavior is treated as soft evidence, not a definitive product preference.
Service issues are only marked high confidence when a transcript promise, open task, and missing sent artifact align.
Sensitive family context is surfaced carefully and only where it changes meeting handling.
The advisor gets six credible briefs instead of six shallow summaries, which means less scrambling, better personalization, and fewer missed commitments across a packed day.
A client who has been quiet for eight months suddenly books a portfolio and planning check-in, and the advisor wants to know what changed before taking the call.
CRM profile showing a moderate-growth household and a retirement target age that has not been updated recently.
Last meeting transcript covering retirement timing anxiety, volatility concerns, and hesitation around a rebalance proposal.
Planning notes referencing a possible down-payment gift for the client's daughter.
Task history showing a retirement scenario update was deferred rather than completed.
Email engagement showing the client still opened retirement-withdrawal and volatility-related content even during the quiet period.
A meeting objective centered on decision clarity instead of performance reporting.
Suggested opening language that acknowledges the prior unresolved discussion without sounding defensive.
A recommended agenda covering retirement timing, cash reserves, portfolio drift, and revised risk tolerance.
A collateral list including updated retirement scenarios, cash reserve worksheets, and the original rebalance rationale in plain English.
SarvaX interprets the long silence as relationship context rather than a blank slate. It surfaces that the client stopped replying after a rebalance proposal but continued engaging with educational content, suggesting continued interest without decision comfort.
The agent identifies the most likely reason for re-engagement as retirement timing anxiety, especially because the transcript captured concern that a down market could delay retirement. It also notes the unresolved family-support question that never made it into the formal plan.
The brief recommends avoiding a generic market recap and opening instead with the unresolved planning questions: retirement age, liquidity needs, family support, and how volatility affects the probability of retiring earlier.
SarvaX explicitly marks the daughter's home-purchase support as low-confidence current context because it was only mentioned once and has not been reconfirmed. That prevents the advisor from overstating a stale topic.
The system highlights that CRM still says retirement at 65 while transcript evidence suggests age 62 was seriously under discussion. Rather than overwrite the field, it recommends logging retirement timing as under review.
Task completion status is checked before claiming a prior follow-up happened.
Transcript-derived intent is separated from hard CRM fields so the advisor can see the mismatch clearly.
Silence is treated as an engagement signal to interpret, not proof of dissatisfaction.
Older family-planning details are preserved but demoted in confidence until reconfirmed.
The advisor re-enters the relationship with context, accountability, and a stronger meeting strategy instead of forcing the client to restate everything that mattered eight months ago.
An advisor is meeting a referred prospect for the first time and wants the conversation prepared without pretending there is more data than actually exists.
Referral email noting the prospect recently sold a medical practice.
Prospect reply saying she wants to get organized before making investment decisions.
CRM stub with only contact details and referral source.
Advisor note that the prospect is likely cautious and analytical.
Collateral library with liquidity-event checklists, advisory process decks, and document-sharing guidance.
Discovery questions tuned to a post-sale planning context.
A first-meeting agenda that qualifies fit before making recommendations.
Collateral that shows process maturity without implying advice before facts are known.
Advisor cautions around privacy, tax advice, and document collection.
SarvaX classifies the meeting as a liquidity-event discovery call rather than a generic introductory meeting because the strongest available signal is the recent practice sale.
The brief is intentionally disciplined about unknowns. It lists what is not yet confirmed, including sale timing, asset level, tax exposure, household structure, risk tolerance, and who else is advising the prospect.
The recommended posture is consultative. Instead of leading with products or model portfolios, the agent suggests a conversation built around urgent decisions, deferrable decisions, current advisors, and what an organized first 90 days should look like.
Privacy boundaries are built into the prep. The advisor is told it is fine to mention that the referring client spoke highly of the firm, but not to reference the referrer's portfolio, fees, or strategy as a comparison model.
SarvaX selects collateral designed to demonstrate structure: a liquidity-event planning checklist, a 90-day decision map, a tax-and-legal coordination checklist, and secure document-sharing instructions.
Referral context is used only to frame likely needs, not to fill unknown client data with assumptions.
All inferred signals are labeled as provisional because the prospect has not completed discovery yet.
No personal data from the referring client is surfaced beyond the existence of the introduction.
The system avoids premature risk or product recommendations until liquidity, tax, and household details are known.
The first meeting feels thoughtful and structured instead of generic, while the firm avoids the credibility hit that comes from over-assuming facts about a referred prospect.
Regulatory Note
SarvaX wealth agents operate as advisor-support and operational automation tools. Final investment decisions, client communications, and recorded compliance outcomes remain subject to advisor or compliance approval. Outputs preserve source context, confidence signals, and audit-ready notes to reduce unsupported automation.
Hours/Week Recovered
AI Sales Automation
& CRM Execution.
The Persona
Marcus: VP Sales
B2B SaaS · 15-person team · 300+ calls/week
Admin per call
CRM Accuracy
Follow-Up
Annual Recovery
Hours on Report Building
AI Operations
Reporting Automation.
The Persona
Elena: Director of Operations
100-person agency · 40 client accounts
Time Saved
Consistency
Coverage
of Tickets: Zero Human Touch
AI Customer Support
Automation.
The Persona
Lisa: Head of Support
SaaS company · 200 tickets/day · 8-person team
L1 Tickets Handled
Resolution Time
Team Focus
Annual FTE Saving