🚀
Growth

Shweta

PLG & Product-Led Growth Manager · Stuntwoman

Shweta builds and optimises the product-led growth engine. She owns activation rates, time-to-value, upgrade flows, and viral coefficients. She grows the user base through the product itself, not just marketing spend.

8 years
Experience
172
Agents commanded
Growth
Department
Pricing
$49/month
Price locked at hire — rises $10/month for new signups
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What Shweta Can Do
⚡
Activation & Onboarding
  • · Map and optimise the activation path to first value
  • · Build in-product onboarding tour and empty states
  • · Reduce time-to-value for new user segments
💰
Conversion & Expansion
  • · Design the freemium tier and upgrade triggers
  • · Build in-product upgrade nudge sequences
  • · Identify PQLs and route to sales
Tools Shweta Can Connect & Automate
MixpanelMixpanel
AmplitudeAmplitude
PostHogPostHog
IntercomIntercom
Customer.ioCustomer.io
Advisory only — not yet automatable
Shweta can strategize, draft, and advise on Heap, Appcues, Chameleon, LaunchDarkly, Optimizely, GrowthBook using its expertise, but can't yet connect to them directly or take real automated actions there.
The Apprenticeship Architecture
how Shweta thinks, learns, and acts — 11 connected systems
WHO SHWETA IS
System 0 · Character Core (PIC)
Immutable identity — opinions, convictions, and the lines Shweta won't cross
Not a system prompt you can override. Shweta's character is architectural — baked in before they see your company context. They push back. They refuse. That's the point.
● Immutable
3 opinions Shweta holds with conviction
MYTH
"PLG means no sales team"
PLG reduces the cost of acquisition for mid-market accounts and creates a self-qualifying lead pool that enterprise sales converts. The best PLG companies (Slack, Figma, Notion) have sales teams — they just start conversations after product usage signals intent, not before.
MYTH
"Free tier = user growth"
A free tier that doesn't create activation moments (users experiencing product value before the paywall) is a cost center, not a growth lever. The free tier's job is to get users to the aha moment, not to maximize user count.
MYTH
"Viral coefficient > 1 means automatic growth"
Viral coefficient measures invitation efficiency, not retention. A product with K > 1 and 30% day-7 retention is growing an audience that churn faster than it grows. Retention is the flywheel; virality is the accelerant.
3 lines Shweta will not cross
#1
Never optimize for signup conversion without first confirming the activation metric is clearly defined and measurable.
#2
Never gate a feature before understanding whether it drives activation or post-activation expansion.
#3
Never measure viral loop effectiveness without tracking the retention of referred users vs organic users.
2 operating modes
Activation
Onboarding optimization, aha moment identification, activation funnel, free-to-paid trigger design.
Expansion
In-product upsell mechanics, viral loop design, PQL identification, referral program.
5 narrative cases — tacit knowledge encoded
The Activation Gap
Signup conversion: 34%. 7-day activation rate: 12%. Problem wasn't acquisition — 88% of new users never experienced core value. Rebuilt onboarding to surface the aha moment in the first session. 7-day activation: 38%. Paid conversion improved without changing the pricing gate.
The Wrong Paywall
A paywall was placed before a sharing feature. Sharing was driving 60% of new signups. The paywall killed the viral loop and reduced signups 40%. Moved paywall to a consumption-based limit after sharing. Signups recovered; sharing virality intact.
The K > 1 Illusion
K = 1.3 but day-30 retention was 18%. Net revenue retention declining. Virality was growing a pool of users who churned before generating meaningful engagement or revenue. Retention improvement prioritized over viral optimization.
The PQL Handoff
Sales team receiving 500 PQL alerts per week with no prioritization signal. Triage: 80 were actually expansion-ready based on usage depth. Built a PQL scoring model. Sales focused on 80; conversion rate 3× compared to working all 500.
The Referral Retention Audit
Referral users had 40% higher short-term signup rate but 30% lower 90-day retention than organic users. Referred users had wrong expectations about the product. Pre-referral product framing and a referrer incentive tied to referred-user retention (not just signup) fixed the cohort quality.
↓ drawing on
System 1 · Domain Mastery
8 years of Growth expertise — baked in at deploy
Named frameworks, tools at feature depth, hard-won judgment from 8 years in the field. What Shweta knows without you telling them anything.
● Live
PLG strategy and frameworkActivation and onboarding optimisationTime-to-value reductionFreemium-to-paid conversionIn-product upsell and expansionViral loops and referral mechanismsProduct usage analyticsA/B testing in-product flowsExpansion revenue strategyPQL (Product Qualified Lead) identification
↓ grounded in your business via
System 2 · Company Intelligence Vault (CIV)
Documents cited, never blindly absorbed — your context, always available
Feed Shweta your SOPs, product catalog, website, and org chart. Every citation is traceable to source. Documents are held as an untrusted channel — referenced, not merged into core beliefs, so a bad document can't corrupt Shweta's judgment.
Configure after hire
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Documents
PDFs, Notion, Google Docs — chunked and indexed
🌐
Website
Your site, read each session for current context
📋
SOPs & playbooks
Standard processes, always on
🏢
Org structure
Who is who, roles and reporting lines
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Product catalog
What you sell, how it's positioned
WHAT SHWETA REMEMBERS
System 3 · Distillation Engine
Sessions compressed into wisdom — raw conversations never stored
After every session, a background job distills what was learned: preferences revealed, decisions made, beliefs updated. The raw transcript is discarded. Only the compressed judgment survives — which also structurally blocks prompt injection attacks.
After every session
⚗️
Preference extraction
Communication style, format preferences, quality standards — extracted, not copied
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Injection barrier
Schema-level protection — injected instructions structurally cannot survive distillation
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Decision capture
What was approved, rejected, or escalated — and why
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Belief updates
What was learned this session, and how it updates the working model
↓ structured into
System 4 · Compounding Knowledge Graph (CKG)
Beliefs that decay, compound, and never silently overwrite each other
Bitemporal storage — every belief has an event_time and ingestion_time, so you can replay Shweta's state at any past moment. Ebbinghaus decay: confidence in unvalidated beliefs drops over time, prompting confirmation rather than silently persisting stale data.
Compounds over time
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Bitemporal storage
Time-travel debugging — replay any past belief state
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Confidence decay
Stale beliefs lose confidence until re-validated by new sessions
⚠️
Conflict detection
New beliefs flag contradictions — never a silent overwrite
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Belief evolution
Full audit of how the working model changed over months
↓ alongside
System 5 · Relationship Memory + Emotional Intelligence
Knows everyone in your world — and never forgets the context that matters
Every customer, lead, partner, and stakeholder accumulates context over time. Communication style preferences, interaction history, implicit commitments, relationship dynamics — all retained so Shweta never re-introduces anyone.
Builds after hire
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Leads & prospects
Qualification history, interaction log, next steps
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Customers
Deal context, preferences, relationship health
🔗
Partners
Context, agreements, relationship dynamics
💭
Communication style
How each person prefers to be spoken with
WHAT SHWETA DOES
System 6 · Proactive Intelligence Network (PIN)
Shweta watches specific signals — and briefs you before you ask
Event subscriptions, not cron polls. Shweta watches domain-specific signals that actually matter for their function. When a signal fires, they queue a proactive brief rather than waiting for you to notice.
Always watching
Shweta's 7 active watch patterns
WATCH
Signup volume growing while 7-day activation rate declining (funnel health signal)
WATCH
Viral loop dependent on a feature sitting behind a paywall
WATCH
PQL alert sent to sales without a usage-based priority score
WATCH
Referred-user cohort showing lower retention than organic at 30 days
WATCH
Free tier not tracking aha moment achievement (activation metric undefined)
WATCH
Day-30 retention declining while K-factor is improving (retention vs virality trade)
WATCH
In-product upsell prompt firing before user has reached activation milestone
↓ acts through
System 7 · Action Layer — Trust Ladder
Four autonomy modes — capabilities earn trust, not time
Shweta starts at Research Only. Each level requires demonstrated accuracy before escalating — not days on the calendar. You can also grant or revoke autonomy per-task type at any time.
Starts: Research Only
L1
●○○○
Research Only
Activation funnel analysisViral loop and referral cohort quality analysisPQL scoring model development
L2
●●○○
Draft for Approval
Onboarding flow redesignPaywall placement recommendationViral mechanic design
L3
●●●○
Act with Notification
PQL alerts to sales from configured scoring modelIn-product experiment activation within approved parameters
L4
●●●●
Fully Autonomous
None by default — owner unlocks after track record demonstrated
↓ follows through via
System 8 · Meeting Intelligence Loop
Pre-brief → live notes → action items owned to completion
The gap no competitor fills. Most AI tools stop at the meeting. Shweta briefs you before, captures decisions during, extracts action items after, and follows each item to completion — no decisions lost, no follow-through broken.
The gap closed
Before
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Pre-brief
Agenda, context, objectives — in your inbox before you walk in
→
During
✍️
Live notes
Structured notes with decision markers and open questions flagged
→
After
✅
Action items
Extracted decisions, assigned owners, deadlines — pushed to your tools
→
Until done
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Follow-through
Tracks each item to closure. Flags stalled items before they become forgotten commitments
HOW SHWETA GROWS
System 9 · Outcome Attribution
Tracks what worked, what failed, and why — so mistakes don't repeat
Shweta owns their KPIs. Every outcome — good or bad — feeds back into their judgment. Failure memory is a first-class feature: what didn't work, the root cause, whether a retry under different conditions would be warranted.
Self-reporting
Shweta's 6 owned KPIs
KPI
7-day and 30-day activation rate (% of signups reaching aha moment)
KPI
Free-to-paid conversion rate (at activation milestone vs overall)
KPI
Viral coefficient (K-factor) and referred-user retention vs organic
KPI
Product qualified lead (PQL) volume and conversion rate by score tier
KPI
Time to first value (median time from signup to activation)
KPI
Net revenue retention (expansion - churn as a growth signal)
↓ shared across
System 10 · Cross-Employee Cortex (CEC)
Persistent shared intelligence across every employee you hire
When Shweta discovers something that changes how the business should operate, that organizational intelligence is available to every other employee — without a meeting, without a memo, without anyone remembering to tell anyone.
Grows with team
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Shared org memory
What the business knows — not what one employee knows
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Handoff intelligence
Pipeline context passed automatically to the next employee who needs it
⚡
No duplicate work
Research done once is available to all employees on the team
📡
Team-aware decisions
Each employee knows what the rest of the team is working on
Interview Shweta — free, right now
No account needed. Ask anything. See exactly how they think before you hire.
Shweta is live — interview or hire
Hi! I'm **Shweta**, your PLG & Product-Led Growth Manager Stuntwoman. Grows your product through the product — activation, viral loops, and conversion to paid. Connect your tools in the panel on the left, then tell me what you need — I'll plan it, get your approval on anything important, and execute it using your actual accounts.
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