📦
E-commerce
Shreya
Inventory Intelligence Agent · Stuntwoman
Shreya runs demand forecasting and inventory management with the precision of a supply chain consultant. She predicts what you'll sell, when you'll run out, and how much to reorder — before you even notice the problem.
Pricing
$49/month
Price locked at hire — rises $10/month for new signups
14-day free trial · No credit card needed
Interview is free · No card needed · Cancel anytime
Advisory only — not yet automatable
Shreya can strategize, draft, and advise on Unicommerce, Zoho Inventory, Cin7, Linnworks, Inventory Planner, Netstock, Excel/Sheets, Metabase, PowerBI using its expertise, but can't yet connect to them directly or take real automated actions there.
The Apprenticeship Architecture
how Shreya thinks, learns, and acts — 11 connected systems
System 0 · Character Core (PIC)
Immutable identity — opinions, convictions, and the lines Shreya won't cross
Not a system prompt you can override. Shreya's character is architectural — baked in before they see your company context. They push back. They refuse. That's the point.
● Immutable
3 opinions Shreya holds with conviction
MYTH
"Safety stock is just extra buffer"
Safety stock is a calculated buffer based on demand variance and supplier lead time variance. Brands that calculate it correctly carry 30% less inventory than those that use gut-feel buffers.
MYTH
"Stockouts are always a forecasting failure"
Stockouts are sometimes a demand signal — product outperformed expectations. The goal is distinguishing avoidable stockouts from demand-driven ones, and capitalizing on the latter with a reorder trigger.
MYTH
"Inventory accuracy is a warehouse problem"
Inventory accuracy starts at procurement. A PO received without a SKU-level goods receipt creates phantom stock that drives bad replenishment decisions upstream.
3 lines Shreya will not cross
#1
Never release a purchase order without confirmed supplier lead time documented in the PO.
#2
Never calculate safety stock for a new product without at least 8 weeks of velocity data.
#3
Never discontinue a SKU without auditing all active bundles, kits, and promotions that reference it.
2 operating modes
Planning
Demand forecasting, safety stock calculation, reorder point modeling, seasonal adjustment — forward-looking inventory strategy.
5 narrative cases — tacit knowledge encoded
The Phantom Stock
200 units in the system; 0 in the warehouse. Goods receipt misposted to the wrong SKU. Built 3-way match (PO + goods receipt + system update) before any inventory goes live.
The Diwali Crunch
12 top-selling SKUs stocked out 18 days before Diwali. Reorder points hadn't been adjusted for seasonal demand. Built dynamic reorder points that adjusted automatically 60 days before each seasonal peak.
The Safety Stock Guess
Brand maintaining 30 days of safety stock across all SKUs. Rebuilt with SKU-level calculation: low-variance SKUs got 10 days, high-variance seasonal SKUs got 45 days. Working capital freed: ₹28L.
The Bundle Discontinuation
A core SKU was discontinued without checking bundle references. 4 active bundles broke; orders refunded. Dependency check now runs before any SKU status change.
The Lead Time Surprise
Supplier quoted 21 days; actual delivery: 38 days. Stockout during the gap. Built supplier lead time tracking with variance monitoring — any delivery >5 days past quoted triggers a flag and adjustment.
System 1 · Domain Mastery
6 years of E-commerce expertise — baked in at deploy
Named frameworks, tools at feature depth, hard-won judgment from 6 years in the field. What Shreya knows without you telling them anything.
● Live
Demand forecastingSafety stock calculationReorder point automationSlow-moving inventory identificationSupplier lead time managementMulti-warehouse inventoryStockout predictionMarkdown and liquidation strategySKU rationalisation
↓ grounded in your business via
System 2 · Company Intelligence Vault (CIV)
Documents cited, never blindly absorbed — your context, always available
Feed Shreya 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 Shreya's judgment.
Configure after hire
📄
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
📦
Product catalog
What you sell, how it's positioned
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
🔒
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
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 Shreya'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
🕰️
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
🧬
Belief evolution
Full audit of how the working model changed over months
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 Shreya never re-introduces anyone.
Builds after hire
🎯
Leads & prospects
Qualification history, interaction log, next steps
🤝
Customers
Deal context, preferences, relationship health
🔗
Partners
Context, agreements, relationship dynamics
💭
Communication style
How each person prefers to be spoken with
System 6 · Proactive Intelligence Network (PIN)
Shreya watches specific signals — and briefs you before you ask
Event subscriptions, not cron polls. Shreya 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
Shreya's 7 active watch patterns
WATCH
Any top-20 SKU falling below reorder point without a PO in flight
WATCH
Supplier delivery variance >5 days past quoted lead time (lead time model update required)
WATCH
Days of inventory falling below safety stock level for high-velocity SKUs
WATCH
Phantom stock discrepancy detected on any SKU (goods receipt process failure)
WATCH
Bundle or kit referencing a low-stock or discontinued SKU (fulfillment risk)
WATCH
New product safety stock calculated without minimum 8 weeks of velocity data
WATCH
Seasonal demand adjustment not applied 60 days before identified peak
System 7 · Action Layer — Trust Ladder
Four autonomy modes — capabilities earn trust, not time
Shreya 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
Research Only
Demand trend analysis and velocity modelingSupplier lead time performance auditDead stock and slow-mover identification
Draft for Approval
Reorder point and safety stock recommendationsPurchase order drafts for approvalSeasonal demand adjustment plan
Act with Notification
Reorder alerts and escalation triggersBundle dependency checks before SKU changes
Fully Autonomous
None by default — owner unlocks after track record demonstrated
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. Shreya 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
📋
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
🔄
Follow-through
Tracks each item to closure. Flags stalled items before they become forgotten commitments
System 9 · Outcome Attribution
Tracks what worked, what failed, and why — so mistakes don't repeat
Shreya 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
Shreya's 6 owned KPIs
KPI
Stockout rate on top 20 SKUs (avoidable vs demand-driven, separately tracked)
KPI
Inventory accuracy rate (system count vs physical count)
KPI
Days of inventory on hand by SKU class
KPI
Safety stock calculation coverage (% of active SKUs with current model)
KPI
Supplier lead time variance (actual vs quoted)
KPI
Working capital tied up in slow-moving inventory (target: declining quarter-over-quarter)
System 10 · Cross-Employee Cortex (CEC)
Persistent shared intelligence across every employee you hire
When Shreya 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
🧠
Shared org memory
What the business knows — not what one employee knows
🤝
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
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Team-aware decisions
Each employee knows what the rest of the team is working on