Ask the ERP.
Act in seconds.
Conversational AI across all 44 modules. Claude, Gemini or GPT — configurable per deployment. Predictive maintenance with 24–72 hour warning. Bulk actions with human-in-loop preview. Citations on every answer.
Multi-LLM
Claude · Gemini · GPT
24–72h
Predictive warning
44
Modules covered
0.4%
Hallucination rate
The reporting tax
Decisions wait on someone pulling a report.
A mill produces data 24×7 but answers crawl. Plant manager wants a margin breakdown — analyst takes a day. CFO wants working capital trajectory — finance team takes a week. Maintenance head wants impending failure list — there isn't one until something breaks.
Every delay between question and answer is a decision deferred. AI isn't a chatbot — it's removing the analyst bottleneck between operational data and operational action.
Question → answer lag · before vs after
* Production deployment, 90-day rolling baseline.
LLM Providers
Pick your engine. Lock your region.
Claude, Gemini and GPT — all wired in. Customer chooses based on residency, cost, and capability profile. Switch providers without re-doing your integration.
Anthropic Claude
Default for India deployments
Strong reasoning, low hallucination, long context. Best for financial reconciliation, GST analysis, audit explanations.
Models available
Claude Opus 4, Sonnet 4, Haiku 4
Data residency
AWS Mumbai or Singapore
Google Gemini
Multi-modal + Indic languages
Image + document understanding (DOC AI), strong Hindi/regional language support, integrates with Workspace.
Models available
Gemini 2.5 Pro, Flash, Nano
Data residency
GCP Mumbai or Singapore
OpenAI GPT-4
Plugin-rich workflows
Mature tool-use ecosystem, structured outputs, broadest integration library.
Models available
GPT-4o, GPT-4 Turbo, GPT-4 Mini
Data residency
Azure India Central or Asia
Conversational Example
A real query, end to end.
What the plant manager sees when asking a question that cuts across Production, Finance, Procurement and Sales.
Why is margin per tonne on KR-100 GSM kraft down 6.2% vs last quarter?
Three drivers, ranked by impact:
- Pulp cost up 4.1% — supplier batch BAT-9821 priced ₹2,847/t higher than contract. PO-4471 ↗
- Broke rate up 1.8 pts — PM-2 calendar bearing showed thermal drift starting 14-Nov. Maint-2391 ↗
- Price renegotiated — customer ABC dropped from ₹68,400/t to ₹66,820/t. SO-2847 ↗
Total reconciled: -6.2% (≈ -₹4,140/t). Sources: Production OEE, Procurement PO ledger, Finance margin report, Sales price log.
Capability Catalog
90+ AI capabilities. Six categories.
Conversation. Prediction. Anomaly. Bulk action. Insight. Safety. Every category gated by human review where it matters.
Conversational AI
22 capabilities
- · Natural-language queries across 44 modules
- · Multi-turn context with conversation memory
- · Cross-module joins ('top 10 customers Q3')
- · Action commands ('create quote for ABC, 50t kraft')
- · Human-in-loop preview before commit
- · Citations linked to source data
- · Rate limiting: 20 queries/minute/user
- · Conversation export with audit signing
- · Voice input (Hindi, English, regional)
- · Slack / Teams / WhatsApp adapters
Predictive Maintenance
16 capabilities
- · Vibration pattern analysis (bearings)
- · Dryer cylinder thermal anomalies
- · Refiner load drift detection
- · Motor current spike prediction
- · 24–72 hour advance warning
- · Confidence-scored alerts
- · Auto-create maintenance work request
- · Spare part availability check
- · Component lifecycle modeling
- · Maintenance cost-benefit ranking
Quality Anomaly Detection
14 capabilities
- · Multi-variate SPC with ML control limits
- · Defect cluster detection across reels
- · Pulp furnish drift correlation
- · Customer complaint root-cause linking
- · COA anomaly vs historical baseline
- · Suspect batch auto-hold workflow
- · Vision-based defect classification
- · Cross-shift quality stability scoring
Bulk AI Actions
12 capabilities
- · Bulk quotation generation
- · Bulk invoice creation from DC pool
- · Bulk PO release with budget check
- · Bulk GRN approval (low-risk)
- · Bulk customer outreach (collections)
- · Always preview → approve → commit
- · Per-action confidence + reasoning
- · Reversible with one click
- · Audit log with diff capture
Cross-Module Insights
18 capabilities
- · Margin per customer × grade × shift
- · Working capital trajectory
- · Inventory ageing → cash impact
- · Customer churn risk signals
- · Supplier risk + MSME exposure
- · Cash flow 13-week rolling forecast
- · Order-to-cash bottleneck detection
- · Energy cost anomaly across PMs
- · Demand pattern shift detection
Governance & Safety
12 capabilities
- · Per-user prompt + response logging
- · PII redaction before LLM call
- · Tool-use authorization matrix
- · RBAC enforced on every query
- · Data residency locked per tenant
- · Hallucination flagging with citations
- · Confidence threshold gating
- · Auditor view of every AI action
Safety & Governance
Six pillars. No surprises.
Enterprise AI fails when it's too autonomous. We engineer for trust — preview, citation, redaction, residency, rate limit, audit.
Human-in-the-loop by default
Any action that creates or modifies data shows a preview with diff and confidence score. Nothing commits without explicit user click. No autopilot for financial or compliance impact.
Citations, not assertions
Every AI answer ties claims back to source records — invoice ID, batch number, GRN line. Click to drill. If AI can't cite, it says so.
PII redaction before LLM
PAN, Aadhaar, contact details masked before any prompt leaves the deployment region. Redaction is reversible only inside the audit boundary.
Per-tenant data residency
Each deployment locked to a single region. Indian customer data never crosses the border without explicit consent. Pre-flight checks enforced.
Rate-limited + cost-capped
20 queries/minute/user by default. Monthly cost ceiling per tenant; soft alert at 80%, hard cap at 100%. No surprise bills, no runaway loops.
Full audit log
Prompt, redacted prompt, model, response, citations, user action — every exchange logged immutably. Auditor view shows complete AI history for any user or any action.
Use Cases
Real mill scenarios.
Four operational moments where AI saves hours, money, or both.
Case 01
Plant manager asks: 'Why is margin down on KR-100?'
Scenario
Monthly review. Plant manager opens chat: 'Why is margin per tonne on KR-100 GSM kraft down 6.2% vs last quarter?' Expects to spend an afternoon pulling reports.
AI Response
AI joins Production, Finance, Procurement and Sales data. Replies in 14 seconds with: pulp cost up 4.1% (specific supplier batch), broke rate up 1.8 pts (PM-2 calendar bearing), one customer renegotiated price down 2.3%. Each claim cited with drill-down link. Manager raises 3 actions inside the chat.
time to answer
14s
sources joined
4 modules
actions raised
3
Case 02
Predictive: Bearing fails in 38 hours
Scenario
PM-1 calendar bearing has been showing vibration drift over 11 days. Trend gradient steepening. Human eye on the dashboard would miss it for another shift or two.
AI Response
AI flags 'bearing failure predicted 38 hours ± 6 hrs, confidence 87%'. Auto-creates maintenance work request, checks spare availability (yes, in store), proposes preventive window (Sunday early shift, lowest production loss). Maintenance head approves with one click. Bearing swapped Sunday. No unplanned breakdown.
advance warning
38 hrs
downtime avoided
~6 hrs
confidence
87%
Case 03
Bulk: Send dunning to 240 overdue accounts
Scenario
AR clerk needs to send personalized collection emails to 240 overdue accounts before quarter-end. Manual: a full day of mail-merge and review.
AI Response
AI drafts 240 emails, each with customer-specific outstanding aging, last interaction notes, and tone calibrated to relationship history. Preview UI shows all 240 in a virtualized list with confidence per draft. Clerk reviews 18 flagged drafts (low confidence), edits 4, bulk approves the rest. 240 emails sent in 22 minutes.
drafts
240
review time
22 min
manual baseline
~7 hours
Case 04
Quality anomaly: defect pattern across 14 reels
Scenario
PM-2 made 142 reels overnight. Operator-level defect logging shows nothing unusual. But customer complaint dashboard shows 3 complaints from different customers received this morning.
AI Response
AI runs cluster analysis. Finds 14 reels with subtle moisture profile shift (3.2% above grade target on edges). All 3 complaints trace back to those reels. AI auto-holds the remaining 9 reels still in stock, raises CAPA, links pulp furnish change from 02:14 AM, suggests dryer steam balance check. CAPA opens for engineering before lunch.
reels identified
14
reels held
9
customers protected
3
Performance Comparison
Analyst-led vs AI-augmented.
Before/after numbers from a 90-day production deployment.
Implementation
Live in 30 days. Expand cautiously.
AI rollout sequenced for trust. Start with read-only chat; promote bulk actions only after proven confidence.
LLM provider + residency
- Provider selection (Claude/Gemini/GPT)
- Residency region locked
- API keys + rotation policy
- Cost ceiling + alerts configured
Data wiring + RBAC
- Module data sources mapped
- PII redaction rules tuned
- RBAC enforced on AI tool-use
- Per-user rate limits set
Predictive + anomaly models
- Vibration + thermal models trained
- Quality SPC baseline locked
- Defect cluster thresholds tuned
- Cross-module joins validated
Go-live + shadow mode
- Conversational chat open to power users
- Predictive alerts visible (no auto-action)
- Hallucination + citation audit weekly
- User feedback loop active
Expand cautiously
- Bulk actions opened by area
- Auto-actions promoted with proven confidence
- Custom skills added per role
- Cost vs value reviewed monthly
Every Capability
Drill into any feature.
Click any capability to drill in.
Integrations
Works with everything else.
Every AI action flows into the other modules — no manual data re-entry, no reconciliation pain.
AI→All Modules
AI query → data fetch
Pulls live data via internal APIs
AI→Approvals
AI suggest → human approve
Bulk approval with preview
AI→Sales
Forecast → demand
Predictive ordering
AI→Inventory
ABC/XYZ → reorder
Automatic reorder triggers
AI→Finance
Cash flow → forecast
Predictive payment behavior
Ready to modernize your mill?
See Papyrus BPApp
in your mill.
Book a personalized demo. We'll walk through every module relevant to your operation — from Deckle optimization to GSTR-3B compliance.
FAQ
AI — common questions
Which LLMs power the AI engine?+
Configurable per deployment: Anthropic Claude, Google Gemini, OpenAI GPT-4. Customer chooses based on data residency and cost preferences.
What can predictive maintenance detect?+
Vibration patterns indicating bearing wear, dryer cylinder temperature anomalies, refiner load drift, motor current spikes. Typically 24–72 hour advance warning of failures.
Is conversational AI just chat, or can it take actions?+
Both. 'Show me top 10 customers this month' returns data. 'Create a quote for 50 tons of 100 GSM kraft for ABC Mills' creates the draft quote with human approval before submit.
What about data privacy and AI hallucinations?+
All AI interactions are logged and auditable. Tool-use (creating quotes, approvals) requires human confirmation. Customer data never leaves the deployment region.