An agentic AI platform that reads every incoming ServiceNow ticket, checks what your own L1/L2 engineers actually did on similar tickets before, decides the right course of action, and executes it directly against your connected systems — with full audit logging and a human checkpoint on anything risky. Everything on this screen is running against a real local database, a real trained ML model, and a real (simulated-connector) execution engine — nothing here is a canned recording.
–
Tickets analysed
–
Security tickets / mo
–
Connectors wired
–
Hours saved / yr (modelled)
Total historical tickets
–
Escalated to L2
–
SLA breach rate
–
Model accuracy (live)
–
Tickets by category (top 10)
Automation tier split (ground-truth, from historical outcomes)
Live Ops Console
Pulls real pending tickets from the database and runs each one through the actual classify → precedent-lookup → guardrail → execute → write-back pipeline over a live WebSocket. Watch the connector tiles light up as real (simulated) commands are issued.
Agent execution log 0 / 0
Click "Run live batch" to pull real pending tickets and watch the agent pipeline execute against them, step by step, over a live WebSocket.
Ticket queue (this run)
No run in progress.
Connector health & live activity
Ticket Explorer
Browse the synthetic 3-month history (or anything you've ingested). Open any ticket to compare exactly what the human L1/L2 engineer did against what the AI would do (or already did) on it.
Number
Summary
Category
Priority
Tier
AHT (min)
Escalated
SLA
Loading…
–
Connected Systems
Every execution connector the platform can act through. Each uses the real API/CLI syntax for that vendor (see the ticket drawer for exact commands); "simulated" here means the destination system is a local simulator rather than a live customer tenant — everything else in the pipeline (classification, decisioning, logging) is real.
As-Is vs To-Be — the detailed picture
Every number below is derived live from the ROI engine using the inputs set in the ROI & TCO Calculator tab. Change the inputs there and this page updates too.
Average Handling Time by category — As-Is vs To-Be (minutes)
Annual cost by category — As-Is vs To-Be
Automation maturity ramp — zero-touch % over time
Effective FTE required — As-Is vs each ramp stage
Where you start vs where you end — the maturity roadmap
Category detail table
Category
Volume/yr
L1 AHT (As-Is)
Escalation %
As-Is cost
To-Be cost
Hours saved/yr
ROI & TCO Calculator
Fully editable — change any input and every number on this page (and on the As-Is vs To-Be tab) recalculates instantly by calling the same ROI engine used in the exported proposal.
Inputs 🔒 Locked
Per-category AHT overrides (minutes) 🔒 Locked
As-Is annual cost
–
To-Be annual cost
–
Annual savings
–
Hours saved / year
–
FTE released
–
3-year ROI
–
Payback period
–
3-yr platform TCO
–
3-Year Total Cost of Ownership
Monthly savings trajectory across the maturity ramp
Commercial & Cost Breakdown 🔒 Locked
Every cost line behind the Fixed-Bid and Annual Licence models below is locked by default. Click Edit Pricing to modify values, then click Save & Apply Pricing to hardcode and recalculate live.
Fixed-Bid — one-time build charges (Rs)
Includes 4 weeks of hypercare in Month 4 (right after Week-8 50% automation + the Month-3 Phase 2 kickoff), then full handover — no forced ongoing fee.
Fixed-Bid — post-handover support plan (opt-in)
Annual Licence & shared costs (Rs)
Loading commercial breakdown…
Continuous Learning
Every human override on an AI-assisted ticket becomes a labelled training example. Retrain the model on demand and watch the metrics move.
Model accuracy
–
Macro F1
–
Training rows
–
Last trained
–
Model version history
Version
Trained at
Accuracy
Macro F1
Rows
Notes
Learning events feed
Data & Downloads
Ingest data in any format — CSV, TSV, JSON, JSON-Lines, Excel, XML, Parquet — with automatic column mapping onto the internal schema. The same pipeline handles a single streamed "data in motion" event.
Upload your own historical or live data
⬆
Drag a file here or click to browse CSV, TSV, JSON, JSON-Lines, XLSX, XML, Parquet — any column names
Download the synthetic demo dataset
Three months of fabricated-but-realistic ServiceNow-shaped tickets, the human L1/L2 action trace the ML model trains on, approvals, and SLA task rows. Entirely synthetic — safe to use in any live demo.
Set the default display currency for the ROI & TCO Calculator and As-Is vs To-Be pages.
Default currency
About this environment
This application runs entirely on a local ML model and a local SQLite database seeded with three months of synthetic-but-realistic ServiceNow history. No customer-identifying data is stored anywhere in this build. An external LLM call is used only for optional narration if an API key is configured — every such call is explicitly labelled in the console log.