AI-powered email automation and intelligent workflow routing for high-volume enterprises.
CortexOne reads every message that hits your shared inboxes, works out what it wants, pulls the data out of the attachment, and opens the right ticket in the right system — before your first agent has logged in.
- Routing accuracy at go-live
- 94%Routing accuracy at go-live
- Median classification time
- 1.2 sMedian classification time
- Straight-through processing
- 78%Straight-through processing
Live sandbox, one day
We provision a sandbox against a copy of your own mail archive, so the accuracy you evaluate is the accuracy you would get in production.
Or WhatsApp the AI deskWatch it read, extract and route in real time.
Sample enterprise mail flows through the model below. Pause the stream and feed messages one at a time to see each decision on its own.
CortexOne inference stream
sandboxInbound message
accounts@northlinecargo.pk
Invoice INV-88213 — payment remittance attached
Please find attached the remittance advice for invoice INV-88213 dated 04 March, amount PKR 486,500 against PO 5521-A.
remittance-INV-88213.pdf
Model output
Waiting for inputInvoice / remittance
97%
Extracted
Routed workflows
0/0 straight-through
Routed messages will appear here.
Four stages, one pass, fully auditable.
Classify, extract, route, then watch the clock — with a human in the loop wherever the model is not sure.
It knows what the email wants before anyone reads it
A transformer model fine-tuned on your own historical mail scores every message against your intent taxonomy — and tells you how sure it is, so you decide where the automation line sits.
- Intent taxonomy built from two weeks of your archive
- Confidence score on every prediction, threshold set by you
- Language, sentiment and urgency detected in the same pass
- Thread-aware: a reply inherits context from the conversation
- Day-one accuracy
- 94%Day-one accuracy
- Median latency
- 1.2 sMedian latency
- Intents supported
- 40+Intents supported
Intent scoring
Top candidates for one inbound message
- Invoice / remittance97%
- Order status query62%
- New RFQ21%
Decision
Confidence 97% is above your 85% threshold → routed automatically. Below it, this lands in the human review queue with all three candidates attached.
Everything the inbox layer has to get right.
AI that hands work to people is only useful if people can trust and audit it.
NLP intent classification
Reads the subject, body, thread history and sender reputation to decide what a message actually wants — an order, a complaint, a remittance, a resignation — and how urgent it is.
Smart attachment & invoice extraction
Pulls invoice numbers, PO references, amounts, dates, container IDs and line items out of PDFs, scans and spreadsheets, then validates them against your ERP before anything is posted.
Automated CRM/ERP ticket routing
Opens the ticket, deal or AP document in the right system with the right owner, queue, priority and tags already set — Zendesk, Freshdesk, HubSpot, Salesforce, SAP, Odoo or your own API.
SLA breach alerts
Watches the clock on every routed item, predicts which ones will miss their target based on queue depth and history, and escalates before the breach instead of reporting it afterwards.
Human-in-the-loop review
Anything below your confidence threshold goes to a review queue with the model's reasoning attached. Every correction becomes training data, so accuracy climbs week over week.
Full audit trail
Every classification, extraction and route is logged with its inputs, score and model version. Compliance can reconstruct exactly why a message went where it went, months later.
Connects to what you already run
How many hours is your inbox eating?
Set your daily volume and team size. The model shows the labour hours AI routing gives back every month.
Your inbox today
Four inputs. The model below assumes 78% straight-through automation.
Across every shared inbox: support, orders, invoices, bookings
Agents who triage or reply to that mail
Read, classify, route and log a single message
Salary, benefits, tooling and supervision
Triage load against your team’s capacity
Labour hours saved monthly
2,057
≈ 9.9 full-time agents returned to higher-value work
Rs 2,468k
Monthly labour value
36k
Emails processed / month
How the hours break down
- Fully automated1,893 hrs
28,392 messages classified, extracted and routed with no human touch
- AI-assisted164 hrs
5,460 messages arrive pre-drafted for one-click approval
- Manual triage hours today
- 2,427
- Remaining after automation
- 370
Modelled on 78% straight-through automation and 15% AI-assisted handling at 45% time saved, over 26 working days. Your first two weeks on the sandbox replace these with measured numbers.
“CortexOne reads 3,000 emails a day before my team opens Outlook. Invoices land in SAP already matched to the purchase order, and the exceptions are the only thing a human sees.”
Request CortexOne live sandbox access.
We provision an isolated sandbox, connect it to a copy of your mail archive, and show you measured accuracy on your own traffic — not a demo dataset.
- Sandbox provisioned within one working day
- Intent taxonomy drafted from your own archive
- Measured accuracy report after two weeks
- Costed estimate based on your real message volume
- NDA signed before any data moves
Security review first? Ask for the architecture pack and DPA at info@digitifyzar.com.
CortexOne, answered.
No. Every decision is logged with the input it saw, the confidence score, the model version and the resulting action. You can replay any message through the audit view months later and see exactly why it was routed the way it was.
Around 94% on common intents using the base model plus your two weeks of historical mail. Anything below your confidence threshold — you set it — goes to a human review queue instead of being routed. Corrections feed back as training data, and most customers see 97%+ within a quarter.
Wherever your contract says. The default is our Pakistan-resident cloud; regulated customers run CortexOne inside their own VPC or on-premise. Message bodies can be processed and discarded, retaining only metadata and the extracted fields, if that is what your policy requires.
Microsoft 365, Exchange, Google Workspace, or any IMAP mailbox on the inbound side. Outbound it writes to Zendesk, Freshdesk, HubSpot, Salesforce, SAP, Odoo, Zoho, Slack, Teams, or your own REST endpoint. If a system has an API, we route to it.
It does not guess. Low-confidence messages land in a review queue with the model's top three candidate intents and the evidence for each. A human picks one in a couple of seconds, the message routes, and the correction is used for retraining.
Per thousand messages processed, with a monthly minimum that includes the sandbox, connectors and support. Extraction of complex documents is metered separately. You get a costed estimate from your own volume before you commit to anything.