Use case

Intake and triage automation: getting the case complete before the human opens the file

Intake and triage automation is the work of capturing every new case across every channel, enriching it from the systems of record (Guidewire ClaimCenter, Epic, ServiceNow, Greenhouse), classifying severity against a written rubric, and routing it to the right human queue. The split that matters is **assembling the case versus deciding on it**. Adjusters, clinicians and recruiters keep the decisions; the agent removes the two hours of data entry that wraps around them. Industry-typical intake-step time savings run 30 to 60 per cent.

What we touch

The intake workflows we automate

01

First-notice-of-loss intake and severity classification in Guidewire ClaimCenter or Duck Creek Claims

02

New-patient referral intake with Availity eligibility verification, landing in Epic or Cerner

03

Support ticket categorisation and routing inside Zendesk or Salesforce Service Cloud

04

IT service desk intake with priority scoring in ServiceNow ITSM against the CMDB

05

Candidate application screening and shortlist surfacing in Greenhouse or Workday Recruiting

06

Legal matter intake and conflict checks against the iManage or NetDocuments index

07

Inbound vendor application intake into Coupa or SAP Ariba supplier onboarding

08

Customer onboarding document collection chase across DocuSign, Salesforce and the shared mailbox

Typical impact

What teams typically see

Industry-typical reductions on the intake step run **30 to 60 per cent** once the agent handles channel capture, enrichment and severity classification. An adjuster opening an FNOL in Guidewire ClaimCenter or Duck Creek inherits a fully coded claim with the policy verified, coverage confirmed and a severity tier already proposed. A triage nurse opening a referral in Epic or Cerner sees Availity eligibility checked, prior records pulled from the HIE and the appointment slot pre-suggested. A service-desk analyst in Zendesk or ServiceNow opens a ticket already categorised, deduplicated against open incidents, and routed to the right queue. For a 40-adjuster property and casualty desk handling 1,200 FNOLs a week, the recovered analyst time runs to around 4.2 hours per adjuster per week, which is 168 hours of senior judgement back in the building. The customer experience does not change (there is no chatbot in the loop), but the operator opens a complete file and spends their hour on the decision, not on the data entry that used to consume most of it.

These are industry-typical ranges from published studies and benchmarks, not specific Synarsi-client outcomes.

How an engagement works

From first call to live agent

  1. 01
    Scope. Name the intake workflow precisely. An auto property-damage FNOL is a different beast from a complex liability FNOL, and a new-patient referral into oncology has a different rubric from a routine primary-care intake. Write the severity classification logic down explicitly: the tiers, the inputs that decide each tier, and the routing decision attached to each. Capture the regulatory or commercial constraint that sets the floor: HIPAA for healthcare, the state insurance code for claims, GDPR for any EU-resident data, the SRA's outcome-focused rules for legal matter intake, the internal SLA commitment for IT. That constraint is what stops the agent from ever closing a case it should have escalated. The rubric and the constraint together are the spine of the build.
  2. 02
    Integrate. Wire every inbound channel into a single intake queue: shared mailbox, Marketo or HubSpot web form, IVR transcript from the contact-centre recording, SMS, fax-to-PDF for [healthcare](/industries/#healthcare) and legal, partner portal, file upload from the broker or referrer. Each channel produces a different artefact and the parsing path differs. Connect the enrichment systems next: the Guidewire PolicyCenter or Duck Creek Policy admin layer for [insurance](/industries/#insurance), Epic or Cerner for healthcare, Salesforce for support, Workday HCM and Greenhouse for recruitment, the ServiceNow CMDB for IT, the iManage conflict-checking index for legal. Define the system of record the agent writes to: the claims platform, the EHR encounter, the Zendesk or ServiceNow ticket, the ATS candidate record, the matter management system. Every read and every write goes into an audit log keyed to the case ID.
  3. 03
    Shadow. Run the agent alongside the human intake desk for two to four weeks before it writes a single record. The agent drafts its enrichment summary, its severity classification and its routing decision into a review queue; the intake coordinator, triage nurse, service-desk analyst or paralegal approves or corrects. Two case types matter most in this phase: the routine-looking case that turns out to be high severity once the enrichment lands (the minor fender-bender with a passenger injury, the standard referral with a missed prior diagnosis, the password-reset ticket from the CEO during an earnings call), and the high-noise case that looks urgent but classifies down once context is added. Calibrate the rubric against these. Shadow mode is also where you discover that one channel feeds the agent broken metadata or that an enrichment system is unavailable more often than the SLA claims.
  4. 04
    Cut over. The agent intakes routine cases end-to-end: parsing the inbound, pulling enrichment, classifying severity, populating the system of record and routing into the assigned queue within the SLA window. Exceptions escalate to a named human with the full audit trail attached: what came in, what was pulled, what classification was proposed and why it was held back. The human always handles the decision step; the agent never closes a case, denies a claim, refuses a referral or rejects a candidate on its own. Hold a weekly intake-quality review for the first quarter so the rubric tightens as edge cases surface. Intake is one of the [boring workflows that pay back fastest](/insights/boring-workflows-pay-back-fastest/) because the shape repeats thousands of times a month. See our [methodology](/methodology/) for how we sequence the rollout.

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