LoresseGet in touch
Home

Client use cases

Where expertise meets action.

Explore expert-led workflows across insurance, banking, healthcare, M&A, and higher education. Each story is summarized here at a glance; follow any of them for the full detail.

The work in focus

Case studies.

Explore the workflows and solution areas below, with specialists' judgment at the center of every decision.

Premium sedan with front-end collision damage
Case study / Insurance01 / 09

P&C insurance

A working example of the Loresse AI Operating System: every stage of the auto liability claim — from first notice of loss through closure — carried by agents, with claims professionals owning every decision. Document intelligence reads the police report, photographs, and correspondence as they arrive, assembles one visual claim view, and removes roughly 400 clicks from a claim's early handling while consolidating 22 manual data-field steps into a single reviewable summary.

  • Six stages: visualize, reconcile, recommend and reserve, communicate, manage, close
  • Liability, bodily-injury evaluation, medical bill review, and damage estimation with the adjuster in command
  • The same system completes end-to-end processing of workers' compensation and other bodily-injury claims
Read the full case study
Person making a card transaction
Case study / Financial risk02 / 09

Payment Screening

Loresse has solutioned a data analyst digital twin enabling level one disposition: AI assembles case context and prepares a reasoned OFAC clearance or escalation — with a written explanation — for an analyst to inspect and override. The same disposition solution handles all five screening types, bringing each through the same reasoned, reviewable workflow. Per screened record, AI disposition takes roughly 4 seconds against a five-minute manual-review benchmark, at about half a cent of processing cost versus $1.25 of analyst labor.

  • Handles OFAC, AML, PEP, adverse media, and fraud screening
  • ~4 seconds per screened record vs a five-minute manual benchmark — roughly 75× faster
  • The analyst owns the decision; every recommendation carries its reasoning trail
Read the full use case
Doctors consulting with a patient
Case study / Healthcare03 / 09

Claim processing automation

With a leading healthcare insurer, document-intensive claims consume roughly one-third of claim-processing time. In this workflow, AI reads claim documents directly from scanned images, extracts payment values with confidence scores, and matches them to the claim file — delivering 85%+ of key payment values extracted accurately and matched. The enriched record drops straight into the existing claim process, with no change to the claim system.

  • 85%+ of key payment values extracted accurately and matched
  • Five steps, from governed ingestion to the existing claim process
  • Every decision logged, explainable, and audit-ready — data stays masked and secure
Read the full case study
Traders on the New York Stock Exchange floor (photo: Anthony Quintano, CC BY 2.0)
Case study / Banking04 / 09

Loan origination

Construction-to-permanent lending rebuilt around expert judgment: two purpose-built digital twins — legal and operational — encode decades of underwriting and legal knowledge into persistent AI agents. Document ingestion falls from 30–40 minutes to 1–3 minutes and the end-to-end cycle from 30–51 days to a modeled 5–10 days, supporting a modeled $1.1M–$2.2M+ in annual uplift across reduced abandonment, faster time to interest income, and pipeline capacity.

  • Digital twins of legal and operational expertise, with 60–70% and 40–55% risk reduction respectively
  • Modeled figures, stated so the bank can test them against its own baseline in a POC
  • Progressive human-in-the-loop autonomy over an immutable audit trail
Read the full case study
Legal and deal professionals reviewing agreements together
Case study / M&A05 / 09

Mergers & acquisitions

Two sides of diligence in one workflow. Contract intelligence turns a changing agreement library into a structured register and clause-level search, with every answer citing the governing language. Financial statement analysis brings separate income statement, balance sheet, and work-in-progress files into one review-ready acquisition summary in familiar Excel, flagging unusual values for analyst scrutiny. Built on actual processes and data, ready to deploy with customization per customer.

  • Contract register, ask-anything agent, and living corpus
  • Source spreadsheets to a review-ready acquisition summary, with live Excel formulas and reviewable account mapping
  • Flags guide review — interpretation and acquisition decisions stay with experts
Read the full case study
Student-athletes and staff reviewing documents at an athletics facility
Case study / Higher education06 / 09

College NIL

A connected NIL lifecycle for colleges and universities: onboard student-athletes and sponsors, structure agreements, coordinate applicable NIL Go review, follow fulfillment and payments, and prepare records for state and College Sports Commission reporting. Expert compliance teams retain the judgment over requirements and submissions as the rules evolve.

  • One connected record from onboarding through reporting
  • NIL Go review and state and CSC reporting in the same workflow
  • Human review of each deal and its supporting evidence
Read the full case study
Appointment cards and scheduling slips in ordered columns connected by a copper routing thread
Case study / Service operations07 / 09

Scheduling & service desk

An AI-native operating system for scheduling and service desk work: one connected workflow from intake and AI-assisted diagnosis through routing, quality control, and close-out — with payments and records posting directly to NetSuite and other ERP systems. Built on actual service-desk processes and data, ready to deploy with customization per customer.

  • Six steps from intake to close-out, with the coordinator's judgment captured as a digital twin
  • Integrates with NetSuite and other ERP systems — no rip-and-replace of the system of record
  • Targets 20–30% faster turnaround and 40–50% fewer status calls, sized to your baseline during onboarding
Read the full case study
RFP questionnaires and due-diligence forms with ink lines connecting repeated questions to a master answer ledger
Case study / Asset management08 / 09

RFP & DDQ response

An AI-native operating system for RFP and DDQ response in asset management: seven functional digital twins — investment, compliance, operations, client service, legal, risk, and marketing — draft from a governed, version-controlled answer library, with subject-matter experts reviewing every response that needs their judgment. Built on actual RFP processes and data, ready to deploy with customization per customer.

  • ~83% less time per RFP — 20–40 hours down to 3–7 — expanding capacity 3–5× with the existing team
  • Automated compliance checking against current disclosures and GIPS standards, over a full audit trail
  • Targets 70–80% fewer inconsistencies and 60–70% fewer compliance exceptions, sized to your baseline during onboarding
Read the full case study
Cross-functional working pods collaborating across a bright open-plan office floor
Case study / Private equity09 / 09

The Clean Room

Port Co transformation: the AI-native conversion of a legacy business. A parallel operating core — the Clean Room — stands up with zero technical, process, and cultural debt, while the acquired operation keeps serving existing commitments untouched. The judgment of the company's best people is captured into agents that run the work, humans take the high-value seats, and the org chart becomes a work chart. Built on actual conversion processes and data, ready to deploy with customization per portfolio company.

  • Six phases from stand-up to first live business — targeted at six months
  • Planning target of ~40 people at launch against the 120–180 a traditional build would staff
  • Agents do the routing humans used to; every agent ships with a model card, audit trail, and kill switch
Read the full case study

What would this look like in your operation?

Start a conversation