CASE STUDIES

Problems, solvedin the open.

Anonymised engagements, representative of real delivery. Client names and verified metrics are shared under NDA.

01 · LOGISTICS02 · HEALTHCARE03 · PROF. SERVICES
CASE STUDY 01LOGISTICSAI AGENTS · AUTOMATION · REPORTING

A regional logistics operator drowning in exceptions.

THE CHALLENGE

Dispatch exceptions were triaged by hand across phone, email and spreadsheets. By the time issues surfaced in reporting, they were days old — and coordinators spent their shifts reacting instead of planning.

THE SOLUTION

An AI operations agent that ingests exception events, classifies and prioritises them against business rules, drafts responses, and escalates only what needs human judgement — feeding a live operations report refreshed the same day.

AI ARCHITECTURE

EVENT SOURCES CLASSIFICATION AGENT RULES + PRIORITY ACTION / ESCALATE LIVE OPS REPORT

BEFORE

  • · Exceptions triaged manually, hours later
  • · Reporting lagged operations by days
  • · Coordinators in constant reactive mode

AFTER

  • · Routine exceptions handled automatically
  • · Same-day operational visibility
  • · Humans reserved for true judgement calls

Automated triage

Routine exceptions no longer queue on a person.

Same-day reporting

Operations visibility moved from weekly to daily.

Focused teams

Coordinators shifted from reacting to planning.

TECHNOLOGY: AI AGENT · RULES ENGINE · SYSTEM INTEGRATION · AUTOMATED REPORTING

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CASE STUDY 02HEALTHCAREDOCUMENT AI · KNOWLEDGE ASSISTANT

A clinic group losing clinical hours to paperwork.

THE CHALLENGE

Intake forms arrived by hand, email and fax; staff re-keyed everything. Policy answers lived in binders and in senior staff memory, so simple questions interrupted clinical work all day.

THE SOLUTION

Document AI that digitises and validates intake with a human-review queue for low-confidence fields, plus an internal knowledge assistant answering staff questions from approved policy documents — with citations.

AI ARCHITECTURE

INTAKE DOCS EXTRACTION + VALIDATION HUMAN REVIEW QUEUE PATIENT RECORD SYSTEM

BEFORE

  • · Manual re-keying of every intake form
  • · Policy questions interrupting clinicians
  • · Errors discovered downstream

AFTER

  • · Intake digitised end to end
  • · Answers in seconds, with citations
  • · Validation catches errors at the door

Hours returned

Intake administration moved off clinical staff.

Instant answers

Policy lookup stopped consuming senior time.

Cleaner records

Validation at intake, not after the fact.

TECHNOLOGY: DOCUMENT AI · KNOWLEDGE RETRIEVAL · ACCESS CONTROL · REVIEW WORKFLOW

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CASE STUDY 03PROFESSIONAL SERVICESGENERATIVE AI · CRM INTEGRATION

A services firm rewriting every proposal from scratch.

THE CHALLENGE

Winning work depended on proposals, yet every draft started from a blank page. Past work was scattered across drives and individual inboxes, so quality depended on who happened to be available.

THE SOLUTION

A generative proposal assistant grounded in the firm's case library, credentials and pricing guidance — integrated with the CRM so each draft starts from the actual opportunity record, with partner review built in.

AI ARCHITECTURE

CRM OPPORTUNITY KNOWLEDGE RETRIEVAL DRAFT GENERATION PARTNER REVIEW SUBMISSION

BEFORE

  • · Every proposal started from zero
  • · Quality varied by who was available
  • · Hours of hunting for past material

AFTER

  • · Structured first draft in minutes
  • · Consistent voice grounded in real work
  • · Partners edit, not assemble

Faster turnaround

First drafts produced in minutes, not days.

Consistent quality

Every draft grounded in the firm's actual track record.

Knowledge reused

Past work became a living asset, not an archive.

TECHNOLOGY: GENERATIVE AI · KNOWLEDGE RETRIEVAL · CRM INTEGRATION · REVIEW GATES

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