Case 01Health insurance

Triage

From stalled pilot to 96% accuracy in production

From 53% to 96%

Classification accuracy

  • 6,000+ hrs/yrManual triage eliminated
  • €175k/yrNet benefit

The problem

Customer emails and cases landed in shared queues, and staff read every one to decide where it should go. An AI triage project meant to fix this had stalled at 53% accuracy. Nobody trusted it enough to switch it on.

What we built

We rebuilt the triage. Rules handle the clear-cut cases, AI handles the grey areas, and anything uncertain goes to a person.

How it was measured

Before: the stalled pilot's own accuracy, 53%. After: accuracy in production, 96%. Hours saved are the manual triage time removed over a year.

  1. 01IntakeEmails and cases
  2. 02RulesClear-cut cases routed
  3. 03AI classifierGrey areas, read in context
  4. 04Confidence checkDoubtful items to a person
  5. 05Right teamRouted to resolve

Case 06Telecoms · Finance

VAT reporting

The data behind a key pillar of tax compliance, fixed at the source

From 3 hrs to 0 hrs

Manual work per run

  • Weekly to DailyTax data checked five times as often
  • 100%Transactions traced to the rule that decided them

The problem

Purchase VAT reporting is a key pillar of tax compliance. But the finance system report behind it came out wrong every time it was downloaded: duplicates, misaligned rows, mixed old and new tax codes. Two analysts fixed and reformatted it by hand, then reconciled the VAT figures to the general ledger, using judgement that lived only in their heads.

It was slow, so it only ran weekly. Each missed run was a chance for an error to slip through. That could mean tax overpaid or underpaid, or an audit by the tax authority with interest and penalties.

What we built

We wrote that judgement down as ordered tax rules and automated the whole pipeline to run every working day, with an audit trail. Each transaction is labelled include, exclude or human review, along with the rule that decided it. Nothing is released until the VAT totals reconcile to the general ledger.

How it was measured

Manual work per run was the time spent fixing, reformatting and reconciling the report by hand. It's now zero. The 100% is by design, because every transaction carries the rule that decided it.

  1. 01ExtractPulled from the finance system daily
  2. 02CleanFixes rows, removes duplicates
  3. 03DecideTax rules label each line
  4. 04ReconcileMatched to the general ledger
  5. 05DeliverAudit-ready file to the tax team

Read the full story of the VAT reporting case

Case 05Telecoms · Business customers

Usage alerts

From a manual chore to a feature customers configure themselves

1,600 hrs/yr

Agent time saved

  • One size fits all to Set per customerSpend alerts
  • €125k/yrAnnual cost saving
  • €80k+Customer credits avoided
  • 5New settings each customer can control

The problem

Agents warned business customers about unexpected usage spikes by hand, reading each customer's wishes from a free-text note. Written that way, it couldn't be automated or scaled, and every customer got the same blunt service. Missed warnings ended in goodwill credits.

What we did

We redesigned the process and replaced the free-text note with structured preference fields. With those in place the alerts could be automated, the service was easy to manage, and the business could offer tiered and premium alerting.

How it was measured

Agent time saved is the manual warning work removed over a year. Credits avoided are goodwill credits that missed warnings would otherwise have cost.

What customers could now choose

  • ThresholdWhen to raise an alert
  • FrequencyHow often to be told
  • ChannelHow alerts arrive
  • ContactsWho gets alerted
  • Auto-restrictCap usage, or not
  1. 01DetectDaily usage spike report
  2. 02MatchFinds the customer account
  3. 03Read settingsThreshold, contacts, channel
  4. 04AlertRight people, right way
  5. 05RestrictCaps usage if chosen

Read the full story of the usage alerts case

Case 04Telecoms

Stalled orders

Stalled orders, brought back to finish

€200k+

Annual recurring revenue recovered

  • Manual to AutomaticCustomer follow-up on stalled orders

The problem

New bill-pay customers had to upload identity and address documents before their order could go live. Many stopped at that step and never came back. Each dropped order was a contract, and its monthly revenue, lost after the sale was already made.

What we built

We built an automation that finds orders stuck at document checks and reminds customers to finish.

How it was measured

There's no before figure for revenue recovered. Until the automation, orders that stalled at the document step were lost. The €200k+ is the annual recurring revenue of the orders it brought back.

  1. 01DetectOrders waiting on documents
  2. 02RemindPrompt to upload
  3. 03Follow upRepeat at set intervals
  4. 04ActivateVerified and live

Case 02Telecoms

Supplier invoices

Supplier invoices, read and posted without retyping

From 6 min est. to 1 min est.

Handling time per invoice

  • 2,000 hrs/yr est.Data entry eliminated
  • Under 1% est.Keying error rate

The problem

Supplier invoices arrived by email as PDFs in dozens of layouts. The accounts payable team keyed each one into the finance system by hand, then chased mismatches against purchase orders. It was slow, and every typo turned into a payment problem later.

What we built

Invoices are read automatically, matched to the purchase order, and posted. Anything the reader is unsure of goes to a person.

How it was measured

Every figure in this case is an estimate, marked est.

  1. 01CaptureAP inbox, any layout
  2. 02AI extractionSupplier, totals, VAT, PO
  3. 03ValidationMatched to the PO
  4. 04ExceptionsLow-confidence fields to a person
  5. 05PostingStraight to the finance system

Three projects

Restoration services

Case 03Restoration services

Daily job review

One report replaces a morning of clicking

From 3 hrs est. to 20 min est.

Daily job review

  • 600+ hrs/yr est.Freed for one project manager
  • 1 reportReplaces dozens of job files

The problem

A project manager started every day by opening each active water mitigation job, one by one, to read the notes, dates and tasks. It took most of the morning. The standard report showed only the latest note, so the full picture was spread across dozens of files.

What we built

We built one report listing every active job with its full notes. An AI assistant reads it and ranks the day's actions.

How it was measured

The review used to take most of the morning. The 3 hours, 20 minutes and hours freed are all estimates, marked est.

  1. 01Job systemEvery active job
  2. 02Consolidated reportAll notes, one table
  3. 03Safe exportAccess codes left out
  4. 04AI rankingUrgency and next step
  5. 05Action listOne page a day

Case 07Restoration services

Equipment billing

Idle equipment, back on the invoice

$30k/yr

Additional revenue

  • Ad hoc to Daily est.Equipment utilisation check

The problem

Drying equipment only earns money when it's on a job and on the invoice. Kit sat idle in the warehouse, or went to site without matching the job's needs, and billable equipment days went unclaimed.

What we built

We built utilisation reporting, crew reminders and a simple process to match equipment to each job and bill for it.

How it was measured

Additional revenue is a year of billable equipment days that used to go unclaimed. The move from ad hoc to daily checks is an estimate, marked est.

  1. 01TrackKit logged to jobs
  2. 02ReportIdle kit flagged daily
  3. 03MatchThe kit suits the job
  4. 04RemindCrews log every move
  5. 05BillEvery day invoiced

Case 08Restoration servicesIn progress

Job estimates

Job estimate drafting, in progress

From 2 hrs to 30 min

Target time to draft an estimate

  • Each estimateWill be checked by a person before it goes out

The problem

Every new job needs an estimate, and building one from site notes, photos and measurements takes an experienced person hours. Estimates queue up behind site work, which delays both the start of each job and payment for it.

What we're building

We're building a tool that drafts each estimate from the job inputs and asks a person for anything it can't work out.

How it will be measured

This project is still in progress. The 30 minutes is a target, not a result.

  1. 01Job inputsLoss, rooms, measurements
  2. 02AI draftLine items proposed
  3. 03Human inputEstimator fills the gaps
  4. 04ReviewFinal check
  5. 05SendTo customer or insurer

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