Use Cases

Where Lean AI delivers ROI

Boxtree engagements start with a problem, not a product. Below are two illustrative projects, based on real operations we have assessed, showing where our Lean AI approach could release capacity and cut cost. The scenarios are anonymised and the savings are projections, costed conservatively at the UK National Living Wage, so the true return is typically higher.

National hospitality group · Invoice processing

Turning a 225-invoice backlog into a seconds-long batch check

A national multi-site hospitality group validates thousands of contractor maintenance invoices a year. Before any invoice is paid, it has to be checked against its work order and the supplier's contract.

The challenge

For every invoice, a reviewer opened three documents, the invoice, the work order and the contract, and manually cross-checked seven areas: parts, consumables, call-out charges, contract rates, capex versus revenue, code alignment and labour hours. Around one in five invoices are complex and can take up to 25 minutes. With roughly 2,000 invoices a week to check, it takes a full-time team of six, and even then they were permanently behind, working through a standing backlog. Holding three documents in your head also makes discrepancies easy to miss.

Current process~2,000 invoices a week · checked by a team of six
📥
Invoice arrives
In the facilities system
📑
Open 3 documents
Invoice · work order · contract
🔍
Manual cross-check
Hold all three in context
Check 7 areas
Parts, rates, call-outs, codes…
Decide & route
Reject · approve · escalate

The Lean AI solution

Boxtree would build an agentic AI workflow that pulls every unapproved invoice from the facilities system and automatically matches each one to its work order (by job reference) and contract (by supplier and region), in bulk, in seconds. Each invoice is then risk-scored: clean matches are auto-cleared, while anomalies are flagged showing exactly where the issue is and what is already correct. A person would then review only what genuinely needs judgement, in around eight minutes, with the cross-checking already done.

With Lean AIA whole week's batch checked in seconds
📥
Invoice arrives
Pulled automatically
AI
Batch pull & match
Auto-matched to its docs
AI
🚒
Risk score & flag
High · medium · low
👤
Human review
Only flagged invoices
Decide & route
Same outcomes
Low → auto-cleared
Medium → spot check
High → human review

The projected result

6 → 1–2
people on invoice checking
~76%
less checking effort
~£114,000
projected saving / year*
2,000/wk
checked in one batch

The same 2,000-invoice-a-week workload would need only one or two people instead of six, freeing around four and a half full-time roles to redeploy, or take as a projected saving of around £114,000 a year at the National Living Wage (and far more against actual finance-team cost). The standing backlog would clear, and because every invoice would be checked to the same standard, overcharges that manual spot-checking might miss would be surfaced too.

UK animal welfare charity · Animal intake

Giving a front desk back nearly a person-day, every day

At a UK animal welfare charity's hospital, every animal that arrives must be logged before it can be treated, around 40 admissions a day, every day of the year.

The challenge

An officer read each animal's details aloud to the reception team, who manually keyed them into the system, handwrote an intake sheet, rescanned it back in, attached it to the animal's record and printed the notes. That is about 10 minutes of reception time per animal, slow, repetitive, error-prone, and capacity that could be spent on animals and people rather than re-keying and rescanning.

Current process~10 minutes of reception time per animal
📥
Animal arrives
🗣
Read to reception
2 min
Keyed into system
2 min
Handwrite & rescan
4 min
📎
Attach & print
2 min

The Lean AI solution

Boxtree would replace the manual hand-off with an AI intake assistant. The officer would read the animal's details straight to the AI, which enters them into the system, populates and prints the intake sheet, attaches it to the record and prints the notes, in around 40 seconds. Reception would be taken out of the data-entry loop entirely, with the same outputs produced at the end.

With Lean AI~40 seconds, reception out of the loop
📥
Animal arrives
🗣
Read to the AI
2 min · officer
AI
🤖
AI logs everything
Enter · sheet · attach · ~30s
AI
🖨
Notes printed
~10s

The projected result

10 min → 40 sec
per animal
~6.7 hrs
freed every day
~2,430 hrs
released per year
~£30,900
projected saving / year*

Across 40 animals a day that would be close to a full person-day of reception capacity returned daily, which the charity could take as a direct cost saving, or redeploy to animal care. Intake would also be faster and cleaner, with no re-keying or rescanning to introduce errors.

* Projected time savings costed at the UK National Living Wage (£12.71/hour, 21+). The roles involved typically cost more, so these are conservative figures.

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