Weeks to months
Compromise 01Consultancy reviews
Rigorous, but slow and costly, and every new question means a new engagement.
MeridianTravel policy intelligence
Know the impact before you change the policy.
Meridian turns your travel policy into a live model of your programme. Adjust any rule and watch cost, CO₂ and traveller disruption update instantly, then see how every term compares with real peer programmes.
Levers
Current policyAnnual programme cost
£11.1Mfrom £12.4M
↓ £1.30M lower · 10.5%
CO₂ a year
−840 t
Disruption
3.2 / 10
Saving by lever
The problem
Policy changes move millions.
Testing them shouldn’t take months.
Tighten a cabin rule, cap a hotel rate or mandate rail, and the effects ripple through cost, carbon and traveller behaviour. Working them out today usually means one of three compromises.
Tied to one agency
Compromise 02TMC analytics
Programme-level what-ifs without lever-level detail, available only to that agency’s clients.
Manual
Compromise 03Spreadsheets
Manual, error-prone and unbenchmarked. Hard to defend in front of a CFO.
Minutes, self-serve
The alternativeMeridian
The rigour of a consultancy review at the speed of a slider.
Self-serve, lever by lever, and independent of any TMC.
How it works
From policy document to board-ready answer
- 01
Upload your policy
Drop in your PDF or Word policy. AI extracts every quantifiable rule and links it to the source text for you to confirm.
- 02
Model the change
Adjust levers in Policy Lab and watch annual cost, CO₂ and disruption move against your baseline in real time.
- 03
Optimise and benchmark
Set a savings target for the optimiser, or align any generous term to your peer benchmark in one click.
- 04
Commit and track
Export a board-ready report, commit to a scenario, then track predicted savings against actual results.
Policy Lab
Move a lever. Watch the whole programme respond.
Change any rule and Meridian instantly recalculates annual programme cost, CO₂ and traveller disruption against your current baseline.
- See which lever drives which saving
- Spot the routes and business units most affected
- Save scenarios and compare them side by side
- Grounded in your own route-level fares, hotel rates and trip volumes
Every rule in your policy becomes a lever: cabin class thresholds, advance booking windows, hotel rate caps by city tier, rail mandates, meal allowances and more.
Savings optimiser
Start from the target. Let Meridian find the route.
Tell Meridian you need 10% off programme cost, and the optimiser recommends the least disruptive way to get there.
- Ranks changes by traveller disruption, not just savings
- Works within the constraints you set
- Says plainly when a target can’t be reached
It searches every combination of levers, so you don’t have to.
Benchmarks
Know where your policy is generous, and where it’s strict.
Compare every policy term with anonymised data from real corporate travel programmes: behavioural data, not survey averages.
- Configurable peer groups
- Generous, in line or strict, term by term
- One-click “align to benchmark” simulation
- No comparison drawn from fewer than five programmes
Choose the peers that matter to you, see where each term sits against them, and simulate the saving from aligning to the benchmark.
AI policy parsing
Start from the policy you already have.
Upload your policy and Meridian’s AI extracts the quantifiable rules and maps them to structured levers, ready to simulate and benchmark.
- PDF, Word or plain text
- Every value traced to its source sentence, page and section
- Uncertain values flagged for review
- Prefer a blank slate? Start from a PredictX baseline template
Every value links back to the sentence it came from, with a confidence rating, so your team reviews and confirms rather than re-keying.
Why Meridian
Numbers that stand up in the boardroom
01
Explainable by design
A deterministic cost model, not a black box. Every figure traces back to fares, rates, volumes and the policy terms you set.
02
Cost and carbon, side by side
CO₂ sits next to cost in every scenario, so you never weigh a saving without its carbon impact.
03
Independent of any TMC
PredictX isn’t a travel management company, so the analysis serves your programme, not a booking channel.
04
Board-ready exports
Export any view as a clean PDF or slides. No more screenshots of dashboards.
The PredictX platform
Design the policy. Forecast the trip. Check the spend.
Meridian You are here
“What should our policy be?”
Policy simulation, optimisation and benchmarking.
Avenue
“How much will this trip cost?”
Trip cost forecasting and budget planning.
DetectX
“Was this expense compliant?”
Expense integrity, fraud detection and compliance.
FAQ
Questions, answered
What data does Meridian run on?
If you’re already a PredictX client, Meridian uses your own historical travel data: route-level fares, hotel rates and trip volumes. New clients can start straight away with cost assumptions drawn from the PredictX benchmark, then switch to their own data once it’s onboarded.
Do we have to upload our policy?
No. You can upload a PDF or Word policy for AI extraction, configure levers by hand, or start from a PredictX baseline template.
How do we know the AI read our policy correctly?
Every extracted value appears next to the text it came from, with a confidence rating, and anything uncertain is flagged for review. Nothing is saved until you confirm it.
How is our data protected in the benchmark?
Peer comparisons use anonymised, aggregated data, and no comparison draws on fewer than five programmes. Uploaded policy documents are processed in memory, with no personal data stored outside your tenant.
How is CO₂ calculated?
With DEFRA-aligned methodology. CO₂ appears alongside cost for every scenario, so you can see the carbon effect of each lever and export it for sustainability reporting.
Which currency does Meridian use?
Your configured base currency. Spend in other currencies is converted using exchange rates updated monthly.
See your policy as a model, not a document.
Book a demo to see how Meridian models cost, carbon and peer position for a programme like yours.