Quoting and site survey support
Turning survey notes and floor areas into a consistent, costed specification using your own productivity rates, so quotes stop varying by whoever visited.
Industries
Cleaning contracts run on rotas, evidence and tenders
Contract cleaning is a people-scheduling business with a paperwork problem attached: cover to arrange, right-to-work to verify, audits to evidence and tenders to answer. Almost none of that is cleaning, and almost all of it is automatable.
The pressure right now
What we hear from cleaning businesses
- Daily cover chasing when staff call in, absorbing the whole operations desk
- Quoting from site surveys that are inconsistent between estimators
- Onboarding and right-to-work checks slowing every new site mobilisation
- Clients asking for audit evidence and service reports you assemble by hand
- Tender and PQQ responses rewritten from scratch each time
Where it pays off
Six places AI earns its keep here
Not everything on this list will apply to you. Most organisations start with one and extend once it has been measured.
Rota and cover management
Drafting the cover plan when someone calls in - by proximity, competence and hours worked - and preparing the messages, with the final call staying with a supervisor.
Onboarding and compliance packs
Chasing and assembling the documentation for new starters and site mobilisations, flagging what is missing before day one rather than after.
Audit and quality evidence
Turning site audit notes and photographs into the client-facing report against the contract specification, consistently and on time.
Client reporting and queries
Monthly service reports assembled from your existing data, and quick, accurate answers to the routine client questions that currently interrupt a manager's day.
Tender and PQQ responses
First drafts built from your own library of previous answers, policies and case evidence - the single biggest time sink in winning public sector work.
Where we would start
The first three moves
1
Cover management first
It is the daily fire, it is measurable in hours, and fixing it buys goodwill for everything after.
2
Build the answer library
Once your tender answers, policies and evidence are in one retrievable place, response time collapses.
3
Standardise the quote
One specification method and one rate card before automating - otherwise inconsistency scales.
Risk and regulation
The part most suppliers skip
Where the risk sits
- Right-to-work checks - the legal responsibility and the penalties stay with you
- Working time, rest breaks and minimum wage implications of any automated scheduling
- TUPE information handling when contracts transfer between providers
- Staff personal data across a large, high-turnover, often multilingual workforce
- Contract and client site data covered by confidentiality terms
How we handle it
Scheduling suggestions are checked against working time and rest requirements, and a supervisor confirms every allocation. Algorithmic shift allocation without human oversight is both an employment risk and a retention problem.
Right-to-work document handling is designed to assemble and prompt, never to make the compliance decision - the statutory check remains a named person's responsibility.
Staff communications support the languages your workforce actually uses, which in this sector is usually where the real adoption gain sits.
Where this fits
Where to go next
Questions
Questions from cleaning businesses
Yes, because most of the workload we are addressing sits with your office, supervisors and mobilisation team. Where staff are involved, it is through the channels they already use - typically messaging, not a new app to install.
It can propose allocations with the constraints applied; a supervisor confirms. Fully automated allocation tends to breach working time rules, damage retention, or both.
The first draft is, when it is grounded in your own previous answers and evidence rather than generated from nothing. A bid manager still edits and owns the submission - but they start from 70% rather than a blank page.
Start with an audit of what you already run
Two to four weeks to an evidenced picture of your AI use, spend and risk - and a ranked list of what to do first.