If one of these is you, we have the solution. If none of them are, we'll build one around your problem. No commitment.
The day disappears into tasks technology solved years ago. You know it. Your team feels it.
Your accounts team checks every purchase order, delivery note and invoice against each other in Excel. Before: 6-8 hours a week per person. After: AI matches all three, clears the exact matches and only flags what needs a human eye.
Your warehouse team types every delivery note into a spreadsheet or the ERP. Before: delays, typos and paperwork that goes missing. After: they snap the note with a phone, AI reads it, checks it against the open PO and books the goods in.
Every new supplier sends around 15 documents: ISO certificates, insurance, tax and HSE records. Before: someone reads each one and types it into a spreadsheet. After: an AI agent pulls out the data, checks expiry dates and builds the checklist for you.
50+ contractors arriving in a single week, each with their own permits, certifications and sequence of work. Before: one giant master spreadsheet, frantic calls on day 1, crews standing around. After: a single console with the schedule, HSE permits, spare parts and a checklist for every contractor.
Every operator wants its own document pack: insurance, permits, training records, personnel files. Before: 3-6 months of largely manual work and a dedicated admin team. After: templates for each operator, automatic checks and real-time tracking. Every new approval starts with 80% of the work already done.
The information is there, and you know it. But getting hold of it takes a chain of requests nobody has time for, so you end up deciding blind.
20% of your suppliers account for 80% of your spend. Before: you only audit the big ones, and the rest flies under the radar. After: an AI agent reviews documents and invoices from every supplier in real time and warns you before small issues become big ones.
The system says one thing, the warehouse another, and the operator on the line a third. Before: you review every SKU, every week. After: an agent cross-checks usage, lead times and actual stock, and only flags the items that need attention.
A supplier sends a quote and you suspect it's inflated. Before: you negotiate on gut feel, or chase three more quotes to compare. After: a should-cost model built from raw materials, logistics and labor, fed by live market indices. You negotiate with facts.
You ask for a report on Tuesday and get it Friday at 7pm. Before: someone pieces it together by hand from five-plus systems. After: an agent queries your systems, joins up the data and has the report waiting for you at 8am every day.
You export to 12 markets. Every payment lands with its own exchange rate, withholding taxes, bank charges and timing differences. Before: days of spreadsheet reconciliation, typos and settlements that never quite balance. After: an agent matches invoices, bank receipts and the day's exchange rate, spots the differences and only escalates the exceptions.
You have capable people stuck doing repetitive work because nobody has the time to automate it. Hiring won't fix that. Automating the repetitive work will.
Every new contractor means 15 documents, 5 checks and 3 signatures. Multiply that by 50 contractors on a turnaround. Before: HR spends three days on each one. After: contractors upload their own paperwork, AI checks expiry dates and certifications, and HR only handles the exceptions.
Your analyst spends half the morning copying numbers from five systems into PowerPoint. Before: the daily report eats four hours of someone's day. After: an agent pulls from your systems and has the report ready to present at 8am.
An external audit is on the calendar. Before: your team pulls together 200 folders, checks expiry dates one by one and writes up reports. Two weeks of lost productivity. After: AI keeps every document validated in real time, so when the auditor arrives, you're ready.
The annual inventory count: plant shut, the whole team counting, errors in the spreadsheets. Before: three days of lost operations. After: a system that decides what to count first based on turnover and value, guides the operator and records counts in real time.
You hold several certifications so you can work with large customers, at a cost of USD 10,000-30,000 a year, mostly in staff time. Before: someone dedicated to gathering evidence, cross-checking spreadsheets and preparing for audits. After: digital evidence validated in real time, a dashboard for each standard, and a team freed up for real work.
Your customers want you to double or triple capacity. But every new customer costs twice as much to serve internally, and information reaches decision-makers too late.
Your biggest customer wants you to triple capacity within two years. Before: hire more people in procurement, billing and the warehouse. After: automate the repetitive work, so your current team handles three times the volume without the operation buckling.
A new customer means certifications, integrations, custom processes and their own paperwork. Before: six months before they're profitable. After: AI onboarding templates, automatic document checks and dashboards live within weeks.
You're choosing which make of pickup truck to buy for the fleet. Before: nobody compared maintenance history against the alternative. The data existed, but getting hold of it was impossible. After: an agent pulls from five-plus systems and gives you the answer before you commit.
Or something close to it? Let's talk. In 30 minutes we'll tell you whether we can solve it and how, and you'll come away with practical suggestions either way.