IVP.ai Labs
AI that gets past the pilot and into production.
IVP.ai Labs designs and builds AI and machine learning systems for mid-market operations. We fix the data foundation first, build on your real data, and hand over a system that keeps running after we leave.
For COOs, Operations Directors and PE operating partners.
01 / The problem
Most AI pilots never reach production.
The demo works. Then it meets the business. Customer records live in the CRM, jobs in a field-service system, margins in a finance spreadsheet, and nobody agrees on which number is right. The pilot was built on a cleaned-up extract, so there is nothing for it to run on once the extract goes stale. The model was rarely the problem. The data foundation was never there.
- A proof of concept that impressed the board and was never switched on.
- Reports that still get rebuilt by hand because nobody trusts the source data.
- A consultancy roadmap with no working software at the end of it.
- Automation nobody will sign off, because nobody can see what it decided or why.
02 / Our approach
Foundation first. Working systems, not slide decks.
01
Data foundation first
We connect and model the systems the work actually depends on before building anything clever on top. Most of the risk in an AI project sits here.
02
Production systems, not slide decks
Every phase ends in something running on your real data, with the workings visible, so your team can check it rather than take it on trust.
03
Built on the IVP.ai platform
The data layer, the automations and the governance all live on IVP.ai. When we hand over, you keep a running system your team can operate.
03 / The sprint
The Operations AI Sprint
A fixed-scope, two-to-three-week sprint on your real data. You finish with something working, and a clear answer on whether a bigger build is worth it.
- Duration
- 2–3 weeks
- Scope
- Fixed, agreed up front
- Price
- £4,000 per sprint
- 01
A data and process audit
Where your operational data lives, how good it is, and which manual processes are worth automating first.
- 02
One working automation on real data
Not a prototype on a sample. A live workflow running on your own systems, delivered into the tools your team already uses.
- 03
A costed business case
What a full implementation would deliver, what it would cost, and what it depends on, so you can make the decision on evidence.
Week 1
Audit
- Connect the systems the process depends on
- Map how the work is done today
- Agree the one automation to build
Week 2
Build
- Build it on your real data
- Run it alongside your team
- Check the output against today's
Week 3
Business case
- Cost a full implementation
- Set out what it depends on
- Readout with your leadership team
04 / After the sprint
Full implementation engagements
If the business case stands up, we build the full system with your team: the data foundation, the models or automations, and the checks that let people trust the output. Everything runs on the IVP.ai platform, so when the engagement ends you keep a live, governed system, not a codebase nobody can maintain.
Single source of truth
Operational, financial and customer data joined and governed in one place.
Forecasting and ML
Seasonality-aware forecasts and models built on your own history.
Multi-step automation
Agent workflows across a whole process, with people kept on the decisions that matter.
Controls and audit
Every automated decision recorded with its workings, so it can be checked.
05 / Delivered work
What this looks like in practice.
Fire Safety
Renewal review: days to a single run.
More than 100 contracts priced and reported on in the first live run, with over 90 renewal invoices sent in a single dispatch. No price changes without a person.
Read the case studyFintech
Forecasts in minutes, not months.
Annual forecasting used to take months of the CFO's and CEO's time in Excel. With five-plus tools brought into one platform, forecasts now run in minutes.
Read the case studyFire Safety
One source of truth, four-plus systems.
HubSpot, Uptick and finance spreadsheets brought into one governed view, so leadership sees performance by service line, region and team without manual collation.
Read the case studyNot ready yet?
Not sure you're ready for a sprint?
Take the two-minute AI-readiness assessment. It scores your data, processes and team, and the results give us a useful starting point for a scoping call.
Tell us where the manual work is.
A 30-minute scoping call with the team who would do the work. We'll tell you honestly whether a sprint makes sense, and what it would cover.