An AI proof of concept that runs on your real data before you commit the budget

In 2 to 6 weeks we build a working prototype connected to your staging ERP, CRM or data warehouse, then hand you a go/no-go report on accuracy, latency, token cost and total cost of ownership. It covers one use case, at a fixed price agreed before you sign, and the fee is credited toward your production build.

Book a Discovery Call See the Odoo scheduling pilot

Jason, our COO, runs the call and helps you set the pass mark for your use case.

Example prototypeInvoice intake, on staging data

INV-20418PO matched, amount matchedPosted to ERP
INV-20419PO matched, VAT line differsFlagged for review
INV-20420PO matched, amount matchedPosted to ERP

Pass mark, set in week oneExtraction accuracy on your documents

Over 90%

Clients include

What you hold when the pilot ends

Software your operations team has used on your own records, and the numbers your CFO needs to fund the build or stop it.

Real data

A prototype wired to your staging systems

Connected to your staging ERP, CRM or data warehouse from the first week, so malformed records, inconsistent schemas and slow APIs show up during the pilot instead of after launch.

Interface

A screen your team logs into

Your operations team tests edge cases in a clickable interface, so stakeholders judge the use case on what it does with their own work.

Report

A go/no-go report with four measures

Accuracy against the pass mark, latency under real load, token cost at your expected volume and total cost of ownership at scale.

Ownership

The code and the IP, yours outright

No licensing fees and no dependency on us to keep it running. The prototype is built from self-hosted n8n workflows, standard REST APIs and documented integration layers that any capable engineering team can extend.

Scale, pivot or stop, decided on measured numbers

Every pilot ends in the same report. You read the four measures against the pass mark you set in the first week, and we give you a direct recommendation.

Accuracy

Measured on your own document corpus or records

Against the pass mark

Latency

Under real load, against your staging systems

Production-like

Token cost

Projected at your expected production volume

Monthly

Total cost of ownership

Hosting, models and upkeep, projected before you commit

At scale

Scale

The pass mark is met. The use case moves to a production build.

Pivot

The use case holds and the architecture changes.

Stop

You end it here, before sunk costs grow.

Three stages from pass mark to recommendation

An AI architect, an integration engineer and a UX designer work as an extension of your team. The pilot takes 2 to 6 weeks, set by how many systems the use case touches, and you see the length before you sign.

01
First week
Set the pass mark
We review your data, map the integration surfaces and agree a binary success criterion before any code is written, such as over 90% extraction accuracy on your document corpus.
- Role-based access, audit logging and data residency configured first
- Matched to your internal security policies
02
Build
Build the backend on your data
Data ingestion, prompt architecture, vector indexing and API connections to your systems. The interface stays minimal, because the go/no-go signal comes from the backend.
- Self-hosted n8n inside your cloud
- Hosted or locally deployed models, chosen per workload
03
Final weeks
Evaluate and recommend
The prototype runs against the pass mark in a production-like environment under monitoring, which is where runaway inference costs and failures under real load show up.
- The go/no-go report
- A recommendation to scale, pivot or stop

A year of failed scheduling builds in Odoo, replaced in weeks

A Dutch Odoo implementation partner, building for a dental care group that serves 25,000 dependent residents across the Netherlands.

Dutch Odoo implementation partnerHealthcare, appointment scheduling

1,500

Appointments scheduled a week, processed nightly

The scheduling logic moved out of the ERP and into an n8n layer with an AI agent

The partner had spent a full year building scheduling natively in Odoo. Treatment series with healing intervals, role matching and urgency limits across 200 practitioners outgrew the ERP's data model. We prototyped an n8n orchestration layer on top of Odoo with Supabase as a planning cache. Every night five sync workflows feed a multi-stage matching step, an AI agent picks the best slot, and each failed placement produces a report saying why.

The engine maps when practitioners are unavailable, so holidays, vacations and booked slots become blocks the agent checks yes or no, and a calendar change is one new block. The client named Ovidius its preferred AI delivery partner, on a retainer for support and Phase 2.

200practitioners across multiple locations

25Kdependent residents served

5sync workflows between Odoo and Supabase

Ovidius did in weeks what we couldn't build natively in Odoo for a full year. They architected an AI-powered n8n layer on top of our ERP that now schedules well over a thousand appointments a week automatically. They are our preferred AI delivery partner.”

Why a sandbox demo cannot fund a build

88%

of enterprise AI pilots never reach wide-scale deployment, held back by infrastructure and data pipeline limits

Source: IDC, reported by CIO.com

CFOs are pulling funding from pilots that cannot show traction within a quarter, and a demo on sanitized data shows nothing about your schemas, latency or edge cases.

42% of companies abandoned most of their AI initiatives in 2025

A pilot that ends in a costed recommendation gives you a reason to continue or an early place to stop.

95% of generative AI pilots deliver no measurable financial return

We set the pass mark in week one, so the pilot has a finish line and a number to report against.

30% of generative AI projects abandoned after proof of concept

Gartner predicted this in July 2024 for the end of 2025. Our report projects token cost and total cost of ownership before anyone signs for production.

Sources: Forbes, CFOs Are Coming for the Enterprise AI Budget (June 2026); S&P Global Market Intelligence (2025); MIT Project NANDA, The GenAI Divide: State of AI in Business 2025; Gartner press release, 29 July 2024.

A demo on sample data against a pilot on yours

Pricing is fixed and scoped to the integrations and user journeys involved. Most pilots fall between $35,000 and $75,000 for one core use case, fully integrated with a secure data environment, and the fee is credited toward the production build if you go ahead.

	Ovidius pilot	Demo on sample data
Data	Your staging ERP, CRM or warehouse	A cleaned or synthetic dataset
Deliverable	Working software your team operates	An API endpoint or a notebook
Measured	Accuracy, latency, token cost and TCO	Whether the model can do the task
Price	Fixed, agreed before you sign, credited toward the build	Depends on the vendor
Ends with	A recommendation to scale, pivot or stop	A demo

Questions buyers ask before they fund an AI pilot

Ask us something else

What does an AI proof of concept cost?

Most pilots fall between $35,000 and $75,000, fixed and scoped to the integrations and user journeys involved. The price covers one core use case and full integration with a secure data environment, and the fee is credited toward the production build if you go ahead. See what that build can be across our solutions.

How long does an AI pilot take?

A pilot takes 2 to 6 weeks. The length depends on how many systems the use case touches, and you see it in the written scope before you sign.

What happens to the prototype after the pilot?

You own it outright. Hand it to your IT team as a working specification, take it into a production build with us, or move it into a maintained system under our AI managed services.

Can the pilot connect to SAP, Salesforce or Odoo?

Yes. We build the connectors through each system's APIs, as on the Odoo scheduling pilot above, and compare the custom build with features such as SAP AI or Salesforce Einstein so you end with a build-versus-buy recommendation. Our n8n agency page covers the orchestration layer.

How is our data protected during the pilot?

The prototype runs in your own AWS, Azure or GCP account or on your own infrastructure, so GDPR, HIPAA and SOC 2 scope stays inside systems your compliance team already governs. Access controls, audit logging and residency rules are set in the first week.

Should we start with the pilot or the AI Audit?

Start with the pilot when you already know the use case. Start with the AI Audit when you need to find and rank the use cases first: it takes 4 to 8 weeks, ends in a ranked 90-day roadmap, and its fee is credited toward your first build.

Before and after the pilot

Check

AI readiness assessment

Ten questions across data, systems, workflows, people and governance, to answer before you pick a use case.

See the assessment

Plan

Enterprise AI roadmap

The ranked 90-day roadmap the AI Audit ends in, showing where the pilot's use case sits among the quick wins and strategic bets.

See the roadmap

Build

AI consulting

The production build after a passed pilot, run and improved by the team that built it.

See AI consulting

Put your first workflow on the board

Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.

Owen, Maciej, Jason, Oskar and Ben