Enterprise AI proof of concept services that run on your real data

We build a working, clickable AI prototype connected to your staging ERP, CRM or data warehouse in 4 to 8 weeks, so your CFO sees measured results before anyone commits production budget.

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Bypassing the traditional AI pilot trap

Most AI proof of concept services hand you an API endpoint, a Jupyter notebook and a promise. Your operations team needs something they can log into and test against your own data, and your CFO needs results they can defend.

• Real-data integrationYour prototype connects to your staging ERP, CRM or data warehouse from day one. Sanitized demo datasets hide the malformed records, inconsistent schemas and latency spikes that stop AI projects in production, so we test against the real thing.
• Self-hosted and secureWe build on self-hosted n8n and locally deployed LLMs, so your data never passes through a third-party inference API. There is no per-task billing, and every run stays auditable inside your own security perimeter.
• A clickable, working interfaceEvery engagement delivers software your team operates directly, where a wireframe or an isolated API call would leave them guessing. Stakeholders test edge cases before any production budget is committed.
• Clear go/no-go metricsEach prototype ships with a report covering latency benchmarks, extraction accuracy, token cost projections and total cost of ownership at scale. You decide to proceed, pivot or stop on measured data.

A prototype that never touches your real data cannot tell you whether the use case survives your actual schemas, latency and edge cases. Ours runs on that data from the first day.

The death of the "experimentation" phase

CFOs are auditing enterprise AI spending and pulling funding from pilots that cannot show traction within a quarter.

Gartner predicted in July 2024 that 30% of generative AI projects would be abandoned after proof of concept by the end of 2025. Large systems integrators still sell 12-to-24-month engagements before any production code is written. Our AI consulting services start from a different premise: the only credible way to validate an AI use case is to build it, connect it to your data and measure what happens.

Pilots stall in the sandbox, because an isolated test environment never surfaces the legacy-system latency, data drift and messy user workflows that decide whether an AI agent works at scale. Our Production Probation model deploys the prototype into a live, production-like environment under real-time monitoring. Validating enterprise AI solutions at this level catches the two most common failure modes early: runaway inference costs, and integration failures that only appear under real load.

From concept to clickable prototype in 4 to 8 weeks

A dedicated pod of an AI Architect, an Integration Engineer and a UX Designer works as an extension of your team. We orchestrate with n8n and deploy models on enterprise-grade LLM frameworks, which routes the work around internal engineering bottlenecks. By week six, a secure agentic workflow runs inside your environment, ready for executive evaluation. See our four-stage process.

1. Week 1Setup and scopeWe review your data readiness, map your integration surfaces and define binary success criteria before any code is written. A binary criterion is a target such as over 90% extraction accuracy on your document corpus, which the prototype either meets or misses, so the pilot has a finish line from the start.
2. Weeks 2 to 3MVP build and refinementEngineering goes into the backend: data ingestion pipelines, prompt architecture, vector database indexing and API connections to your live systems. The interface stays deliberately minimal at this stage, because the backend is where the go/no-go signal comes from.
3. Weeks 4 to 6Evaluation and decisionThe prototype runs against your Week 1 criteria. We project total cost of ownership at production scale and give you a direct recommendation: scale it, change the architecture, or stop before sunk costs grow.

How an AI scheduling engine on Odoo and n8n replaced a year of failed builds

The challenge

The partner had spent a full year trying to build automated appointment scheduling natively in Odoo. Scheduling across 200 practitioners, multiple locations, treatment series with healing intervals, role matching and urgency limits created a web of interdependent rules that Odoo's data model was never designed to handle.

The solution

We left the scheduling logic out of Odoo and prototyped an AI-powered n8n orchestration layer on top of the existing ERP, with Supabase as a high-speed planning cache. Every night, five sync workflows feed a multi-stage matching algorithm, an AI agent selects the best time slots, and any failed placement produces a report explaining why.

The innovation

We inverted the logic. The engine maps when practitioners are unavailable, so holidays, vacations and booked slots become simple blocks the AI checks yes or no. That keeps the system fast, and a change to anyone's calendar is one new block.

The client named Ovidius their preferred AI delivery partner, with a retainer covering ongoing support and Phase 2 development. More outcomes like this are in our case studies.

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.

Leadership, Dutch Odoo Implementation Partner

Traditional AI consulting vs. Ovidius.ai rapid prototyping

Frequently asked questions about our AI proof of concept services

Ready to test your AI use case on real data?

Bring one use case to a discovery call. An AI engineer will help you define the success criteria and map the integration surfaces, so you know what a prototype would prove before you commit any budget.

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