Skip the 12-month speculative roadmaps. Build instead. We ship fully functional, clickable AI prototypes integrated with your real enterprise data in 4 to 8 weeks, so you can validate ROI before you commit to scaling across the organization.
Validate your ROI before you commit production budget.
Most AI proof of concept services hand you a backend API endpoint, a Jupyter notebook, and a promise. You need something your operations team can log into, stress-test against your own data, and present to your CFO with a straight face.
That is the difference between a demo and a decision. A prototype that never touches your real data cannot tell you whether the use case survives contact with your actual schemas, latency, and edge cases. Ours does, from day one.
Your prototype connects directly to your staging ERP, CRM, or data warehouse from day one. Sanitized demo datasets hide the exact edge cases: malformed records, inconsistent schemas, and latency spikes that kill AI projects in production. We test against the real thing.
We build on self-hosted architectures, including n8n and locally deployed LLMs, so your data never touches a third-party inference API. No per-task billing surprises. No GDPR or HIPAA exposure. Complete auditability inside your own security perimeter.
Every engagement delivers a working software interface your team can operate directly, not a static wireframe or an isolated API call. Stakeholders can interact with the system and stress-test edge cases before a single dollar of production budget is committed.
Each prototype ships with a structured report covering latency benchmarks, extraction accuracy, token cost projections, and total cost of ownership at scale. Proceed, pivot, or kill based on verifiable data, not consultant intuition.
CFOs are no longer permitting open-ended AI exploration budgets, with CFOs actively auditing enterprise AI spending to demand hard ROI. Enterprise boards now require that every AI initiative connect directly to measurable operational outcomes, and they are pulling funding from pilots that cannot demonstrate traction within a single quarter. Gartner predicts that 30% of generative AI projects will be abandoned after proof of concept by the end of 2025. Traditional global systems integrators have not adapted. They still sell 12-to-24-month engagement contracts before a single line of production code gets written.
Our AI consulting services are structured around a fundamentally different premise: the only credible way to validate an AI use case is to build it, connect it to your actual data, and measure what happens. Our 30-day deployment commitment exists precisely because the slide-deck phase has a well-documented body count.
Where traditional pilots stall is in the sandbox. An isolated test environment will never surface the legacy system latency, real-time data drift, or messy user workflows that determine whether an AI agent functions at scale. Our Production Probation model deploys the prototype into a live, production-like environment under strict real-time monitoring, exposing it to operational reality rather than curated conditions. Early enterprise AI solutions validation at this level prevents the two most common failure modes: runaway inference costs and integration failures that only appear under genuine load.
of enterprise AI initiatives were completely abandoned in 2025 due to stalled pilots and lack of rapid validation. (S&P Global Market Intelligence)
of enterprise AI pilots fail to reach wide-scale operational deployment due to infrastructure bottlenecks and data pipeline limitations. (IDC / CIO.com)
of generative AI proof of concepts fail to deliver a measurable financial return when built in isolated sandboxes. (MIT Project NANDA)
These benchmarks demonstrate that testing AI models in isolated environments fails to account for real-world integration challenges.
We deploy a dedicated, cross-functional pod: an AI Architect, an Integration Engineer, and a UX Designer, working as a direct extension of your team. Using n8n for orchestration and enterprise-grade LLM frameworks for model deployment, we route around internal engineering bottlenecks. By week six, a fully functional, secure agentic workflow is running inside your environment and ready for executive evaluation. See our four-stage process.
We conduct a rapid data readiness audit, map your existing integration surfaces, and define binary success criteria before a single line of code is written. "Binary" is deliberate: success means achieving greater than 90% extraction accuracy on your document corpus, or it does not. Ambiguous KPIs are how pilots become indefinite.
Engineering effort concentrates almost entirely on backend functionality: data ingestion pipelines, prompt architecture, vector database indexing, and API connections to your live systems. The frontend is deliberately minimal at this stage: a functional interface, not a polished product. Resources go where they produce signal, not aesthetics.
The prototype runs against your Week 1 KPIs. We produce a cost-benefit analysis projecting TCO at production scale, then deliver a binary recommendation: scale it, pivot the architecture, or terminate before sunk costs compound. No hedged language. No "further discovery required."
A Dutch Odoo implementation partner had spent a full year trying to build automated appointment scheduling natively in Odoo for a dental care group serving 25,000 dependent residents across the Netherlands. The ERP alone could not solve it. Rather than force the scheduling logic into Odoo, we prototyped an AI-powered n8n orchestration layer on top of the existing ERP, with Supabase as a high-speed planning cache.
Scheduling across 200 practitioners, multiple locations, treatment series with healing intervals, role matching, and urgency limits created a web of interdependent rules Odoo's data model was never designed to handle.
A cloud-native n8n engine runs nightly between Odoo and Supabase: five sync workflows feed a multi-stage matching algorithm, an AI agent selects optimal time slots, and failed placements generate detailed reports explaining exactly why.
We inverted the logic: instead of mapping when practitioners are available, we map when they are unavailable. Holidays, vacations, and booked slots become simple blocks the AI checks yes/no, making the system fast and resilient to change.
Appointments automated per week, processed nightly
Practitioners coordinated across multiple locations
Dependent residents served across the network
Real-time sync 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.
Stop deliberating. Start kicking the tires.
Define your success criteria and map your integration surfaces with an AI engineer.
You own the prototype and all associated intellectual property outright: there are no licensing fees, no usage restrictions, and no dependency on Ovidius.ai to keep it running. We architect every prototype using clean, modular components: self-hosted n8n workflows, standard REST APIs, and documented integration layers that any capable engineering team can extend.
From there, you have three practical paths: use it as a functional specification for your internal IT team, scaffold it directly into a production build, or transition it into a maintained production system with our ongoing support. The decision is yours, and the asset is yours. No strings attached.
Security is an architectural constraint, not an afterthought. Every prototype is deployed within your own secure cloud environment (AWS, Azure, or GCP) or on your self-hosted infrastructure, depending on your compliance posture. Your data never passes through a third-party inference API, which means there is no exposure surface for GDPR, HIPAA, or SOC 2 violations.
We configure role-based access controls, audit logging, and data residency guardrails during Week 1 of the sprint, before any model training or pipeline construction begins, ensuring complete alignment with your internal security policies.
Connecting modern LLMs and agentic workflows to legacy enterprise systems is one of the core technical capabilities we have built across 100+ deployments. Whether the requirement is an SAP AI consultant to bridge invoice data, a Salesforce integration for customer operations automation, or a custom n8n orchestration layer for document processing, we build the connectors rather than waiting for native vendor support.
We also help you evaluate custom builds against commercial off-the-shelf AI features like SAP AI, Salesforce Einstein, or Microsoft Copilot, ensuring the engagement produces a defensible build-versus-buy recommendation, not just code.
We operate on a clear, fixed-price model scoped to the complexity of the integrations and user journeys involved. Most enterprise prototyping engagements fall between $35,000 and $75,000, covering a single core use case and full integration with a secure data environment.
For context, that range sits squarely within what most global consulting firms charge for a static discovery phase that produces a slide deck rather than functional software your team can test directly. Explore the full range of what that investment delivers across our AI solutions.
Build real tools. Partner with Ovidius.ai to build, test, and validate real AI capabilities at enterprise scale, fast.
Validate your enterprise AI investment without committing to long-term consulting contracts.