Our AI consultants scope one workflow with you, build it inside your systems on your own data, and keep it running after launch. GoKickflip's content pipeline was built and shipped in 30 days.
Jason, our COO, runs the call and scopes the workflow with you.
Clients include
Each one is written up in full on its case page, with what the work cost or took before and what it costs or takes now. See all case studies
E-commerce and SaaS, content pipelineThree agents on n8n research, brief and write each article with a multi-model stack, so output scaled without hiring. Built and shipped in 30 days.
From day one, the Ovidius team moved fast, thought big, and executed with precision.”
B2B e-commerce, chat agentof AI projects fail, roughly double the rate of IT projects without AI
Source: RAND Corporation, The Root Causes of Failure for AI Projects (2024)Each stall has a cause, and each cause has an engineering answer we build in from the first week.
95% of generative AI pilots deliver no measurable P&L impact. We build against your real systems and data, inside your environment, from the first week.
46% of AI proofs of concept are scrapped before production. The team that builds your workflow runs it after launch, with retries and alerts from the first run.
We record execution volume, error rates, hours spent and cost per transaction before launch, then report the same numbers after it.
We train the people who use the workflow and name an owner for each step before go-live, so it becomes part of how your team works.
Sources: MIT NANDA, The GenAI Divide: State of AI in Business 2025; S&P Global Market Intelligence, Voice of the Enterprise: AI & Machine Learning (2025).
AI advisory services and the engineering and adoption work behind them, from one team that works as an extension of yours.
One workflow, the systems it touches and what it will take, written down and agreed before you commit.
Self-hosted n8n on your AWS, Azure or GCP account, behind your own auth. Your data stays where it is.
Agents read and write your CRM, ERP and databases through their APIs, with the permissions your people already have.
Large models from providers such as OpenAI and Anthropic for reasoning, and smaller open-weight models where speed and cost per call matter more.
Error handling, automatic retries, and Slack and email alerts, so your team hears about a failed step as it happens.
Training for the people who use it, a named owner for each step, and weekly sprints after launch that you direct.
The same team carries your workflow from the first call to the system running in production, so nothing is lost in a handoff. See how we work
One workflow, the systems it touches and what it will take, agreed before you commit. Owen joins the first calls until you are onboarded.
Built against your real data, in your environment, as production software from the first week.
Into production behind your auth, your governance and your monitoring, with your team trained on it.
Watched, fixed and extended in weekly sprints by the engineers who built it.
Every build comes with the same five answers: what you receive, who builds it, where it runs, how it is priced and who runs it after launch.
| What you get | |
|---|---|
| You receive | Workflows running in production |
| Who builds it | The team that scoped it |
| Where it runs | Your cloud, behind your auth |
| Pricing | What it will take, agreed before you sign |
| After launch | Run and improved by the people who built it |
Every build starts with a written scope: the workflow, the systems it touches and what it will take. You see the cost before you sign, and each milestone is defined as working software. If you start with the AI Audit, its fee is credited toward your first build.
FTI Consulting reports that Colorado's AI Act (SB 24-205) requires disclosure and impact assessments for high-risk AI systems, California's AB 2013 requires training data transparency, and Texas TRAIGA sets penalties of $10,000 to $200,000 per violation. We treat those as engineering requirements:
Yes, through the AI Audit: eight phases across strategy, operations, data and systems, ending in a ranked 90-day roadmap you own. Every other engagement ends in software running in your environment, maintained by the engineers who built it.
Our AI consultants find the workflow worth automating, write the scope, build it on n8n inside your cloud and run it after launch. Jason runs the first call and the scope, and the engineers who build your workflow stay on it in production.
Every build is priced in a written scope before you sign, with each milestone defined as working software. If you start with the AI Audit, it costs $10,000 to $100,000 depending on scope, takes 4 to 8 weeks, and its fee is credited toward your first build.
We scope the workflow and tell you the timeline before you commit. For a sense of pace, GoKickflip's pipeline was built and shipped in 30 days and saves an estimated $358K a year at equal volume.
No. We build on self-hosted n8n in your cloud, behind your own auth, and agents read your systems through their APIs with the permissions your people already have.
The one that suits the task. GoKickflip's pipeline runs about 33 agent executions across six or more models for each article, and the routing is part of what we maintain.
Self-hosted n8n runs in your own cloud, so operational data stays off third-party servers, and you are not billed per task as you are on Zapier. See our n8n agency page for what we build on it.
The team that built it runs it: monitoring, fixes and the next use case, in weekly sprints that you direct.
Answer a few questions about your team and what you want automated, then Jason scopes it with you on the call.
