How we work

How We Work: AI Implementation From Audit to Production

One senior team scopes your first AI workflow, builds it inside your environment, connects it to the systems your people already use and keeps it running after launch. The people you meet on the discovery call are the people still accountable six months later.

Book a Discovery CallStart with the AI Audit
Four frosted glass blocks on a dark surface, from a flat plan to a finished glowing machine, joined by one orange light line
Scoped first
You know what the build takes before you commit to it
Your environment
Self-hosted in your cloud, behind your own auth
One team throughout
The engineers who scope it build it and run it
Results reported
Every system we ship shows what it saves
Ovidius clients include
AudinatePinkcubeBizIQCourserAllClear Travel Insurance

Why Every Engagement Runs the Same Way

More than 80% of AI projects fail, twice the rate of IT projects without AI, and in Lenovo's study with IDC only 4 of 33 proofs of concept reached production. The models usually work. Projects stall in three predictable places, and each stage of our process is built around one of them.

Where it stalls
The strategy deck

Months of roadmap work and a six-figure invoice, with nobody accountable for delivering what the slides describe.

What we doThe team that writes your AI roadmap ships the first build on it and answers for the result.
Where it stalls
The pilot that never ships

A demo impresses the board, then dies on integration, governance and someone else's backlog.

What we doWe build in your environment from the first week and take it through integration and governance into production.
Where it stalls
The handover

It ships, the consultants leave, and nobody left in the building knows how to keep it running.

What we doWe stay on. The engineers who built it run it through our managed service and ship the next one.

Sources: RAND, The Root Causes of Failure for AI Projects (2024); Lenovo CIO Playbook 2025, with IDC.

Our AI Implementation Process, Stage by Stage

Each stage has a named owner and ends with something you can hold. Start at the audit if you are still deciding where AI fits, or go straight to scoping if you already know the workflow.

01
Optional · under 30 days · fixed fee

AI Audit: find where AI pays off

If you are not sure which processes to automate first, we start with the AI Audit. Eight phases cover your strategy, operations, data, systems and governance. Your team joins two working sessions, in phases 1 and 6, and our engineers do the rest.

The fee is fixed before the audit starts and credited toward your first deployment contract. To gauge your starting point before a call, our AI readiness checklist gives you ten questions to answer first.

You get
  • A readiness scorecard for data, systems and change
  • An opportunity matrix and ROI models for your top 3 to 5 initiatives
  • A 90-day roadmap you own, whether or not you build it with us
Jason, COO of Ovidius AI
Audit, ship and run
Jason, COO
02
Before you commit

Scope: one workflow, priced before you sign

We pick one workflow, map the data and systems it touches and tell you what the build will take before you commit to anything. Scoping one workflow at a time is what keeps a first build to weeks: a single workflow usually takes weeks, something spanning several systems takes longer, and the scope tells you which yours is.

When the risk sits in your data rather than the idea, we build a proof of concept on staging data first and report whether to scale it, change it or stop.

You get
  • A written scope: the workflow, the systems and the data access it needs
  • Baseline metrics, set before the build so the result can be measured
  • A cost and a plan you approve before work starts
Owen Boesveld, CEO of Ovidius AI
Sales and scoping
Owen, CEO
03
Production code from week one

Build: inside your environment, on your data

Agents and workflows get built inside your environment, connected to your CRM, ERP and databases, and tested against a staging replica before anything touches live records. We orchestrate on self-hosted n8n with the models that suit the task, so you own the infrastructure and pay no per-task fees.

Where an agent needs company knowledge, the enterprise context layer decides what it may read. Where output reaches a customer, a second agent checks it first, the pattern behind our enterprise AI chatbot.

You get
  • A working workflow on staging, running your real cases
  • Error handling, retries and alerts in the first release
  • Review steps wherever output reaches people
Maciej, CTO of Ovidius AI
04
Live behind your controls

Integrate: into production and into the team

The workflow goes into production behind your authentication, your governance and your monitoring. Approval rules and logs are set with whoever signs off on AI in your company, using the controls on our enterprise AI governance page.

Your team is trained on what was built and gets the documentation and direct access to all of it. For teams that want to build the next workflows themselves, corporate AI training runs on the same tools.

You get
  • The workflow live in production, in your cloud
  • A trained team, documentation and full access
  • Approval rules and audit logs your governance owners agreed
Jason, COO of Ovidius AI
Audit, ship and run
Jason, COO
05
Weekly sprints after launch

Run and improve: we keep it working

After launch, the engineers who built the workflow keep watching it. When an upstream system changes, the error handler pauses the run and alerts them, the fix goes through staging, and held records are replayed. Each week you see what changed, what the workflow saved and what the next sprint goes to.

Most engagements carry on to a second workflow, which reuses the access, environment and patterns of the first. We run twelve production systems for Pinkcube, including the support agent that handles about 500 chats a week. Our AI managed services page covers what we watch.

You get
  • Monitoring and alerts on every workflow we run
  • Weekly improvement sprints that you direct
  • A report of what each workflow saves
Jason, COO of Ovidius AI
Audit, ship and run
Jason, COO
How long it takes

It depends on what you are building. A single workflow usually takes weeks; something across several systems takes longer, and we tell you which before you commit. For a sense of pace, GoKickflip's content pipeline was built and shipped in 30 days. The AI Audit is a separate engagement on its own timeline.

Everything We Build Runs Inside Your Environment

Your AI workflows sit on top of the systems you already run, in your own cloud, so you keep the data, the infrastructure and the ability to change course. Nothing gets rebuilt to make room for AI.

A frosted glass enclosure holding three dark system blocks connected by orange light lines to a glowing central cube, with nothing leaving the enclosure
What a build sits onYour cloud
Your systems
The CRM, ERP, helpdesk and databases you run today, from Salesforce and HubSpot to Odoo and SAP. Our n8n and SAP Joule Studio guide shows one integration end to end.
Orchestration
Self-hosted n8n in your AWS, GCP or Azure account, so workflows, logs and records stay in your environment.
Models
Claude, OpenAI or a model you host, chosen per task.
Data access
An enterprise context layer that scopes what each agent can read, with every retrieval logged.
Controls
Error handlers, second-agent checks and human approval where the stakes call for it, as on our AI invoice automation builds.
Agencies and MSPs: the same builds ship under your brand on Forge AI

Where Teams Usually Start

Teams join at whichever point fits them. Each of these six starting points links to the page that covers it in full.

"We don't know where AI fits yet"AI AuditEight phases ending in a ranked opportunity matrix and a 90-day roadmap.See the AI Audit →"We know the workflow we want automated"AI consulting and buildScope, build, ship and run one workflow on self-hosted n8n.See AI consulting →"We need proof before a full build"Proof of conceptA working prototype on your staging data and a go or no-go report.See enterprise AI consulting →"We need a company-wide plan"Enterprise AI strategyWhere AI fits across the business, in what order, and where it runs.See enterprise AI strategy →"It's built and needs someone to run it"AI managed servicesMonitoring, fixes and weekly sprints from the team that built it.See managed services →"Our team should build this themselves"Corporate AI trainingYour employees automate a real task from their own week, with an engineer beside them.See corporate AI training →
Builds we run this wayEnterprise AI chatbotAI invoice automationEnterprise context layern8n workflowsAI agents in healthcareAll AI solutions

Two Systems Still Running After Launch

Both were scoped, built and shipped by the team you would work with, and both report their own results.

E-commerce · content pipeline
GoKickflip: an agentic content pipeline, built and shipped in 30 days

A multi-agent pipeline on n8n takes a keyword through research, brief and drafting to a publish-ready SEO article, across about 33 agent executions and six or more AI models.

98%
lower cost per article, $700 to $12.11
10×
output, from 4 or 5 to 43 articles a month
29 min
from research to final draft

"From day one, the Ovidius team moved fast, thought big, and executed with precision. Together, we built something truly cutting-edge that will be a major unlock for our business and change how we operate and grow."

Renaud Teasdale, Founder and CEO of GoKickflip
Renaud TeasdaleFounder and CEO, GoKickflip
Read the GoKickflip case study →
Insurance · 4 brands, 3 regions
IES Limited: one content engine for four regulated insurance brands

An 11-stage agentic pipeline in n8n and Notion. A Compliance Checker Agent audits every draft against each brand's banned terms, exact statistics and approved phrasing before a human reviews it.

80%
less manual content production effort
4 → 1
four brands, three regions, one system
0
external agencies needed in 2026

"Working with Owen and the Ovidius team has been a seamless experience; they are incredibly responsive and adapted quickly to our specific requirements. By leveraging their AI-driven content for our travel insurance sites, we've completely phased out our reliance on external agencies and freelancers in 2026 while significantly speeding up production."

Bhavek Rughani, Head of Marketing at IES Limited
Bhavek RughaniHead of Marketing, IES Limited
Read the IES Limited case study →

More engagements, including Pinkcube's 24/7 support agent, are in our case studies.

How Our AI Implementation Services Compare

Most teams weigh three options for getting AI into production. Here is where they differ once the contract is signed.

Strategy consultancy
Build-and-hand-off vendor
Ovidius AI
What you get
A slide deck and a roadmap
Software, then a handover
Working software in your stack, maintained
Who builds it
A separate delivery team
Their engineers, until the contract ends
The team that scoped it
Time to production
Months of discovery first
Whatever the statement of work says
Weeks for a single workflow, scoped before you commit
Where it runs
Usually left to you
Often the vendor's platform
Self-hosted, in your cloud
After go-live
A new engagement
A change request for every fix
Weekly sprints by the engineers who built it

Questions Teams Ask Before They Start

How long does an AI implementation take?

It depends on what you are building. A single workflow usually takes weeks; something that spans several systems takes longer. We scope it and tell you before you commit, and GoKickflip's pipeline, built and shipped in 30 days, gives a sense of pace for a well-scoped first build.

The AI Audit is a separate engagement. It runs in under 30 days and ends in a prioritized 90-day roadmap.

Do we have to start with the AI Audit?

No. If you already know which workflow you want automated, we start at scoping. The audit is for teams still deciding where AI fits, and its fee is credited toward your first deployment contract if you build with us.

Where does our data live?

In your own cloud. We deploy self-hosted n8n inside your environment and behind your existing authentication, so workflows, logs and records stay with you. We sign NDAs and data governance agreements at the start of the engagement, and the enterprise context layer controls what each agent can read.

Who will we actually work with?

A senior team with one owner per stage: Owen, our CEO, on sales and scoping, with Jason; Maciej, our CTO, on architecture and the build; Jason, our COO, on the audit, delivery and everything after launch. You speak directly with the engineers doing the work. Meet everyone on our team page.

How is an engagement priced?

The AI Audit has a fixed fee, scoped before it starts. Builds and managed services are scoped on a discovery call around the workflows involved, and you know the cost before any work begins.

What happens after the first workflow is live?

We keep it running and ship the next one. The engineers who built it monitor it, fix what breaks through staging and spend weekly sprints on what you choose. The second workflow reuses the access and environment set up for the first. AI managed services covers the detail.

We are an MSP or an agency. Does the process change?

The stages stay the same, and what we build can ship under your brand on Forge AI, our white-label platform, with every client managed from one console. See how that works for MSPs and for agencies.

Start With a 30-Minute Discovery Call

Answer four short questions so we know what you need before the call, then bring the process you want fixed. We come back with the stage to start at: the audit, a scoped build or a proof of concept.

Owen Boesveld, CEO of Ovidius AI
Sales and scoping
Owen, CEO
Discovery and scoping
Maciej, CTO of Ovidius AI
Build
Maciej, CTO
Architecture and the build
Jason, COO of Ovidius AI
Audit, ship and run
Jason, COO
Audits and delivery
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