Ortelian vs building in-house:
which fits your company?
Building AI agents in-house means your own team picks the models, connects your systems, builds the agents and keeps them running. Ortelian is a platform for running AI agents on a maintained world model of your company, deployed by our team on site. We work on site not because our product needs it, but because your company does. AI has the most impact when the work itself changes, and that takes time with the people who do it.
Build in-house if you have a strong AI and data team, the time to build, and the work is core to what makes your company different. Choose Ortelian if you want impact soon on complex work, without first hiring a team and building a platform. After launch, your team runs it or we keep running it for you.
At a glance
Same goal, different starting point.
An in-house build gives you full control. It also starts with hiring, and with building the parts agents need before the first one does useful work. Ortelian starts with the work: an audit on site, then builds in two-week cycles on a platform that already exists. Access to the Ortelian platform is part of an engagement, and there is no self-serve product. If you are also weighing a company-wide assistant, see Ortelian vs Glean.
When to build in-house
When AI is the product, or close to it.
Build it yourself when the agents are part of what you sell, or when the work is core IP you want to keep entirely inside the company. Then knowing how to build them is worth owning, and the cost of the team is part of building the business.
It also fits when you already have a strong AI and data team with room on its roadmap, and the time to build what agents need before they do useful work: connections to each system, a shared picture of the business, permissions, evaluations and a way to watch every run. And if a process is simple, an agent doesn’t need much context, and a small internal build may be all you need.
When to choose Ortelian
When the work is complex and you want it changed soon.
Choose Ortelian when you want AI to change how a specific piece of work runs, such as a planning round, a back-office check or a sales process with many people and sources, and that work depends on context no single system holds. Most of the effort there goes into knowing how the work really runs, redesigning it with the people who do it and agreeing the standard on real examples. We work on site not because our product needs it, but because your company does. The steps are on how we work, and the full method is in our guide on how to deploy AI agents in an enterprise.
The platform is the part an in-house team would otherwise build first. Agents work from a world model for knowledge work: your systems of record joined with outside sources, organised by an ontology we define with you, with every fact keeping its source. Each agent gets only the tools its job needs, every action is recorded with the sources behind it, and evaluations keep running after launch. Workshops during the build prepare your team to own the work. After launch, they run it or we keep running it for you. Either way, we host and operate the platform. It is hosted in the EU, agents use models with zero data retention, and you own your data and world model and can export both. More on what an AI-native deployment partner does.
01 · Software
The code holds everything the work needs.
That’s why AI took off in software first.
02 · Internal knowledge
A wiki holds what the company writes down.
That’s enough for questions about the company itself.
03 · Knowledge work
Your work also depends on the world outside.
Customers, competitors, regulations, people moving. We join it with what’s inside, in one model.
Inside the company The world around it
Can you combine them?
Yes. Many teams will want both.
Your engineers can keep building the AI that is part of your product, while we redesign and run internal work that depends on context from many systems. Workshops during the build are there so your team learns the work and can take on more of it over time.
Because the platform can run headless, agents your team builds can work with the same world model through scoped tools, once that is set up as part of the engagement. Your own agents then start from the same picture of the business instead of rebuilding it. We agree in the audit which systems and sources each side uses.
Other comparisons: Ortelian vs Glean, Ortelian vs AI consultancies, Ortelian vs Microsoft Copilot, Ortelian vs ChatGPT Enterprise and Ortelian vs Sierra.
Questions
Questions buyers ask when comparing.
Is it cheaper to build AI agents in-house?
It depends on the team you already have. An in-house build costs the people who build and maintain it, the models and infrastructure, and the time before the first agent does useful work. Ortelian comes as an engagement. The audit is a fixed fee, credited against the first build if you go ahead. Each build is a fixed fee agreed at the end of the audit. The platform is a monthly fee sized to your usage, with unlimited users and no annual commitment. We agree the scope, expected usage, support and fees with you before work begins.
Can our team take over after launch?
Yes. Workshops during the build prepare your team to own the work. After launch, your team runs it or we keep running it for you. Either way, we host and operate the platform, and the evaluations keep running on the work in use. Over time your team takes on more of the day-to-day changes.
Can agents our engineers build use the Ortelian platform?
Yes, once it is set up for you as part of the engagement. The platform can run headless: your own agents and AI tools, such as Claude or ChatGPT, work with the world model through scoped tools, and each one only gets the access its job needs. There is no self-serve product.
Do we lose control or get locked in?
You own your data and world model and can export both, along with the full history of how your agents were set up. The platform fee has no annual commitment. Ortelian works across AI models and tests them against your work, so the world model and working methods stay when models change. Each agent gets only the tools its job needs, and anything that leaves the company waits for a person’s approval.
When should we build it ourselves instead?
When the agents are part of the product you sell, when you already have a strong AI team with time to build, or when the process is simple. If a process is simple, an agent doesn’t need that context and you probably don’t need a partner like us.
Let’s work out the fit.
Bring the work that is held up and what your team has already built. We’ll tell you whether it is a job for us, or whether your own team is the better place to start.
