AI & Automation | Custom Application Layer
Own the whole application layer.
For organizations ready to make AI a core capability, not a subscription. We design and build a completely custom application layer around your business, the workflows, guardrails, integrations, and interface, on the model and infrastructure you choose. You own all of it.
What This Is
The model is a commodity. The application layer is the asset.
A raw AI model is the same one your competitors can rent. The value, and the safety, live in the layer built around it: the workflows that apply your standards, the guardrails that keep it in bounds, the integrations into your systems, and the interface your people actually use.
Most firms run on the shared application layer we have already built, delivered as Managed AI. For now it is purpose-built for architecture, engineering, and construction firms, which is why our Built Intelligence peer group exists: every firm on the layer makes it sharper for the next. Over time we will build layers for other industries the same way.
A custom application layer goes further. If your business is large enough to justify it, we build you a system of your own, designed around your operations and owned outright by you.
Where This Leads
The path to agentic operations
A custom application layer is the foundation for something bigger: agentic AI. As the layer matures, agents run the routine back-office operations of your business, the workflows, the handoffs, the follow-ups, so your people no longer spend their days on the production work.
They move up to the work that stays unmistakably human: the business outcome and the intent behind it, the relationships, and making sure the people on every side are looked after. They hold the direction and own that the outcome lands the way it should. That is the destination, and a custom application layer is how you get there safely, on the infrastructure and models you control.
Who It Is For
Built for organizations, not experiments
This is for mature businesses, small enterprises at a minimum, with the scale and budget to make AI a core capability, and the appetite to own it rather than rent it.
This fits when
- You operate at enterprise scale, and AI is central to how you deliver.
- You want to own the IP, the data, and the stack, not license them.
- You can commit to a serious, multi-year build.
Start elsewhere when
- You are still proving the value and want results before a big commitment.
- You need the IT and data foundation in place first.
- A productized build would get you most of the way.
That is what Managed AI and the Strategic AI Assessment are for, and we will say so honestly.
Your Model, Your Infrastructure
On-premises and owned, or outsourced and governed
We are model-agnostic. Which model runs underneath is a business decision, not a technical lock-in, and you can change it as the field moves.
Own it entirely
Open models running on your own infrastructure, on-premises or in your private cloud. No external API, air-gapped if your policies require it. The models, the data, and the application layer are all yours, end to end.
Outsource the model, own the layer
Connect a leading commercial model, like Anthropic's Claude or OpenAI's ChatGPT, under enterprise data terms. You skip running the model yourself, and still own the application layer built on top of it.
How We Build It
A serious build, run like one
Architecture and discovery
We map your workflows, data, and systems, then design the application layer and the model strategy around them.
Foundation and governance
We stand up the infrastructure, security, and data governance the layer runs on, owned by you from day one.
Build and integrate
We build the workflows, guardrails, and interface, and wire them into the systems your team already uses.
Adoption and handover
We roll it out, train your people, and hand over documentation and ownership. It is your system to run, with us alongside for as long as you want us.
The Investment
An enterprise commitment
A custom application layer is a major, multi-year investment, sized for organizations making AI a core capability. It is scoped to your business, your systems, and the model strategy you choose.
We size it precisely on a discovery call, and we will tell you honestly if Managed AI would get you most of the way for a fraction of the commitment.
FAQ
Custom application layer questions
What is a custom application layer? +
It is a completely custom AI system we design and build around your business, the workflows that apply your standards, the guardrails that keep the model in bounds, the integrations into your systems, and the interface your people use, on the model and infrastructure you choose. You own all of it.
Who is a custom application layer for? +
It is for mature businesses, small enterprises at a minimum, operating at scale where AI is central to how you deliver, with the budget to make AI a core capability and the appetite to own it rather than rent it. If you are still proving the value or need your IT and data foundation in place first, we will point you to Managed AI or the Strategic AI Assessment instead.
Do we have to run our own AI model, or can we use one like Claude? +
Either. We are model-agnostic. You can run open models on your own infrastructure, on-premises or private cloud and air-gapped if your policies require it, or connect a leading commercial model like Anthropic's Claude or OpenAI's ChatGPT under enterprise data terms while still owning the application layer built on top.
How do you build a custom application layer? +
In four phases: architecture and discovery to map your workflows and design the layer, foundation and governance to stand up the infrastructure and data governance you own from day one, build and integrate to create the workflows and wire them into your systems, then adoption and handover with training, documentation, and ownership handed to you.
Why not just use the raw AI model on its own? +
The model is a commodity, the same one your competitors can rent. The value and the safety live in the layer built around it: the workflows that apply your standards, the guardrails, the integrations, and the interface your people actually use. That layer is the asset.
Start Here
Scope what owning it looks like
It starts with a conversation: what you want AI to do, what you want to own, and whether a custom application layer is the right call. If it is not, we will point you to the option that is.