Build
Production engineering against the first increment — data, model, integration, security review and QA. The phase where a decade of shipping mission-critical systems is the difference between a demo and a capability.
The model is the small part
Standing up a model that produces plausible output is a week’s work. Making it something your business can depend on is the other ninety percent: retrieval that respects your permissions model, integration into the platform where the work actually happens, an evaluation harness that catches behavior drift, an audit trail your compliance team accepts, and a release process that lets you change it safely next quarter.
We engineer that the way we engineer any production system, because that is what it has to become. Build ends with a working capability in your environment, measured against acceptance criteria set during Plan — not a demonstration on our laptops.
What we build
Custom AI Agents & Copilots
Agents that carry a workflow end to end, with tool scopes narrowed to the minimum, human confirmation on consequential actions, and autonomy widened only on production evidence.
Explore → Custom AI applicationsGenerative AI & RAG Solutions
Retrieval-augmented systems grounded in your own content, with access control enforced at query time and source citation so answers are checkable.
Explore → Application layerCustom Software Development
The application an AI capability is usually delivered through — built to fit the process rather than forcing the process to fit a product.
Explore → AutomationIntelligent Process Automation
Rules-based and AI-assisted automation for the repetitive work that currently scales with headcount.
Explore → InterfacesAPI Design & Development
The contracts that let an AI capability be consumed by more than one system, and be replaced without a rewrite.
Explore → IntegrationPlatforms & Integrations
Connecting to the ERP, EHR, PMS or CRM the work lives in. Consistently the largest line item, and the one most estimates miss.
Explore →What Build actually covers
Data & pipelines
Retrieval, quality and lineage work — what determines whether the output can be trusted at all.
Model & application
Behind a gateway abstraction, so the provider stays a configuration decision rather than a rewrite.
Security & governance
Access control, data handling, audit trail and human-in-the-loop boundaries. See Trust & Security.
QA & release
Automated evaluation of model behavior alongside conventional test coverage, wired into your pipeline.
About build engagements
How quickly can a first increment reach production?
For a scoped, single-workflow build with reasonable data access, weeks rather than quarters. The real drivers are integration surface and data readiness, not model choice — which is why Plan produces a specific estimate for your environment before Build is committed.
Who owns the code?
You do. Work product is delivered into your repositories and your cloud accounts, and we do not build dependency on proprietary Xcelacore middleware.
Can you build on top of an AI product we have already bought?
Yes. Integration, QA, security review and deployment around someone else’s model or platform is a common engagement shape, and we are comfortable being the delivery partner rather than the model partner.
How do our engineers stay involved?
They review our pull requests throughout. It is the fastest knowledge-transfer mechanism available and it keeps us honest — see Partnership Model.
Ready to build something that ships?
Bring the increment you want first. We will scope it against your real integration surface.