AI Solutions that ship and keep working
We design, build and run custom AI for your business, from LLM-powered applications to workflow automation and AI agents, with the same managed-service rigour we bring to the cloud.
Custom LLM applications, RAG and AI agents that reach production
Most AI projects stall between a promising demo and a system you can actually depend on. The demo answers questions from ten documents; production has to answer them from ten thousand, with the right permissions, without making things up, at a cost you can predict. Orbit3 closes that gap.
We scope the right use case, build it on secure cloud foundations, integrate it with your existing tools and data, and then keep it running. Because we already run managed cloud operations for our clients, your AI doesn't become another silo. It is monitored, secured and maintained as part of one accountable operation.
- Custom AI & LLM applicationsRetrieval-augmented generation, copilots and assistants grounded in your own data.
- Workflow automation & agentsAutomations and agents that take real action across your systems, with human oversight built in.
- Secure by designYour data stays in your cloud account, with access controls and audit trails from day one.
- Run as a managed serviceEvaluation, monitoring, cost tracking and model updates handled after launch.
For businesses that want AI doing work, not giving demos
The best AI use cases are rarely the most exciting ones. They are the repetitive, high-volume tasks where a reliable system pays for itself in weeks.
Operations and support-heavy teams
Tickets, documents, emails and forms that follow patterns a well-built assistant can handle, with a person checking the edge cases.
Companies sitting on unused knowledge
Policies, contracts, manuals and past work that nobody can search properly. A retrieval system turns that into answers your team trusts.
Product teams adding AI features
You want an AI capability inside your own product and need it built to production standards: evaluated, monitored, cost-controlled.
From scoping to a system your team relies on
AI work fails when it stops at the demo. We take a use case from a scoping conversation through a working build to a monitored production system, and we stay responsible for it.
- Use-case scopingA structured session to find the use case with the clearest payback and lowest risk, with a written recommendation either way.
- Data and integration assessmentWhere the data lives, who may see it, what needs cleaning, and how the system will connect to your existing tools.
- Retrieval-augmented generation (RAG)Document ingestion, chunking, embeddings, vector search and grounding so answers cite your sources rather than inventing them.
- Custom assistants and copilotsInterfaces inside the tools your team already uses, with role-based access to the underlying data.
- AI agents and workflow automationMulti-step automations that read, decide and act across systems, with approval steps where the stakes require them.
- Evaluation and guardrailsTest sets, quality metrics, prompt-injection defences and output checks, run before launch and continuously after it.
- Secure model hostingManaged model APIs or private deployments inside your own cloud account, chosen to match your data-handling obligations.
- Monitoring and cost controlUsage, latency, quality and spend tracked per feature, with alerts when any of them drift.
- Ongoing improvementModel updates, prompt and retrieval tuning, and new capabilities added as a managed service rather than a new project each time.
Scope. Build. Run.
A clear, low-risk path from idea to value that compounds, rather than a one-off project that decays after launch.
Scope
A short, free scoping call to find the use case with the clearest payback and the lowest risk. You leave with a recommendation, including 'don't do this yet' when that is the honest answer.
Build
We design and ship a working solution on secure cloud foundations, integrated with your data and tools, and tested against real inputs with the people who will use it.
Run
We monitor, secure and improve it as a managed service, so the value compounds instead of decaying after launch.
AWS, Azure and Google Cloud
We build on the model and hosting options your data and compliance posture allow, from managed model APIs to private deployments inside your own cloud account.
Amazon Web Services
Amazon Bedrock for managed model access, SageMaker for custom hosting, OpenSearch or Aurora for vector search, and Lambda and Step Functions for orchestration.
Microsoft Azure
Azure OpenAI Service, Azure AI Search for retrieval, Azure Functions and Logic Apps for automation, with Entra ID controlling who can see what.
Google Cloud
Vertex AI and Gemini models, Vertex AI Search, BigQuery for analytics-driven use cases, and Cloud Run for serving.
Frequently asked questions
Which AI models do you use?
Whichever fits the use case, your data-handling obligations and your budget. That includes managed model APIs from the major providers and open-weight models deployed privately inside your own cloud account. We recommend, you decide, and we build so the model can be swapped later.
Will our data be used to train models?
No. We build on services and deployment patterns where your data is not used for training, and we keep it inside your own cloud environment wherever possible. Access is controlled through your existing identity provider.
How do you stop the system making things up?
By grounding answers in your own documents through retrieval, by testing against a set of known questions before launch, by constraining what the system may say when it is unsure, and by monitoring answer quality continuously afterwards.
How long does a first project take?
A scoped first use case typically reaches a working, tested system in a matter of weeks rather than months. The scoping call is where we give you a realistic estimate for your situation.
Do we need a data science team?
No. We handle the engineering. What we need from you is access to the people who do the work today, because they know which answers are right.
What happens after launch?
The system is monitored, secured and improved as a managed service: model updates, prompt and retrieval tuning, cost tracking and new capabilities. You are not left holding a prototype.
Often combined with this
AI systems are only as reliable as the platform under them. These are the services that make an AI build safe to depend on.
CloudOps Managed Services
24/7 monitoring, patching, security and cost control for your AWS, Azure or Google Cloud environment, run by us.
Learn moreCloud Security
Security reviews, risk assessments and hardened target-state controls mapped to the standards you need to meet.
Learn moreCloud DevOps
CI/CD pipelines, infrastructure as code and observability so your team ships daily with confidence.
Learn moreHave an AI idea? Let's pressure-test it.
Book a free 30-minute scoping call. We'll tell you whether AI is the right tool, where it pays off first, and what it would take to ship.