AI at Orbivo
Put AI to work, responsibly and in production
Plenty of companies have an AI demo. Few have AI their customers depend on. We build the second kind, with evaluations, guardrails and data residency controls included.
Three ways we use AI
Smarter teams, smarter products
AI inside your product
Copilots, semantic search, recommendations and agents that give your customers features they’ll pay for.
- RAG over your own content
- Agents connected through MCP
- Multilingual experiences
AI in your operations
We automate the repetitive, document-heavy work that slows your teams down, and keep people in charge of decisions.
- Document intake & validation
- Support triage & resolution
- Analytics in plain language
AI in how we build
Our engineers use AI assistants throughout delivery, with policy, review and security checks so speed doesn’t cost you quality.
- AI-assisted code & test generation
- Automated review & security scans
- Faster legacy code comprehension
Use cases
Where AI is paying off for our clients
We start with use cases where the value is measurable, the risk is manageable and the data already exists.
Document intelligence
Extract, validate and route invoices, claims, customs forms and contracts.
Customer support agents
Resolve common requests across chat and email in multiple languages, and hand off to a person cleanly.
Text-to-SQL copilots
Let staff ask questions of the warehouse in plain language, with queries they can check.
Knowledge search
Search policies, manuals and tickets with answers that cite their sources.
Forecasting & anomalies
Predict demand, spot fraud and flag equipment issues before they cause outages.
MCP integrations
Connect LLMs to your CRM, ERP and internal APIs through scoped, auditable tools.
Our approach
From idea to production in four steps
Discover
We score candidate use cases on value, feasibility and risk, then choose the one to prove first.
1–2 weeksProve
A working prototype on your real data, with an evaluation set that defines what “good” means.
2–4 weeksProductionize
We add guardrails, observability, access controls and cost budgets, then integrate with your systems.
6–12 weeksOperate
We monitor quality and cost, run regression evaluations on model updates and keep improving.
Ongoing
Responsible AI & data sovereignty
Your data stays where you need it
We design AI systems around privacy regulations such as GDPR, HIPAA and CCPA, and around your risk appetite, not the other way around.
- Hosting in the cloud region of your choice on AWS, Azure or Google Cloud
- Model-agnostic: Anthropic Claude, OpenAI, Google Gemini, Cohere or open-weight models you host yourself
- Privacy impact assessments and automated-decision transparency wherever regulations require them
- Human review for consequential or irreversible actions
- Evaluation suites, red-teaming and full audit logs
Residency
Data, embeddings and logs stay in your chosen region.
Security
SSO, least-privilege tools and PII redaction.
Quality
Automated evaluations on every release.
Cost control
Budgets, caching and model routing.
FAQ
Common questions about AI projects
Will our data be used to train AI models?
No. We use enterprise API terms or self-hosted models that don’t train on your data, and we write this into our contracts. Where possible, we keep prompts and logs in your own cloud account.
Can the whole AI system run in a specific region?
Yes. Major model providers offer regional deployments, and open-weight models can run entirely in your own cloud tenancy. We help you weigh the trade-offs between capability, cost and residency.
How quickly can we see results?
Most clients have a working proof of value on their own data within four to six weeks of kickoff, along with an evaluation report showing how it performs.
How do you measure whether the AI is “good enough”?
Before building, we agree on an evaluation set with your domain experts: real examples with expected outcomes. Every change is scored against it for accuracy, groundedness, safety and cost, so quality is measured rather than assumed.
Do we need a data platform first?
Not always. Many valuable use cases, such as document processing and knowledge search, work with the data you have today. If a use case needs better data foundations, we’ll tell you and build only what it requires.
Find your highest-value AI use case
In a half-day workshop, we’ll map opportunities, score them and leave you with a shortlist you can act on.