AI Solutions
AI that solves a real problem, not AI for the sake of it.
AI is useful when it takes a specific, repetitive job off your plate: answering the same questions, finding information in documents, sorting enquiries. We start with the problem, prove a small version works, and keep a person in the loop where it matters.
Try it
A customer assistant that knows when to stop.
This prototype answers questions for a fictional café using only approved information. Every answer shows its source. Allergy questions, complaints and anything uncertain go to a person, with no guessing.
Internal Prototype
Rules-based demo. Production assistants use a language model with retrieval, guardrails, evaluation and logging.
AI readiness
Five questions before building anything
Data quality
Is the information the AI would use accurate, current and written down?
Repetition
Does the task happen often enough to be worth automating?
Knowledge availability
Can the answers be found in approved sources, not guesswork?
Privacy
What personal or sensitive data is involved, and who may see it?
Measurable outcome
How will you know it helps: time saved, replies faster, fewer errors?
For technical buyers
How we build production AI
Retrieval over your information
Provider-neutral design
Guardrails
Evaluation
Human handoff
Access control & privacy
Monitoring & fallbacks
Ten straight answers
Everything you’d want to know before starting
What is it?
Assistants and tools that use language models to answer questions, search information, extract data or draft content, grounded in your own approved information and designed with clear limits.
Who is it for?
- Businesses answering the same customer questions every day
- Teams whose knowledge lives in PDFs, chats and people’s heads
- Businesses that process forms, invoices or documents by hand
- Founders exploring an AI feature for their product
What problem can it address?
Repetitive questions, slow access to information and manual document work take time away from customers. The right AI use case can reduce that load, but only if it is chosen carefully and built with sensible safeguards.
What can Orbit Hive deliver?
- Customer-facing FAQ and support assistants
- Internal knowledge assistants
- Knowledge search over your documents (RAG)
- Document Q&A and structured data extraction
- Support triage and routing
- Summarisation and content workflows
- AI features inside internal tools
What does the process look like?
What do you need from me?
- The questions or task you want help with, with real examples
- The documents or information the AI may use
- Who reviews answers and handles handoffs
- Where it should live: website, WhatsApp, internal tool
What does a typical project include?
- Use-case discovery and feasibility check
- A small working prototype
- Evaluation against real questions
- Production build with guardrails and handoff
- Monitoring setup and a review after launch
What should I ask before starting?
- What happens when the AI doesn’t know the answer?
- Where is our data stored and who can see it?
- How do we measure whether it’s helping?
- What will it cost to run each month?
- Can we switch AI providers later?
Ask us these too. You should get a clear answer to each one.
What happens after launch?
We review real conversations or outputs after launch, improve the knowledge base, tighten guardrails and only then consider the next use case. AI systems need a little ongoing attention, and we plan for that from the start.
How do I start?
Tell us a little about your business and what you want to improve. We’ll reply personally and suggest a sensible first step.
Have a business idea, website problem or workflow worth improving? Let’s talk.
Tell us what you’re trying to improve. We’ll reply personally, ask a few questions and suggest a sensible first step, even if that step is small.