AI agents and copilots
Agents that match people, draft rotas and write daily briefs, with people approving the result.
GeminiAI agentsAI practice
AI agent and MCP server development
Agents, MCP servers and RAG that people use every day, with a safety check or a human approval before anything reaches a user.
In short
We build applied AI into real products. At Everhome, AI matches carers to clients and drafts rotas that coordinators approve, and an MCP server lets coordinators ask operational questions in plain language. At Ovue, Gemini insights pass a server side safety check before seafarers see them, and a document reader fills medical certificate fields that crew confirm. For ThemeXpert we built a RAG pipeline that keeps replies grounded in each client’s own content.
- Models
- Google Gemini and other LLMs
- Patterns
- Agents, MCP, RAG, document AI
- Guardrails
- Safety checks, human approval, audit
- Stack
- Node.js, TypeScript, vector search
- Starts with
- 1–2 week sprint, $1k–$2k
Applied AI
Agents, MCP servers and RAG that people use every day, with a safety check or a human approval before anything reaches a user.
Proven at
Everhome
Ovue ANCORE
ThemeXpert
GeminiAI agentsMCP servers
Your systems exposed as typed tools, so teams can ask operational questions in plain language.
MCP
Node.jsRAG and document AI
Answers grounded in your own content, and readers that turn documents into checked fields.
RAGVector searchEmbeddings
Guardrails and human approval
Server side safety checks and approval steps before any output reaches a user.
Safety checksAudit trail
- 1AI agentPlans the steps and calls tools to answer, on models like Gemini.
- 2MCP serverExposes your systems as typed tools, with authentication and an audit log.
- 3Your data and documentsTools query databases and APIs, and RAG answers from your own documents.
- 4Safety checkEvery answer is checked on the server before a user sees it.
- 5Human approvalAnything that changes data waits for a person to approve it.
Built for Everhome coordinators and Ovue ANCORE crews.
GeminiMCPRAGAI agentsNode.jsWhen to call us
Signs this is the right time
- You want AI features your team will trust in production
- Staff spend hours on drafting, matching or document entry
- You need an assistant that can safely use your internal systems
- Answers must come from your own documents, not the open web
- Every AI action needs an audit trail and a human sign off
Questions
Common questions
Anything else is easier to answer on a short call.
What is an MCP server, and why would we need one?
The Model Context Protocol lets an AI agent use your systems through typed, permissioned tools. An MCP server exposes your database and APIs safely, so an assistant can answer operational questions without open access to everything.
How do you stop AI from giving wrong or unsafe answers?
Answers are grounded in your own data through RAG, every answer passes a server side safety check before a user sees it, and anything that changes data waits for a person to approve it.
Which AI models do you work with?
Mostly Google Gemini in production, and other LLMs through standard APIs, chosen per use case for quality, speed and cost.
Can AI read documents for us?
Yes. A Gemini document reader at Ovue fills medical certificate fields, and the seafarer confirms each value before it is saved.
Tell us what you need to build or scale.
Engagements start with a 1–2 week discovery sprint, $1,000 to $2,000. Email [email protected]