Knowledge bases · RAG · Agent skills · Tool calling
AI that knows your business and can act on it.
We build knowledge bases and RAG pipelines that answer from your own documents, custom skills for agent frameworks like OpenClaw and Hermes, and tool-calling agents wired into the systems you already run. Then we stay to tune them on how your team actually uses them.
What we build
From your documents to an agent that does the work
Start with a knowledge base, or go all the way to agents that act across your systems.
Knowledge bases
Your documents, manuals, tickets, policies and databases turned into a clean, searchable knowledge base, with ingestion that stays in sync as the source material changes.
RAG pipelines that answer correctly
Chunking, embeddings, hybrid search and reranking tuned on your own questions, with answers that cite their sources and say so when the knowledge base doesn't cover something.
Skills for OpenClaw, Hermes & other agents
Custom skills and tools that let agent frameworks such as OpenClaw and Hermes do real work in your business: look things up, fill forms, run reports, trigger workflows.
Tool calling & MCP integrations
Agents connected to your CRM, ERP, databases and internal APIs through well-defined tools or MCP servers, so they act on live data instead of guessing.
Agentic workflows
Multi-step workflows where an agent gathers information, drafts, checks and hands off to a person for approval, built around how your team already works.
Guardrails, permissions & evaluation
Agents that act under the user's own permissions, keep an audit trail of every call, stay inside their job, and are measured against a test set of real questions.
We run one in production ourselves
The AI assistant inside Izma Office is a tool-calling agent with 37 live data lookups over a company's books. Ask who owes the most and it queries the receivables report, ranks the answer and tells you which report it used. It works under the user's own permissions, is read-only, and logs every lookup. That's the standard we build client agents to.
- ✓Answers cite their source, every time
- ✓Agents act under the user's own permissions
- ✓Every tool call written to an audit trail
- ✓Runs on your infrastructure with the model you choose
- ✓Tested against your real questions before launch
- ✓We stay after launch to tune on real usage
Further reading
Questions
Before you ask
▸What's the difference between a RAG chatbot and an AI agent?
A RAG chatbot looks up relevant documents and answers from them. An agent can also take actions: it calls tools, queries live systems, runs multi-step workflows and asks a person to approve when it matters. Many useful systems combine both, and we build either.
▸Can you build skills for OpenClaw or Hermes?
Yes. We write custom skills and tools for agent frameworks such as OpenClaw and Hermes, connecting them to your data and systems, and also build standalone agents and MCP servers when a framework isn't the right fit.
▸How do you stop the agent from making things up?
By architecture rather than prompting alone. Answers come from retrieved sources or live tool calls, the agent cites where each fact came from, and it's designed to say it doesn't know when nothing relevant is found. We measure this against a test set of your real questions before launch.
▸Is our data safe? Can it run on our own infrastructure?
The knowledge base, vector store and agent can run on your own servers or cloud account, with the language model of your choice, including open-weight models hosted privately. Agents act under the requesting user's permissions and every lookup is logged.
▸Have you built this kind of system before?
Yes. The AI assistant in our own product, Izma Office, is a tool-calling agent with 37 live data lookups over a company's books. It answers under the user's own permissions, cites the report behind every figure, and writes every lookup to the audit trail. Client projects are under NDA.
What should your agent know, and do?
Tell us the questions your team answers every day. That's where we start.