AI systems
AI implementation and chatbot development
Assistants over your data, agents that act through tools, and LLM features in software you already run. We ship one ourselves: MokuBot works inside NetSuite under the account's own permissions.
sales@mokuhub.com. We reply within 24 hours.
Starting points
What people bring us
- A chatbot that answers confidently and wrongly.
- A pilot that impressed in the demo and that nobody uses.
- An assistant that needs company data, with no decision on what it may see.
- Model spend growing faster than usage.
- Years of documents and tickets that should answer support questions and do not.
Scope
The work itself
Build
- Assistants and chatbots over your documents and records
- Retrieval you can check
- Agents that act through tools in NetSuite, Salesforce or your own APIs
- MCP servers that expose an internal system to a model
- LLM features inside an existing product
- AI steps in n8n and Workato
Make it trustworthy
- Access control: what the agent may read, and what it may change
- An evaluation set, so prompt changes are measured
- Logging of every model call and its context
- Human approval on the steps that write
- Cost and latency budgets per request
- Behavior when the model is slow, down or wrong
Process
How an engagement runs
- 01
We check whether it needs a model at all
Often a report, a search index or a validated form does the job. If so, we say it first.
- 02
One narrow version first
One task, real data, a few users, and a way to check the answers.
- 03
Evaluation before expansion
Questions with known answers, run on every change.
- 04
Handover
Prompts, evaluation set, infrastructure and keys are yours, with documented limits.
FAQ
Common questions
Start with the job, not the model
What it should do and where its data lives. We reply with whether a model fits and what a first version looks like.
Describe the use casesales@mokuhub.com. We reply within 24 hours.