An AI Agent Development Company for Processes That Already Run
Yamama Software is an AI agent development company that builds language-model solutions into processes that already run, without breaking them. We ground models in your own documents through retrieval-augmented generation, build agents that call real tools within explicit boundaries and human approvals, and automate repetitive steps with a full audit trail. A model goes only where it removes a step or sharpens a decision.
The easy part of AI is attaching it to an interface. The hard part is making it work inside a process that already runs, without breaking it. So we start from the opposite question: where exactly does your team lose time to repetitive work, and where is a decision being made on incomplete information? What answers those two questions deserves a model. Everything else deserves ordinary code.
Language models and retrieval
Grounding models in your own sources through retrieval-augmented generation, so an answer comes from your documents rather than from what the model memorised — and so every answer can show where it came from.
Agent and tool orchestration
Agents that call real tools and APIs inside explicit boundaries: what they may do unattended, what needs human approval, and where the agent stops and hands back to a person.
Workflow automation
Automating the repetitive steps between systems — extract, classify, route, notify — with an audit trail showing what happened and why, because automation you cannot review is automation you cannot trust.
Evaluation and monitoring
Evaluation sets that measure output quality before and after release, plus monitoring for drift, cost and latency. A model nobody measures degrades quietly.
Frequently asked questions
Quick facts
- Retrieval (RAG)
- Models grounded in your own sources, with every answer showing where it came from
- Agents
- Real tools and APIs called within explicit boundaries, with human approval where needed
- Workflow automation
- Extract, classify, route and notify between systems, with an audit trail
- Evaluation
- Evaluation sets measuring output quality before and after release
- Monitoring
- Drift, cost and latency tracked in production
- Principle
- A model where it removes a step or sharpens a decision; ordinary code everywhere else
We ask where your team loses time to repetitive work, and where decisions are made on incomplete information. What answers those two questions deserves a model. Everything else deserves ordinary code, which is simpler and cheaper.
It grounds a language model in your own documents, so answers come from your sources rather than from what the model memorised, and every answer can show where it came from.
Each agent has explicit boundaries: what it may do unattended, what needs human approval, and where it stops and hands back to a person.
With evaluation sets before and after release, and monitoring for drift, cost and latency. A model nobody measures degrades quietly.
Describe the process as it runs today. We will come back with a view of what is worth automating and what is not.
Have another question?Talk to us
If you have a repetitive process eating your team's time, or knowledge spread across documents nobody can search properly, start by describing the process as it runs today and we will come back with a view of what is worth automating and what is not.
Start your project