Embedded AI engineering.

What embedded engineering means

Nagomi engineers work with your team to turn a recurring workflow into a working AI system. The work includes understanding your data, building integrations, reviewing outputs with the people responsible, and teaching your team to operate the result.

This is forward-deployed work inside your environment. We build internal assistants, workflow agents, and automations around the work your people already do. Your team retains the implementation and the capability to maintain it.

Assistants and agents have different responsibilities

An assistant helps a person find knowledge or prepare a draft. A workflow agent can connect steps: classify a request, retrieve information, and prepare an action in another system. Each needs a defined scope and a clear way to stop or escalate.

We design approval points and exception handling with your staff. Judgment, relationships, and responsibility stay with people. The integration has to respect the permissions and limits of every system it touches.

• Internal knowledge assistants with source material available for review.
• Report and response preparation grounded in your records.
• Workflow integrations that prepare actions for human approval.

Build around an evaluation your team understands

Before extending the workflow, choose representative tasks and examples of acceptable results. Include missing data, conflicting records, and requests the system should decline or escalate.

Review quality, exception frequency, task completion, and operating cost together. A convincing demo is a starting point; a repeatable evaluation lets the team decide which changes belong in everyday use.

Twelve weeks, with a defined handover

We observe the workflow in weeks 1–2 and focus the roadmap in weeks 3–4. Weeks 5–10 are for building and reviewing working software weekly. Weeks 11–12 are for transfer: the code, models, and playbooks remain with your organization.

We agree on responsibilities and outcomes early. Your workflow owner helps review the result; your technical team brings the access and system context needed to integrate it. The delivery plan makes room for learning from actual use.

Operating costs belong in the design

Model calls are one part of the bill. Retrieval, document processing, integrations, repeated attempts, and human review also affect the cost of completing a task. A useful evaluation follows the whole workflow.

We discuss the operating environment and constraints before building. There is no universal cost-per-agent number: the appropriate measure depends on the task, how often it runs, and the review it requires.

Capability is part of the deliverable

Your repositories, tools, and data are the working environment. Alongside the implementation, the team needs instructions for reviewing results, resolving exceptions, and changing the workflow.

We practice those steps together before the engagement ends. Bring a workflow and the systems it crosses to the first call; we will work through where an embedded build can help.