AI products for customers and teams
Applications that help people search, analyze, create, or complete a valuable task, with the experience and AI behavior designed together.
AI Agent Implementation
AI agent implementation for workflows that need more than a demo.
We design, build, and validate AI agents around your documents, business data, tools, permissions, review process, and output formats. The first release focuses on a defined task your team can evaluate.
One team from idea to launch. Work directly with senior product, AI, software, and data specialists who can design the experience, build the system, connect it to your business, and support it in production.
01
An agent is not the strategy
A useful AI agent is a workflow system: it needs input boundaries, tool access, source retrieval, business rules, action limits, review checkpoints, audit logs, and a clear owner. Dotnitron starts with the workflow and only then decides what the agent should be allowed to do.
02
The best first agents are narrow
We target high-friction workflows where the agent can produce measurable value quickly: evidence review, policy-to-control mapping, diligence document triage, background verification, secretarial due diligence, ERP answer generation, and workpaper drafting.
03
Work directly with the team implementing your agents
Our senior builders work with process owners and technical teams to define the task, build the software, test results, and plan rollout. You can review progress and make decisions with the people doing the work.
04
Human review stays in the loop
We design agents to draft, retrieve, classify, compare, and prepare outputs for human approval. The system exposes source citations, visible SQL where relevant, exception reasons, confidence notes, and reviewer edit history so teams can trust what changed.
05
Production controls are built in
Every implementation defines approved data scopes, role-based access, model/provider boundaries, logging, evaluation questions, failure handling, and deployment constraints. This keeps the agent useful inside real operating environments, not just in a sandbox.
06
From first workflow to repeatable system
A useful pilot produces a working workflow, validation results, measured preparation and review effort, security notes, and a recommendation to expand, revise, or stop.
Where this can help
Start with the change your users or business need.
You may want a better customer experience, more capacity for your team, or a faster way to reach an important decision. We help you turn that need into the right product or system.Systems for expert work
Document and knowledge systems that prepare analysis, surface evidence, and give experts control over consequential decisions.
Operational data and decision systems
Applications that connect approved business data, visible query logic, and useful answers to the people running the business.
FAQ
Frequently asked questions
What is AI agent implementation?
AI agent implementation is the process of turning a business workflow into a production AI system that can retrieve context, use tools, follow controls, produce source-backed outputs, and route work to humans for review.
How is this different from building a chatbot?
An agent workflow has a defined task, allowed tools, approved data access, output formats, state, review checkpoints, and evaluation criteria. A chat interface may be part of it, but the implementation covers the full process.
Which agent workflows should we start with?
Start with recurring work that is manual, expensive, source-heavy, and easy to validate against human output. Evidence review, diligence triage, policy-control mapping, verification, reporting, and ERP answer queues are strong first candidates.
Why work with Dotnitron on agent implementation?
We bring product design, AI and software engineering, integrations, evaluation, and rollout into one engagement. Your team works directly with the builders to shape the system around its business process.
Have something in mind?