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Iotron AI

AI solution · AI Agents & Generative AI

Assistants and agents grounded in your company knowledge, connected to your tools, and constrained by guardrails you control.

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01The problem

Generic chatbots hallucinate, ignore your data and cannot act. Teams need assistants that answer from approved sources and complete tasks in the systems they already use.

The Iotron approach

We ground every answer in your approved sources, give agents only the tools and permissions a task needs, and put a person in the loop wherever an action carries risk.

02Use cases

Where it makes a difference.

Each one scoped against a real decision, evaluated on your data and integrated with the systems you already run.

  1. 01

    Internal knowledge assistants

  2. 02

    Customer-support assistants with escalation to humans

  3. 03

    Sales and operations copilots

  4. 04

    Report and document drafting with citation to sources

  5. 05

    Task agents that call your APIs

03How it works

From data to decision.

The typical architecture, from your data to a decision people can trust. Every stage is evaluated, monitored and logged.

  1. 01InputIngest and index your content
  2. 02Stage 2Retrieval (RAG) with permissions
  3. 03Stage 3Model reasoning
  4. 04Stage 4Tool calls to your systems
  5. 05Stage 5Guardrails and human approval
  6. 06OutcomeLogging and evaluation

04Architecture & control

What it takes — and how it stays safe.

What goes in, what the system does with it, and the controls around every stage.

Inputs

Data you need

  • Approved knowledge sources
  • Access-control rules
  • Example conversations or tasks

System

From data to decision

  1. 01Ingest and index your content
  2. 02Retrieval (RAG) with permissions
  3. 03Model reasoning
  4. 04Tool calls to your systems
  5. 05Guardrails and human approval
  6. 06Logging and evaluation

Controls

Security & governance

  • Source citation
  • Permission-aware retrieval
  • Content filters
  • Human-in-the-loop for sensitive actions
  • Evaluation sets tracked before and after launch

Applied at every stage, not added at the end.

AI Agents & Generative AI: common questions

RAG or fine-tuning?

Usually retrieval first: it keeps answers tied to current sources; fine-tuning is considered for style or narrow tasks.

How do you prevent wrong answers?

Grounding, citations, refusal rules and evaluation against test questions.

Which models?

Model-agnostic; chosen for accuracy, cost, latency and data-residency requirements.

Project scenario · Telecom

Giving care agents answers from the right policy, plan and procedure — in seconds, with the source attached.

See how the project runs

From our engineers

RAG vs fine-tuning for enterprise knowledge

Why retrieval usually beats fine-tuning for grounding an assistant in company knowledge, and when fine-tuning still earns its place.

Read the article

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Ready to discuss AI Agents & Generative AI?

Tell us where ai agents & generative ai could change a decision or a workflow. We will give you an honest view of feasibility, data needs and the first step.