A service by Opal Interactive

AI that gives your team hours back, inside the tools they already use.

We build AI platforms and agents, LLMs with retrieval over your own data, that reason and act inside your existing processes. Not one more tool people forget to open: hours recovered every week.

Why our AI platforms

Grounded in your data

Agents answer and act from your documents, systems and rules, so their output is about your business, not generic internet knowledge, and uncertain cases are admitted instead of improvised.

Inside existing workflows

The AI plugs into the systems your team already uses instead of demanding new habits. Adoption is designed in, not hoped for.

Measured in hours, not hype

The goal is operational: manual work leaving the week. If it does not save real time, it is not done.

Use cases

Domain assistants

An assistant that knows your catalogue, policies or protocols and takes hours of lookup and drafting off the team.

Document and data workflows

Reading, classifying, extracting and summarizing the paperwork your team processes by hand today.

Customer-facing AI

Advisors and support that answer from your real content, hand off to humans honestly, and never invent.

Internal automation

Multi-step processes where an agent reasons, acts across systems and leaves a trail you can audit.

How it works

Map the hours

We find where the week actually leaks: the repetitive reading, writing and lookup worth automating first.

Ground the AI

We connect the model to your data and rules with retrieval, so it works from what is true in your business.

Fit the workflow

It goes live inside the tools and processes your team already uses, with humans in the loop where it matters.

Tighten with use

We watch real usage and sharpen the agent on what people actually need, so the saved hours grow instead of decaying.

Frequently asked questions

What is an AI agent with RAG?

A system where a language model is connected to your own documents and data through retrieval: before answering or acting, it pulls the relevant facts from your sources, so its output is grounded in your business instead of guessed.

Does our data need to be perfect first?

No. We start from the sources you already have, documents, wikis, systems, and improve coverage iteratively. The agent is honest about what it does not know.

How do you keep it from making things up?

Grounding plus guardrails: answers come from retrieved sources, uncertain cases are admitted or routed to a person, and we review real conversations to close the gaps.

Where does your week leak?

Tell us the process that eats your team’s hours. We will propose the agent that takes it off their plate.

Tell us about your project