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(AI / Data)

AI, ML & RAG

Assistants, search, and automation that answer from your own data and show where each answer came from. We start from a real problem, measure quality before launch, and keep your data under your control.

  • LLM integration
  • RAG & vector search
  • Automation
  • Evaluation
(Why Zettops)

Built to hold up.


  1. 01

    Grounded in your data

    Answers come from your documents and systems, not from what a model happens to remember, so they reflect how your business actually works.


  2. 02

    Sources you can check

    Every answer links to the passages it was built from, so readers can verify a claim in one click instead of taking it on trust.


  3. 03

    Measured before launch

    We build a test set from your real questions and score answers for accuracy and grounding, then keep measuring as data and models change.


  4. 04

    Your data stays yours

    We design for where your data may go: which providers see it, what is stored, and what is never sent. Access follows your existing permissions.

What we deliver.

(06 parts)

(Process)

How we work.

Four steps, with you in the loop at every one. No black boxes and no surprises at launch.

  1. Discover

    Goals, users, and risks

    We map the goal, the users, the risks, and agree on what success looks like.

  2. Design

    Reviewed early and often

    Direction, prototypes, and a system, reviewed with you early and often.

  3. Build

    Tested at every step

    Production-grade engineering with tests, security checks, and a live preview at every step.

  4. Launch & grow

    Measured and improved

    We ship, measure, and keep improving: performance, content, marketing, new features.

(AI, ML & RAG)

Your project

  • The problem to solve and who will use the result
  • The data sources in scope and who may access them
  • Model providers and where data may be sent
  • How quality is measured and what counts as good enough
  • Hosting, monitoring, and ongoing evaluation

Model and hosting usage is billed by the provider and estimated separately. Fine-tuning and large data cleanup are scoped on their own. All of it is agreed in a written proposal.

Start a project
(Questions)
01Will our data be used to train someone else's model?

Not if we can help it. We choose providers and settings that do not train on your data, document exactly what is sent where, and can use self-hosted models when your data must not leave your infrastructure.

02What if the assistant gives a wrong answer?

No AI system is right every time, so we design for it. Answers cite their sources, the assistant declines questions its sources cannot answer, users can flag mistakes, and the evaluation suite shows how often errors happen and whether changes make it better or worse.

03Do we need a lot of data to get started?

Usually not. Most assistants start from documents you already have, such as policies, manuals, tickets, or wikis. The first step is a look at what you have and how clean it is, so we can tell you honestly what is possible.

All work

Guides worth reading.

All insights

(Start a project)

Have an idea? Let's build it.

A product, a platform, a brand, or a security review. Tell us what you are building and we will reply with next steps.