Code Hunterz

AI Integration

We add AI to an existing product or internal tool: search, classification, drafting, or an assistant that can only see what that user should see.

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The problem

A demo that answers from the public internet is not an integration. The work is your data, your roles, and a failure mode that does not invent a policy or a price.

Teams also get stuck buying a model wrapper that cannot be turned off, logged, or explained to the person whose name is on the answer. We build the feature inside the system you already operate.

What you get

Feature inside your product

A specific capability in the app people already open, not a separate toy.

Permission-aware data access

The model only retrieves what that user is allowed to see.

Logging

A record of prompts, sources, and outcomes you can audit.

Kill switch

A way to disable the feature without taking the rest of the product down.

How the work runs

  1. 01

    Pick one feature

    Search, summarize, classify, or draft. Not all of them in the first release.

  2. 02

    Find the source of truth

    Documents, database rows, or tickets, and who may read them.

  3. 03

    Wire and constrain

    Retrieval, tools, and validation sit in front of the model.

  4. 04

    Evaluate

    A set of real questions, scored, before the feature is offered to everyone.

Stack

  • Your existing application stack
  • LLM APIs
  • Retrieval over approved content when the task needs it
  • Auth you already use

Who it is for

  • Product teams adding an assistant to an app
  • Companies that want search across internal documents
  • Operators who tried a generic chatbot and need it tied to real records

Reviews

What clients say about the work

“Collaborating with Code Hunterz on our complex website development project was a seamless experience. Their developers showcased exceptional technical skills and a deep understanding of our requirements. They made a fantastic website that streamlined our operations and enhanced efficiency. We look forward to working with them again.”

John Smith

Director

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Questions

Which model do you use?

The one that fits the task, latency, and the terms you can accept. We do not lock you to a single vendor without a reason, and the provider is called through an interface we can change.

Will our data be used to train public models?

We configure providers so your content is not used for their training when that option exists, and we will tell you when a provider's terms do not allow that guarantee.

Can this read our whole database?

Only through queries and permissions we define. Broad, unfiltered access is a bug, not a feature.

What does integration cost relative to a new app?

It is scoped as a feature on the system you have. If that system cannot expose data safely, the first work is an API, and we will say that before starting.

AI Automation

Repetitive document and routing work automated with rules and models, and a person where a mistake is costly.

Chatbot and LLM Apps

Assistants that answer from content you approve, and hand off when they should not guess.

API and Backend Engineering

Auth, data models, and APIs so web, mobile, and automation share one source of truth.

Inquiry

Start a ai integration project

Describe the job and the timeline. We reply to the email address you enter. You can also reach us through the contact form.