Code Hunterz

AI Automation

We automate the steps your team retypes: intake, classification, routing, and follow-up. A model is used where it helps. A human stays in the loop where it must.

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The problem

People paste the same details from email into a spreadsheet, then into another tool. A chatbot on the website does not fix that. The process was never written down, so the software has nothing precise to do.

We map the steps, automate the handoffs that are boring and checkable, and keep an approval when a wrong answer is expensive. This includes lighter workflow automation. It is not a promise to remove a department.

What you get

Process map

The steps, the systems, and the exceptions, written before any model is called.

Automated handoff

A job that reads an input, produces a structured result, and writes it where the next person works.

Review point

A queue or notification for the cases that should not pass through silently.

Log of what ran

Enough history to see what the automation did to a given item.

How the work runs

  1. 01

    Shadow the work

    We watch a real week of the task, including the weird cases.

  2. 02

    Separate rules from judgment

    Deterministic steps stay deterministic. The model is reserved for language and classification.

  3. 03

    Pilot on past items

    We run the flow on historical examples before it touches live work.

  4. 04

    Turn it on with a limit

    Volume starts small. The review point stays until the error rate is acceptable to you.

Stack

  • LLM APIs for language tasks
  • Queues and webhooks
  • The inbox, drive, or internal tool you already use
  • Structured outputs and validation

Who it is for

  • Operations teams drowning in repetitive intake
  • Companies copying data between two systems by hand
  • Support or finance groups with a document-heavy queue

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

Will this replace staff?

It removes retyping and routing, not accountability. Someone still owns the outcome, especially when money, customers, or compliance are involved.

What if the model is wrong?

Outputs are validated. Low-confidence or out-of-policy items stop for a person. We do not hide uncertainty behind a confident sentence.

Is this the same as RPA?

Some of the glue is similar: watch a queue, do a step, write a result. We prefer APIs over clicking through a user interface, and we use a model only on the language parts.

Do you need our data to train a model?

Usually no. We call a model with your content at run time under your account's terms. Training a private model is a different project and we will say if it is unnecessary.

AI Integration

Model-backed features added to software you already run, with your data and your permissions.

AI Agents

Narrow agents that follow a tool list and a stop condition, with a person when the job should not continue.

API and Backend Engineering

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

Inquiry

Start a ai automation project

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