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Samer Hany

AI Agents & AI-Powered Solutions

Build AI agents and AI-powered workflows that can interpret information, use your tools, and ask for approval before consequential actions.

Companies with real work that needs interpretation, language, or contextual judgment, whether they are starting from nothing or from a prototype that did not survive contact with production.

Describe your process

From unstructured work to a bounded AI system

  1. 01

    Real input

    Language, documents, email, and company context

    then

  2. 02

    Bounded agent

    A defined job, sources, and stopping rules

    then

  3. 03

    Tools and approval

    Least-privilege actions with human gates

    then

  4. 04

    Traceable output

    Structured results, citations, and run history

AI handles interpretation; conventional software protects permissions, calculations, and consequential actions.

Solutions

Common starting points

  • A blank page and a process that clearly needs interpretation
  • A prototype that demonstrated well and could not be trusted with real data
  • Knowledge spread across documents, systems, and experienced staff
  • High-volume inbound email, documents, or requests being sorted by hand
  • An existing product that should do more of the work for its users

You may recognize this if

  • Someone spends hours reading, classifying, and routing things a system could interpret.
  • The knowledge needed to answer a question exists, but finding it is the expensive part.
  • A demo worked and then fell apart against real permissions and real consequences.
  • You want AI in the business but not making unsupervised decisions that cost money.
  • Information arrives as unstructured text and has to become structured data by hand.

What you end up with

  • Answers arrive in seconds, grounded in real sources you can check.
  • Unstructured input becomes structured data without someone retyping it.
  • Routine classification and routing happens without a person in the loop.
  • Anything consequential is proposed and waits for a human to approve it.
  • The system is observable, evaluated, and safe to run in production.

Usually delivered as

  • AI agents

    Agents that interpret information, use tools, and handle clearly defined tasks, standalone or inside another system.

  • Automated system

    Background software that connects tools, moves information, and keeps the process running.

  • Web application

    A place for your team, an expert, an administrator, or your customers to do the work.

Most projects combine at least two. I will recommend the right mix after I understand the process.

A real example

Business-management and financial software · Belgium

Customers were losing hours to questions the product could already answer, and to administrative tasks that took several screens to complete.

hours seconds

How I approach it

Decide what should not be AI

Deterministic calculations, validation, and business rules stay conventional software, because that is more reliable. AI is used where interpretation, language, or context genuinely creates value. Most real systems are a mix.

Ground everything in a real source

Answers cite what they came from. A confident wrong answer is worse than no answer, and requiring a source constrains what the system is willing to say.

Bound what it is allowed to do

Agents operate within the permissions the user already has, never beyond them. Anything with a consequence is proposed for confirmation rather than executed.

Build it as a product feature, not a demo

Streaming, error handling, evaluation, cost control, and observability are the difference between something that impresses in a meeting and something a business can depend on.

What is included

  • Agents that retrieve from your knowledge and cite what they used.
  • Tool-using agents that read and update your real business systems.
  • Document and email pipelines that classify, extract, and route.
  • Human approval gates before financial, external, or irreversible actions.
  • Evaluation, tracing, and observability so behaviour can be inspected over time.

Investment

Implementation

$20,000 to $50,000

Projects start at $10,000 and most land in this range. Cost moves with scope, integrations, and how much of the existing logic is documented.

Solution Blueprint

$1,500 to $3,000

Scope, architecture, risks, and a phased plan. Credited toward implementation when work starts within 30 days.

If the path is clear, we can go straight to implementation. If it is not, the Blueprint is the cheaper way to find out what you are actually buying.

What partners say about working together

These reviews describe working together on other kinds of projects. The system above is the closest example of this service.

“Samer was the only one who truly took a deep dive into the process we were hoping to automate. He studied the files and asked questions that showed he fully grasped every detail.”

Victor G.

“He often envisions a more elegant solution than yours. Your challenge is to understand what he's suggesting and why it's better.”

Simon L.

Describe your version of this.

No specification needed. A plain description of how the work gets done today is the most useful thing you can send.

Describe your process