Technician Matching System for TechPRO

A system that turns unstructured client conversations into clear service requests, finds technically and geographically suitable specialists, sends outreach, and tracks every response.

It started with a conversation

The founder of TechPRO came to me with a specific problem: managers were spending too much time turning new requests and technician profiles into usable data.

Once we walked through the day-to-day work, the bottleneck became clear. A manager had to read the client conversation, remove irrelevant details, ask ChatGPT to structure the request, and copy the result into the task system.

Adding a technician followed the same pattern: take a CV, extract the relevant experience, and turn it into a searchable profile.

After that, the manager still had to search the database, judge who might fit the request, consider location and travel radius, contact technicians one by one, and remember who accepted, declined, or did not respond.

The process worked, but too much of it lived across disconnected tools and in the managers' heads. For experienced people it was slow. For new people it was difficult to learn.

Client conversationChatGPTTask systemTechnician databaseIndividual messagesManual tracking

We started with the smallest useful version

We did not begin by replacing the company's entire operating system. The first priority was to remove the most repetitive work, so we built a Telegram bot as an MVP.

A manager could send it a client conversation or a technician's CV. The system analysed the text, extracted the useful information, and saved it as a task or technician profile.

For new requests, it also returned the ten most relevant specialists from the existing database.

The right skills were only half of the match

TechPRO works with technicians located across the country. Finding the right technical experience was not enough. The specialist also had to be able to reach the job.

A technician might:

  • work only within their own city;
  • travel to nearby cities;
  • accept work across connected regions;
  • travel anywhere in the country.

A specialist could be an excellent technical match and still be irrelevant if the job was outside their working area. So the matching logic had to account for both capability and real geographic availability.

The system evaluates the task against structured technician data: equipment, control systems, software, previous experience, city, and travel radius.

Send outreach controls with geographic levels in the TechPRO workspace
Detailed technician profile with city, travel radius, equipment, controls and experience

The bot became part of a larger workspace

As the product entered daily work, TechPRO needed more than a bot. Managers needed a workspace where they could see tasks, review candidates, send outreach, track responses, and intervene manually when necessary.

Telegram remained the fastest way to submit information and respond to an offer.

Technician database filtered by a matching request

The workspace became the place where managers controlled the full process.

The system now helps managers:

  • convert the original client conversation into a structured task;
  • filter the technician database using professional and geographic criteria;
  • review profiles and adjust the selection manually;
  • send outreach through Telegram;
  • bring every response back into the task;
  • select the final technician while preserving a clear history of candidates.

From a request to a confirmed candidate

1

The original request enters the system

The manager sends the client conversation to the bot without first rewriting or restructuring it.

2

A structured task is created

The useful details are extracted and saved in a consistent format, ready for matching and further work.

3

The database is narrowed to relevant technicians

The system evaluates professional data and geographic availability. The manager can inspect the results and change the selection.

4

Outreach is sent from the task

The manager chooses the relevant geographic levels and launches outreach directly from the workspace.

5

Technicians respond in Telegram

Each specialist receives a clear description of the job, its location, requirements, and company contact details. They can accept or decline without entering a separate application.

6

Every response returns to the workspace

The manager can see who received the offer, who accepted, who declined, and who has not responded.

Telegram message to a technician with Accept and Decline buttons
Candidate list with Sent, No Response and Declined statuses

Automation without losing control

The system handles repetitive work, narrows the search, and keeps information synchronised. The manager can still:

  • inspect every suggested technician;
  • open the full professional profile;
  • view candidates outside the initial selection;
  • change a candidate's status;
  • assign someone manually;
  • decide how far outreach should expand;
  • choose the final specialist.

It reduces what a manager has to remember without taking away judgement or control.

"I have the coolest technician matching system."

Founder of TechPRO

Built to grow beyond matching

What began as a bot for structuring requests has become the foundation of a broader operational workspace for TechPRO.

The next step planned by the founder is to build on the same foundation and extend the system into contracts, estimates, document generation, successful-deal analytics, and financial records.

The product continues to grow from the way the company actually works, not from features added only to make the system larger.