AI Solutions · Computer vision

Computer vision for attendance, documents and checks.

We build focused vision features that solve one clear problem, such as face punch-in at a shared device or reading fields from a form. We test them in your real conditions before rollout.

Start a project What’s included

PipalHR · Branch attendance

Shared device · Branch B

Good morning, Priya

Punched in at 09:28 · Shift 09:30 to 17:30

Start breakPunch out

In short

What is computer vision?

Computer vision is a field of AI that lets software interpret images and video, such as recognising faces or reading text from documents. technocity builds practical vision features, including face attendance on shared devices and document data extraction, with consent flows, accuracy testing in real conditions and manual fallbacks when recognition fails.

Sound familiar?

The problems we solve

Buddy punching on shared attendance devices

Staff type details from IDs and forms by hand

Paper and scanned records cannot be searched

Off-the-shelf vision tools fail in your lighting or layout

What’s included

How we can help

Face attendance on shared devices

Face punch-in on a shared tablet or phone at each site, with liveness checks and a manual fallback.

from 6 weeks

Document data extraction

Text and fields read from forms, invoices and IDs into your system, with low-confidence items sent for review.

from 4 weeks

Vision proof of concept

A short test of whether a vision task works on your own images before a full build.

from 2 weeks

Accuracy monitoring

Regular checks of recognition errors and retuning as devices, lighting or documents change.

ongoing

Timelines are typical starting points. Every project is scoped with you first.

What you get

A working vision feature in your app or device
Consent and enrolment screens for face data
An accuracy report from tests in your conditions
A review queue for low-confidence results
Manual fallback for when recognition fails
A data retention and deletion process for images

Tools and technology

OpenCVGoogle ML KitTensorFlow LiteTesseract OCRGoogle Cloud VisionPythonKotlin

Get a scoped estimate

How it works

From first call to results

Collect sample images

We gather images from your real devices, sites or documents, with consent where people are involved.

Test feasibility

We measure how well existing models handle your samples before promising anything.

Build with fallbacks

We add review queues, manual options and consent steps around the vision model.

Pilot on one site

We roll out to one location or document type, check errors, then expand.

Proof

Related work

Professional services

HR and payroll for several companies, one login

Medcity companies

Development, PipalHR

An HR and payroll platform for hundreds of staff across several companies and branches, replacing an off-the-shelf payroll app. It became the foundation of PipalHR.

Read the case study

Questions

Common questions

How does face recognition attendance work?

Face recognition attendance works by enrolling each employee's face once, then matching their face at a shared device when they punch in or out. A liveness check helps stop photos being used. A manual fallback is needed for when recognition fails.

Is face recognition attendance legal?

Face recognition attendance is legal in many places but is treated as biometric data, which often requires explicit consent, a clear purpose and strict retention rules. Rules vary by country, so employers should check local law. We build consent, opt-out and deletion into the system.

Can AI read data from scanned forms?

Yes, AI can read printed text and many handwritten fields from scanned forms and turn them into structured data. Accuracy depends on scan quality and layout. We send low-confidence fields to a person for checking instead of saving them automatically.

How accurate is computer vision?

Computer vision accuracy depends heavily on image quality, lighting, camera angle and how similar real conditions are to the test data. It can work well for focused tasks but is never perfect. We measure accuracy on your own images before rollout.

Do you store images of employees' faces?

Face attendance systems usually store a mathematical template of each face rather than raw photos, though some keep images for audit. We agree what is stored, where, for how long and who can access it, and provide a way to delete data on request.

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