Service 01

AI & Automation

Models trained on your data, wired into the workflow that actually uses them.

Vision and prediction where the decision happens — at the edge when the network cannot be trusted, in the cloud when it can.

9Models in production

4Edge deployments

2 wksTo first working slice

Start a project ↗

Capabilities

01Computer Vision

02Predictive Models

03Workflow Automation

04AI Agents

02 — Process

How the work runs

01Technical discovery

We restate your problem as constraints — power, latency, cost, compliance — and name the ones that conflict.

02Architecture & prototype

A working slice through the riskiest layer, signed off before the roadmap is fixed.

03Build & iterate

Two-week increments with one demo each, priorities re-set in the open.

04Deploy & hand over

Production rollout with monitoring, documentation and runbooks that stay yours.

03 — Stack & industries

The Tools we build with
and the Sectors we serve

Models

PyTorch

TensorFlow

Hugging Face

scikit-learn

Serving

ONNX Runtime

Triton

FastAPI

NVIDIA Jetson

Data

Postgres

DuckDB

AirflowLabel Studio

Sectors we serve

Each sector brings its own certification, uptime and data-handling constraints — we design for them from the first sprint.

01Manufacturing

02Healthcare

03Logistics

04Security & access

05Agriculture

06Retail

04 — Selected work

Built with this service

Kadima Security: Workflow Automation

Software / AI Automation

Temp Hub: IoT Automation Software

IoT / AI Automation

MediTask Pro: Healthcare Workflow Automation

Software / AI Automation

05 — FAQ

Questions we get asked

Still have a question?

Send us the constraint — an engineer, not a sales rep, replies within one business day.

Usually less than people expect. We begin with whatever representative history exists, label a small set together, and use transfer learning to get a first honest accuracy number in two weeks — then decide if more collection is worth it.

Wherever the decision has to happen. If the line cannot wait for a round trip, or the site has no reliable link, inference runs on the device and syncs later. We size that constraint before choosing the model.

Every deployment ships with drift monitoring and a retraining path. You get the evaluation script, the thresholds we agreed, and the runbook for what to do when they move.

Yes. We start with an audit of its current behaviour against your data, then either retrain, replace, or leave it alone and fix the pipeline around it — whichever is honest.

06 — Engagement