Custom computer vision software development services · EonTech Poland

Custom computer vision software development, built for production.

EonTech is a Poland-based nearshore engineering partner. We build image and video models that work outside the lab — detection, segmentation, OCR, and quality inspection, trained on your data and deployed to the edge or the cloud. You own the weights, the dataset pipeline, and the code.

Scope a vision build Explore AI & ML

  • Detection & tracking

    Locate and follow objects across frames in real time.

  • Segmentation

    Pixel-level masks for precise area, defect, and boundary analysis.

  • OCR & document vision

    Read text, forms, and labels from images and scans at scale.

  • Quality inspection

    Catch defects on the line faster and more consistently than manual checks.

Why this page exists

What "production" actually means for computer vision

The gap between a Jupyter notebook and a computer vision model that survives a warehouse floor, a manufacturing line, or an operating room is where most CV projects stall. Off-the-shelf models trained on public datasets misfire the moment your cameras, lighting, product mix, or workflow differ from the training set. That is why the useful search term is not "computer vision", but custom computer vision software development services — the whole loop from data to deployment, tuned to one specific operating environment.

EonTech is a Poland-based full-cycle engineering agency covering custom software development, web and mobile, cloud and DevOps, QA, and custom AI/ML model development. Our computer vision practice sits inside that broader engineering team, which is what makes a CV project actually ship — someone builds the annotation tooling, someone else stands up the training platform, someone wires the inference into your app, and the same team monitors drift after launch. No handoffs, no "the model is ready but the integration is another vendor" surprise in month four.

The rest of this page is a practical brief for CTOs, product leads, operations directors, and non-technical founders evaluating a partner for custom computer vision software development services. Everything below is drawn from real delivery — no fabricated accuracy figures, no marketing statistics, no client-list padding.

Key benefits

What EonTech brings to a CV engagement

Six things technical buyers name most often when we ask why they chose EonTech over an offshore CV vendor or a research-only lab.

  • Built on your data, not generic weights

    Off-the-shelf models fail the moment your cameras, lighting, or products differ from the training set. Our custom computer vision software development services train on your imagery so accuracy holds up in your actual environment.

  • Edge, cloud, or hybrid — your choice

    Real-time decisions on-device, heavy batch work in the cloud, or a mix. We optimise the model to the hardware you run, not to a marketing benchmark.

  • MLOps built in, not bolted on

    Data drift monitoring, versioned models, and retraining loops from the start. Accuracy at launch is the easy part — keeping it accurate in month twelve is the whole job.

  • GDPR-native EU delivery

    Poland is an EU member state; our engineers, servers, and data pipelines default to GDPR posture. No third-country transfer questions, DPAs and SCCs ready to sign.

  • You own weights, data, and code

    Model weights, annotation pipeline, training scripts, and inference code are yours from day one. No hidden fees, no vendor lock-in when the engagement rolls off.

  • Senior EU nearshore team

    Live overlap for the whole UK, EU, and Nordics working day, half-day with US East Coast. Real pair sessions and reviews with the engineers writing the code.

Where it pays off

Industries where vision earns its place

Cameras are everywhere. The value is in turning their feed into decisions. We focus on use cases where vision replaces slow manual work or surfaces what people miss.

  • Manufacturing & industrial

    Automated visual inspection, defect detection, count-and-classify on the line, and safety-zone monitoring that keeps production moving without adding manual checks.

  • Logistics & warehousing

    Package and pallet tracking, damage detection, barcode and label reading, and dock-door analytics tuned for varied lighting and cluttered scenes.

  • Retail & e-commerce

    Shelf analytics, planogram compliance, stock-out detection, checkout-free vision, and product-image quality control for marketplaces at scale.

  • Medical & life sciences

    Imaging support models built with the rigour and traceability regulated work demands, from cell counting to imaging pre-screening — always with a human in the loop.

  • Automotive & mobility

    Driver-assist perception, in-cabin monitoring, ADAS data pipelines, and fleet-camera analytics for accident and behaviour signals.

  • Security & smart buildings

    People counting, occupancy analytics, anomaly detection, and privacy-preserving perimeter monitoring — inference on the edge so raw video never leaves the site.

Main use cases

Eight shapes of production computer vision we build

These are the recurring problem shapes where custom computer vision software development pays back the compliance and MLOps overhead. Every one is drawn from real EonTech delivery.

  • Defect & anomaly detection Surface-scratch, missing-component, colour-drift, or dimensional-tolerance checks — replacing slow manual QA with consistent, auditable inspection.
  • Object detection & counting Real-time counting of people, vehicles, boxes, or SKUs across live video or still images, with class hierarchies tuned to your operations.
  • OCR & document intelligence Reading printed and handwritten text, forms, invoices, and IDs — layout-aware so structured data lands in your systems, not free-text blobs.
  • Instance & semantic segmentation Pixel-precise masks for damage assessment, wound care, agricultural analysis, or industrial measurement — where a bounding box is not enough.
  • Pose & activity recognition Human-pose estimation and activity classification for sports analytics, ergonomics, safety compliance, and rehabilitation software.
  • Visual search & similarity Embedding-based visual search for e-commerce, digital-asset management, and image de-duplication at catalogue scale.
  • Video analytics & event triggers Long-form video processed into events — collision, loitering, hand-hygiene, safety-gear compliance — feeding your alerting stack.
  • Synthetic-data generation When labelled real-world data is scarce or sensitive, we generate synthetic training imagery to bootstrap the model and reduce annotation cost.

How it works

A five-phase path from raw imagery to a running model

Every engagement runs the same arc. It ends with a model in production and a monitoring loop keeping it accurate — not a slide deck.

  1. 01

    Discovery & feasibility

    We spend two-to-four weeks with your data, cameras, and operating conditions. Output: a written feasibility, a target accuracy/latency envelope, and a fixed-scope pilot proposal.

  2. 02

    Dataset & annotation pipeline

    We stand up labelling tooling (or plug into yours), agree the class schema, run inter-annotator agreement checks, and build augmentation. Clean, representative data is where the model actually lives.

  3. 03

    Model training & evaluation

    We train candidate architectures, benchmark against your ground-truth split, and pick for the trade-off you actually need — accuracy, latency, energy, or model size.

  4. 04

    Deployment — edge or cloud

    We ship inference where it earns its keep: ONNX/TensorRT for edge, autoscaling inference services in AWS/GCP/Azure for cloud, or both. Model, serving code, and deployment recipes are yours.

  5. 05

    Monitoring & retraining

    Drift monitors watch input distribution and prediction confidence in production. Retraining loops are triggered by real signals, not calendars, and every model is versioned and rollback-safe.

How we deliver

From raw frames to a model that stays accurate

  • Dataset & annotation pipelines

    Vision lives or dies on data. We build labelling, augmentation, and review pipelines so your model trains on clean, representative examples and keeps improving.

  • Edge and cloud inference

    We deploy where the use case needs it. On-device for low latency and privacy, in the cloud for heavy batch work, or both, optimised for the hardware you run.

  • MLOps for the long run

    Versioned models, monitoring for drift, and retraining loops. A vision system is not done at launch; we keep it accurate as conditions change.

  • Owned, not rented

    You keep the model weights, the data pipeline, and the code. We work to GDPR and ISO 27001 standards, with no vendor lock-in.

Trust & compliance

The posture regulated buyers look for

Vision projects run through sensitive data — faces, licence plates, medical images, proprietary product designs. Our posture is set up for buyers whose procurement teams actually check.

  • GDPR-native EU delivery, DPAs and SCCs ready to sign
  • ISO 27001 posture Access reviews, evidence, and change control
  • PII/PHI-aware Face and identifier redaction pipelines when needed
  • Regulated-imaging ready Medical and industrial traceability requirements
  • Reproducible training Data, code, and seeds versioned for audit
  • Your IP throughout Weights, code, and pipelines stay yours

Why EonTech

Senior teams who ship vision end to end

A vision project touches data, models, hardware, and the software that consumes the output. We hold all of it under one contract, so there is no hand-off between the people who train the model and the people who deploy it.

That means a working production model, not a notebook. And it means a system you can keep running long after launch, because the pipeline and the weights are yours.

See our MLOps services

Common questions

What buyers ask before a computer vision engagement

Every question below has come up on a real discovery call — from CTOs, operations directors, and non-technical founders comparing custom computer vision software development services against an off-the-shelf API or an in-house build.

  • What do custom computer vision software development services actually include?

    The full production loop, not just a model file. That means dataset design and annotation pipelines, model architecture selection and training, evaluation against ground truth, deployment (edge or cloud), integration with your app or line, monitoring for drift, and retraining. A CV model is a running system, not a one-off deliverable.

  • Should our model run on the edge or in the cloud?

    It depends on latency, privacy, and cost. On-device inference is best when decisions must be instant or data cannot leave the premises, while the cloud suits heavy batch processing. We often combine the two and optimise the model for the hardware you actually run.

  • We do not have a labelled dataset. Can you still help?

    Yes. We build the annotation and augmentation pipeline as part of the work, set up review so labels stay consistent, and can start from a small seed set and grow it. Clean, representative data is where we begin, not something we assume you have.

  • How do you keep accuracy from degrading after launch?

    We treat vision as an ongoing system. We monitor for data drift, version every model, and build retraining loops so the model adapts as cameras, lighting, and products change. You keep the weights and the pipeline throughout.

  • How much data do you need to build a custom computer vision model?

    It depends on the task. Simple detection tasks with distinctive classes can start with a few hundred labelled examples plus augmentation; fine-grained classification or medical imaging can need tens of thousands. In discovery we scope this precisely and, when real data is limited, we plan synthetic-data or transfer-learning approaches.

  • Which frameworks and tools do you use for custom computer vision software development?

    PyTorch for training, ONNX and TensorRT for edge deployment, and platform-native runtimes (Core ML on iOS, NNAPI on Android, OpenVINO on Intel edge). For MLOps we lean on MLflow, Weights & Biases, and cloud-native model registries — but we can adopt your existing stack.

  • Can you fine-tune large vision-language or foundation models on our data?

    Yes. When a task benefits from a foundation model (SAM, CLIP, YOLO variants, DINO, or vision-language models), we fine-tune or use them behind adapters — always with clear evaluation against a lighter custom model, so you know whether the extra size actually pays back.

  • How do you handle privacy — faces, licence plates, patient data?

    PII/PHI redaction pipelines are built into the data flow: face and licence-plate blurring at the edge, patient-identifier stripping before storage, and access-controlled data stores. For medical work, we apply the same posture used across our healthtech practice.

  • What accuracy can we expect?

    We refuse to quote accuracy without your data — anything else is a marketing number. In discovery we set an accuracy/latency envelope against your ground truth, run a fixed-scope pilot against it, and only then commit to a target. Accuracy claims we cannot verify are exactly the wrong signal to trust.

  • Do you integrate the vision output with our existing software and hardware?

    Yes. As a full-cycle engineering agency we handle the entire loop — model, inference service, API contract, database wiring, and the mobile or web UI that consumes the output. One accountable team from camera to dashboard.

  • What is a realistic timeline and cost for a first vision project?

    A discovery-plus-pilot to a working production model typically runs 10–16 weeks. Cost varies with data availability, deployment target, and integration depth — we quote a fixed first phase after discovery so there is no open-ended commitment.

  • Can you work as a nearshore extension of our existing ML or CV team?

    Yes. We staff-augment vision engineers, MLOps engineers, and data engineers under your management as an alternative to full-project delivery. Same senior bench, different engagement shape.

Related practices

Nearby capabilities at EonTech

Turn your cameras into decisions

Tell us what you need to detect, read, or inspect. We will scope a model, a data pipeline, and a deployment plan built for your environment.

Scope a vision build AI & ML model development