CountrysideSolutions B.V. Discuss a project
Menu Discuss a project

Privacy-first edge AI engineering

Useful AI,
kept close.

We design and deploy tailored AI systems that run where the work happens—on customer-controlled hardware, on premises, or offline.

Bring us the constraintExplore the work

No forced cloud. No generic black box. No data extraction as a business model.

A customer-controlled system boundary connecting field devices, governed data, local models, and operations
Local operationCustomer-controlled boundary
Model adaptedTo the workflow—not the other way around
01Local by design

Sensitive work can stay on site.

02Custom to the constraint

Hardware, data, model, and workflow co-designed.

03Built for recovery

Versioned releases, health gates, and rollback.

Selected systems

Evidence before adjectives.

Deployed work, engineering systems, release candidates, and research concepts are labeled separately—because maturity is part of the product truth.

View the full portfolio
Deployed system

Pyra

A local operations platform shaped around real places, devices, and routines.

Pyra brings household and property operations into a customer-controlled system: local dashboards, purpose-built touch terminals, automation services, connected devices, and compact on-device inference. Its ongoing engineering work emphasizes site-specific configuration, service ownership, testable releases, and recovery paths.

BoundaryPyra is a privately deployed system, not presented here as an off-the-shelf commercial product.
Engineering system · synthetic-data only

TrichoEdge

A governed local imaging workflow from guided capture to edge inference.

TrichoEdge is an engineering system for guided multi-illumination imaging, governed model development, local appliance inference, and private report delivery. The work spans native terminals, an on-premises edge appliance, data and model controls, signed packages, and a hardened static phone viewer.

BoundaryCurrent operation is explicitly synthetic and engineering-only. TrichoEdge is non-diagnostic, and no clinical efficacy, validation, or production-readiness claim is made.
Release candidate

Grassland

Offline-first property maintenance with optional on-device guidance.

Grassland is an Android application for mapping maintained ground, recording work, visualizing coverage, retaining field observations, and generating practical guidance. Core workflows run on the phone without an account, analytics, or telemetry; optional compact-model inference also stays on device.

BoundaryThe application is code-complete for an initial release, but public app-store availability is not claimed.

What we build

The whole edge system.

A useful local model depends on governed data, reliable hardware, clear software contracts, and a deployment path that can recover.

All capabilities
01

Edge AI & embedded inference

We fit the workload to the device rather than forcing a cloud pattern onto the field. Compact language, vision, detection, segmentation, and anomaly models are paired with deterministic code and calibrated measurements.

02

Compact multimodal model adaptation

Domain adaptation starts with a frozen input/output contract, representative data, and a baseline. We fine-tune compact multimodal models on controlled DGX-class infrastructure and promote only declared candidates.

03

Data engineering & governed annotation

Useful AI depends on traceable data. We design capture inventories, consent or usage-rights gates, expert worklists, repeat review, provenance, immutable dataset versions, and separation between development labels and product evidence.

04

Local, on-premises & air-gapped systems

Customer-controlled infrastructure can keep sensitive inputs and operational context close to the work. Connected, outbound-only, intermittently connected, and offline modes are designed as explicit operating states.

Private infrastructure

Local, on-premises, outbound-only, and air-gapped modes.

Compact intelligence

Multimodal adaptation, vision models, rules, and measured systems.

Physical integration

Touch terminals, cameras, sensors, radio, power, and field devices.

How we work

Constrain. Prove. Integrate. Recover.

We start with the decision and its failure cost, not a fashionable model. Every stage ends in evidence or an explicit stop.

See the methodology
  1. 01Discover the real constraint

    We map the operator, decision, environment, connectivity, data sensitivity, hardware, failure cost, and claim boundary before choosing a model.

  2. 02Define data & governance

    Ownership, purpose, retention, export, consent or usage rights, segregation, and deletion are designed with the dataset—not added after training.

  3. 03Build expert annotation

    Domain experts receive a constrained work surface, written rubric, calibration set, and repeat-review path. Provisional labels stay visibly separate.

  4. 04Establish a baseline

    Deterministic rules, classical methods, stock models, and simple compact networks reveal what AI adds—and where sensing or workflow is the real bottleneck.

  5. 05Adapt the model

    We fine-tune only after the contract is stable, preserve run provenance, select declared checkpoints, and keep protected evaluation out of iteration.

Privacy & security

Keep the data path short—and the promises precise.

Local processing can reduce exposure and dependency. It does not remove every risk. We design explicit trust zones, minimize data movement, sign release artifacts, and document what the system does not protect against.

  • Customer-controlled infrastructure
  • Data minimization
  • Offline modes
  • Signed artifacts
  • Threat boundaries
Read our engineering posture

Engineering notes

Lessons from the hard edges.

Original, public-safe notes on compact-model adaptation, accelerator deployment, strict contracts, private delivery, and field power.

All insights

Start with the constraint

Have a constrained problem that cannot depend on the cloud?

Bring the workflow, environment, privacy boundary, and failure cost. We will help determine whether edge AI is useful—and where simpler engineering is better.

Frame an engagementAbout Countryside