EU AI Act-Ready On-Device AI for Employment and HR Mobile Apps in 2026 (Fixed-Price, Money-Back)

Your CHRO's legal team has confirmed that your AI screening and performance assessment features are high-risk under the EU AI Act. Your product team built them without a conformity assessment.

The gap between those two facts isn't just a compliance problem - it's a liability problem. Deploying a high-risk AI system without a conformity assessment is a regulatory infraction. The path to remediation is faster if you approach it with the right architecture partner.

The Project Shape

Four decisions determine whether the conformity assessment your legal team needs closes in 6 weeks or becomes a multi-quarter project.

High-risk scope definition. The Act lists AI in employment decisions - recruitment screening, performance monitoring, promotion assessment, and termination support - as high-risk. If your app touches any of these decisions, even as an assisting tool rather than a decision-making one, the full compliance framework applies. Defining scope precisely changes the conformity assessment work by 30-50%. A feature that surfaces candidate information without ranking or scoring may fall outside the high-risk boundary. Getting that determination from your legal team before the assessment work begins saves weeks.

Transparency to affected workers. High-risk HR AI requires that workers be informed when AI is used to evaluate them. The disclosure mechanism has to be built into the app UI - not added as a clause in the employment contract or a footnote in the privacy policy. Workers need to be able to see that AI is active, understand its purpose, and know who to contact with questions. The disclosure architecture has to satisfy the Act's transparency requirements before deployment.

Bias and fairness testing. The Act requires testing for discriminatory outcomes across protected characteristics before a high-risk HR AI system is deployed. If your engineering team doesn't have a testing methodology for this, building one before the conformity assessment is required. The testing has to cover age, gender, race, disability, and any other characteristics protected under the employment laws of the jurisdictions you operate in.

On-device vs server model. An on-device model that processes evaluation data locally creates a different data flow from a server model. The Act's data governance requirements apply to both, but a local model's audit trail is easier to demonstrate. There is no external API call to log, no subprocessor to document. On-device HR AI is also a stronger transparency story to give affected workers: their evaluation data never left their own device during processing.

Most teams spend 4-6 months discovering these decisions by building the wrong version first. A team that has shipped this before compresses that to 1 week.

The Off Grid Anchor

We built Off Grid because we hit every one of these problems in production. Off Grid is the fastest-growing on-device AI application in the world, with 50,000+ users running it today. It's open source, with 1,650+ stars on GitHub and contributors from across the world. It has been cited in peer-reviewed clinical research on offline mobile edge AI. Every decision named above - model choice, platform, server boundary, compliance posture - we have made before, at scale, for real deployments.

The Delivery Shape

The engagement is four sprints. Each sprint is fixed-price. Each sprint has a named deliverable your team can put on a roadmap.

Discovery (Week 1, $5K): We resolve the four decisions - model, platform, server boundary, compliance posture. Deliverable: a 1-page architecture doc your CTO can take to the board and your Privacy Officer can take to Legal.

Integration (Weeks 2-3, $5K-$10K): We ship the on-device model into your app behind a feature flag. Deliverable: a working build your QA team can test against real workflows.

Optimization (Weeks 4-5, $5K-$10K): We hit the performance and compliance targets from the discovery doc. Deliverable: benchmarks signed off by your team.

Production hardening (Week 6, $5K): Edge cases, OS version coverage, app store and compliance review readiness. Deliverable: shippable build.

4-6 weeks total. $20K-$30K total. Money back if we don't hit the benchmarks. We have not had to refund.

"They delivered the project within a short period of time and met all our expectations. They've developed a deep sense of caring and curiosity within the team." - Arpit Bansal, Co-Founder & CEO, Cohesyve

The Close

Worth 30 minutes? We'll walk you through what your version of the four decisions looks like, what a realistic scope and timeline would be for your app, and what your compliance posture and on-device target mean in practice. You'll leave with enough to run a planning meeting next week. No pitch deck. If we're not the right team, we'll tell you who is.

Book a call with the Wednesday team