Your enterprise customers are asking your security team whether the AI features in your mobile app send their data to a third-party LLM provider. The answer is yes. Three deals stalled at security review last quarter.
The deals didn't stall because enterprise security teams are unreasonable. They stalled because you couldn't produce a SOC 2-aligned architecture document that addresses confidentiality and processing integrity for your AI features. On-device AI changes that answer structurally - and it changes the answer in a way your security team can put in front of a prospect's CISO.
The Project Shape
Four decisions determine whether your AI features clear enterprise security review or continue to stall deals at the finish line.
SOC 2 Trust Service Criteria coverage. Processing integrity and confidentiality criteria apply to AI features that touch customer data. An on-device model satisfies confidentiality structurally: data that never leaves the customer's device cannot be accessed by a third party, regardless of what happens at the AI provider. Your auditor needs documented evidence of this architecture - network flow diagrams, data handling attestations, and model storage documentation - not just a policy statement.
Subprocessor disclosure. If your SOC 2 report lists AI API providers as subprocessors, your enterprise customers' security teams will pull those providers' own SOC 2 reports and examine the scope and exceptions. Each additional subprocessor is a surface area in your security review. Removing the AI API provider by moving to on-device eliminates that subprocessor from your disclosure list and from your prospects' vendor review queue.
Incident response scope. A security incident at a cloud AI provider that processed your customers' data is potentially a reportable incident under your enterprise contracts and under the data breach notification laws that apply to your customers' industries. On-device processing removes that external dependency from your incident response surface entirely. Your security team doesn't have to monitor a third party's incident disclosures to know whether your customers are affected.
Model security review. An on-device model is a piece of software distributed in your app. It needs to be reviewed for prompt injection vulnerabilities, adversarial input handling, and data leakage through model outputs before it ships - the same way your backend API endpoints are reviewed. Most teams skip this step on the assumption that on-device is inherently secure. It's more secure than cloud. It's not automatically secure.
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.
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