Service Overview

Privacy & Data Protection

Build systems that work because users keep their data, not despite it.

Most products treat data harvesting as the default and bolt privacy on later. The result is fragile compliance, brittle audit trails, and products that get worse the day a regulator pays attention. We design systems where privacy is the substrate, not the patch.

4 capabilities5 deliverables5 tooling groups

Key Capabilities

What we actually do during the engagement.

Key Capabilities

01Local-first data architecture

User data lives on the user's device or in their account, not in a central pile. Sync happens encrypted; the server stores blobs it cannot read.

02On-device inference and processing

Run AI, search, and analytics where the data is. Private prompts, private documents, private context — all without round-tripping to a model provider.

03End-to-end encryption design

Key management, recovery flows, multi-device sync, and rotation procedures designed for products that need to survive a breach without leaking content.

04Data-minimization audits

Inventory what your system actually collects, why, and for how long. Cut what isn't load-bearing. Document what remains for SOC 2, GDPR, and APPI reviewers.

Our Approach

Build systems that work because users keep their data, not despite it.

Privacy threat modeling, local-first storage, on-device inference, end-to-end encryption, and data-minimization audits — designed so the system stays operable, auditable, and competitive without hoarding user data.

Deliverables

What your team gets, and can keep running after handoff.

Deliverables

01Privacy architecture document

End-to-end map of data flow, storage location, encryption boundary, and retention rules — ready for legal and compliance review.

02Threat model and adversary inventory

Explicit list of who could see what under which failure modes, plus mitigation design for each.

03Reference implementation for the privacy-critical paths

Working code for the local-first store, encrypted sync, key rotation, and consent boundaries — drop-in for your stack.

04Compliance-readiness checklist

GDPR, APPI, and SOC 2 mapping with concrete evidence pointers — the page a reviewer actually needs.

05Operating runbook for incidents and key compromise

Step-by-step procedures for revocation, re-encryption, user notification, and post-incident reporting.

Technology Stack

01On-device inference (MLX, llama.cpp, ONNX Runtime)

Run models locally on Apple Silicon, x86, and consumer GPUs without cloud round-trips.

02End-to-end encryption (libsodium, age, Noise)

Battle-tested primitives for symmetric and asymmetric encryption, signing, and key exchange.

03Local-first sync (CRDTs, Automerge, Yjs)

Conflict-free replicated state for multi-device user data without a centralized authoritative server.

04Key management and recovery

Hardware-backed keys, social recovery, and rotation flows that survive lost devices without server-side custody.

05Compliance and audit tooling

Data-flow scanners, retention enforcement, and audit-trail pipelines that produce evidence on demand.

Results

Case Study: Neuron — Local-first AI Workspace

Neuron is our reference implementation for the privacy-first AI workspace pattern: agents, retrieval, and inference all run on the user's device. We designed and shipped:

Local, encrypted store for notes, files, and signals — nothing leaves the device unless explicitly routed out by the user

On-device AI agents that read local context and act on it — no remote inference, no telemetry, no silent uploads

End-to-end encrypted sync across user devices — server stores ciphertext only; we cannot read your data even if compelled

Bring-your-own model, local or hosted — Neuron stays in charge of what data each model is allowed to see

CONTACT

Ready to ship a product that doesn't depend on harvesting users?

We help teams design and build systems where privacy is the architecture, not the disclaimer.

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