Customer privacy
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Local AI Infrastructure
Private AI systems under control.
Local infrastructure, private data workflows, and customer-controlled deployments for teams working with sensitive systems.
Apple SiliconOn-prem GPUPrivate cloudCustomer VPCAir-gappedEdge devices
Local AI, private data, implementation support.
We work close to the implementation surface: architecture, prototypes, deployment, review, and handoff.
Commercial secrets
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Patent-sensitive R&D
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Regulated workflows
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Edge latency
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Air-gapped systems
Boundary, retention, access, and evaluation requirements must be explicit before model work begins.
Work close to the real environment.
FDE keeps engineering close to actual data constraints, runtime paths, and user feedback. We collaborate on-site when needed, and remotely when that is the better way to move fast.
Private data boundary
Source categories, movement constraints, retention rules, and review expectations are mapped before training begins.
Products for local, private infrastructure.
Random Walk builds products for customer-controlled workflows, local data infrastructure, privacy-preserving execution, and settlement-aware coordination.
[PRODUCTION-TOOL]
Local AI for Apple Silicon.
Run, adapt, benchmark, and serve models on-device through local workflows, keeping inference and evaluation close to the machine.
Read more ->Local AI Runtime / On-Device Inference / Privacy Infrastructure
- 01Local AI Runtime
- 02On-Device Inference
- 03Privacy Infrastructure
[PRODUCT-EXPERIMENT]
Token-metered agent work.
A marketplace for agent runtime work, where usage can be metered by token output and settlement can follow streamed execution.
Agent Runtime Work / Usage-Based Settlement / Streamed Execution
Read more ->
[REFERENCE]
Community settlement over Fiber.
Bring CKB Fiber-based tipping, creator rewards, balances, and withdrawals into community platforms without forcing users through complex on-chain flows.
Community Rewards / Fiber Network / Settlement Infrastructure
Read more ->Applied systems under real constraints.
Selected work from Random Walk shows how private AI, local data infrastructure, and customer-controlled deployment patterns can be shaped for specific teams and operating environments.
operations / internal tools
Private model workflow for operations and internal tools
Example engagement pattern for teams building private model workflows around internal procedures, customer-sensitive records, automation, and controlled deployment.
research / IP
Private model workflow for research and IP-heavy studios
Example engagement pattern for research and IP-heavy studios that need private model workflows over proprietary knowledge.
content / design / production
Local model workflows for content and production teams
Example engagement pattern for studios that want local model workflows around style, production assets, reusable knowledge, and review sets.
Melix
A local AI runtime and model workbench for Apple Silicon.
Melix supports local model experiments and operations: model loading, LoRA, evaluation, CLI workflows, local server runs, and evidence records.
Capability areas for constrained systems.
Private AI systems, local data infrastructure, deployment architecture, privacy boundaries, and customer-controlled infrastructure.
Dataset Preparation for Model Training
Convert domain material into structured Train / Validation / Test datasets that can be reviewed, versioned, and used for local model adaptation.
Deliverables: Dataset package / Dataset manifest / Split rationale
Deliverables
- 01Dataset package
- 02Dataset manifest
- 03Split rationale
LoRA Adapter Development
Train adapters for customer-specific tasks and deliver them with training records, model references, evaluation summaries, and activation notes.
Deliverables: LoRA adapter / Training configuration / Training run record
Fused Model Delivery
When contracted, package tuned behavior into privately licensed fused models for customer-controlled environments.
Deliverables: Fused model artifact / Private license terms / Deployment notes
Evaluation & Evidence
Produce reviewable evaluation artifacts so security, compliance, and engineering teams can reason about model behavior before deployment.
Deliverables: Task evaluation / Regression checks / Test set summaries
Private Deployment
Deploy model systems across Apple Silicon, on-prem GPU servers, private cloud, customer VPC, air-gapped systems, and edge devices.
Deliverables: Deployment runbook / Runtime configuration / Access path
FDE Support
Support remote and on-site implementation, especially while a private model workflow is being introduced into real use.
Deliverables: FDE introduction support / Remote engineering support / Support handoff
Boundaries, evidence, responsibility.
Private deployment, least privilege, audit logs, evaluation evidence, and ownership are part of the delivery, not afterthoughts.
Constraint registerCaptures privacy, compliance, and deployment constraints.
Dataset manifestDocuments sources, transformations, exclusions, and retention.
Training run recordCaptures model, dataset, adapter, runtime, and parameters.
Evaluation reportPreserves behavior tests, benchmark results, failures, and limits.
Deployment runbookExplains installation, access, monitoring, rollback, and ownership.
Change logTracks model, data, adapter, and runtime changes over time.
A technical studio, not a software shelf.
Random Walk works like an engineering studio: understand constraints, design the structure, verify the result, then hand the system into real operation.
Privacy engineering / Data platforms / Distributed infrastructure / Cryptographic systems / Local-first product design / AI/ML systems
Technical notes from private AI systems work.
Short approved notes on deployment boundaries, local model workflows, LoRA review, evaluation evidence, and systems that can be operated, reviewed, and maintained.
How to evaluate a local LoRA adapter before deployment
What to review before a fine-tuned adapter is used inside a customer-controlled environment.
Read more ->Define the deployment boundary before choosing a model
Why private model work starts with data movement, runtime location, and access paths.
Read more ->Next step
Bring a constrained system.
If you are working with private data, local infrastructure, model workflows, or deployment boundaries, send the context. We will review whether Random Walk is a fit for the work.
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