task boundaries / dataset package
Model adaptation for private workflows.
We adapt models around a defined task, then deliver usable artifacts, evaluation evidence, and activation notes for controlled deployment.
Read more ->Services
Services for local AI and private infrastructure.
Random Walk helps teams design and implement systems where data, models, workflows, or deployment environments need to stay under control.
Dataset Package / LoRA Adapter / Fused Model / Evaluation Report / Deployment Runbook / On-site / Remote Support
Discuss a project
Modules
Compose modules around the model workflow.
Data cleaning, training sample preparation, LoRA adaptation, model fusion, model loading, evaluation, evidence records, local runtime, private deployment, and runbooks.
source register / dataset manifest
Private data packages for model work.
We shape internal material into bounded, reviewable data packages for adaptation, retrieval, evaluation, and local product workflows.
Read more ->runtime topology / access path
AI infrastructure your team can operate.
We design runtime shape, access paths, deployment assumptions, diagnostics, and rollback notes around your real operating environment.
Read more ->privacy boundary / access assumptions
Data boundaries before model work.
We define what customer material can be touched, moved, transformed, retained, or excluded before AI implementation begins.
Read more ->customer VPC / private cloud
AI inside customer-controlled boundaries.
We build private AI systems for scoped operating environments where runtime, data packages, model artifacts, access paths, and rollback procedures stay under customer-defined control.
Read more ->Use cases
For professional work where model capability and data control both matter.
Research / IP-heavy studios
For teams working with proprietary research, invention notes, expert knowledge, contracts, and confidential domain material.
- • Private document reasoning
- • Patent / prior-art support workflows
- • Confidential drafting assistance
- • IP-sensitive data boundary control
Content / design / production teams
For studios that need AI support around internal style, reusable knowledge, production assets, and local review workflows.
- • Studio knowledge workflows
- • Private drafting support
- • Reusable evaluation sets
- • Local model adaptation
Operations / internal tools teams
For companies that need model workflows around internal procedures, customer-sensitive records, automation, and controlled deployment paths.
- • Private knowledge assistants
- • Secure document workflows
- • Workflow automation
- • Model evaluation evidence
Method
The work starts from a system boundary.
Data cannot leave a controlled environment, a model workflow needs review, an internal system needs local inference, or a team needs implementation support around an existing stack.
01
Private Data
02
Dataset Package
03
LoRA Adapter + Evidence
04
Fused Model
05
Evaluation Report
06
Deployment Runbook
OPS
FDE Support
Modules
Compose modules around the model workflow.
Artifact 01
Dataset Package
A versioned package of source categories, transformations, exclusions, splits, and review notes before training begins.
Core model artifacts
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.
- Dataset package
- Dataset manifest
- Split rationale
Additional deliverables
- Preprocessing notes
- Redaction / exclusion notes
- Data movement register
LoRA Adapter Development
Train adapters for customer-specific tasks and deliver them with training records, model references, evaluation summaries, and activation notes.
- LoRA adapter
- Training configuration
- Training run record
Additional deliverables
- Evaluation report
- Activation notes
Fused Model Delivery
When contracted, package tuned behavior into privately licensed fused models for customer-controlled environments.
- Fused model artifact
- Private license terms
- Deployment notes
Additional deliverables
- Known limitation notes
Evaluation & Evidence
Produce reviewable evaluation artifacts so security, compliance, and engineering teams can reason about model behavior before deployment.
- Task evaluation
- Regression checks
- Test set summaries
Additional deliverables
- Limitation register
- Next-iteration recommendations
Operating model
Private Deployment
Deploy model systems across Apple Silicon, on-prem GPU servers, private cloud, customer VPC, air-gapped systems, and edge devices.
- Deployment runbook
- Runtime configuration
- Access path
Additional deliverables
- Rollback plan
- Operator checklist
FDE Support
Support remote and on-site implementation, especially while a private model workflow is being introduced into real use.
- FDE introduction support
- Remote engineering support
- Support handoff
Additional deliverables
- Optional continuous tuning by contract
Deployment modes
Deploy inside the boundary you define.
Apple Silicon / on-device
Local model iteration for teams and individual developers.
Evidence: Device/runtime setup notes.
On-prem GPU server
Training and inference inside company-owned compute.
Evidence: Environment record and operator runbook.
Private cloud
Dedicated private infrastructure with controlled access paths.
Evidence: Architecture diagram and access notes.
Customer VPC
Deployment inside the customer's own approved cloud boundary.
Evidence: Data movement register and runtime record.
Air-gapped environment
Systems designed for restricted or disconnected environments.
Evidence: Transfer procedure, update path, and evidence handling notes.
Edge devices
Inference near devices, operators, sensors, or industrial workflows.
Evidence: Fleet update model and lightweight runtime notes.