Audience
Professional teams / studios
About Random Walk
Build AI systems that stay under your control.
Random Walk is a private AI technology studio. We work with professional teams, independent studios, and companies that need clear data boundaries to deploy systems in their own environments.
Field Deployment Engineering
FDE makes engineering decisions close to the real environment.
FDE (Forward Deployed Engineering, on-site engineering collaboration) connects models, data, deployment environments, and user feedback. Projects do not have to be on-site by default; we collaborate on-site when needed and remotely when that is the better path.
Mode
FDE collaboration
Output
Private AI systems
Principles
Clear, restrained, verifiable.
We prefer clear boundaries, a small number of important capabilities, and engineering results backed by evidence. Every training run, merge, evaluation, and deployment should explain source, method, risk, and responsibility.
Principle 01
Start from the real problem
We do not sell a model first. We first understand why the customer needs private AI.
Principle 02
Keep the system under control
Data, models, and runtime environments need to fit the customer's own boundaries.
Principle 03
Deliver reviewable results
Training, evaluation, deployment, and limitations should be clear to the customer team.
Principle 04
Move the project into real use
Through FDE collaboration, we connect prototype, deployment, and handoff.
Delivery
Define the work from deliverables.
Each project clarifies data scope, model path, runtime environment, acceptance criteria, and maintenance model before moving into adaptation, evaluation, deployment, and support.
Start from the boundary
Where data lives, where models run, how results move, and who approves each step.
Keep the capability focused
Build the few model workflows that matter first, then expand only when the evidence supports it.
Leave work that can be owned
Runtime behavior, limitations, evaluation records, and follow-up support stay clear after handoff.