Scope. Ship. Own.

From idea or demo to production: deployed, monitored, owned.

Book a 30-minute scoping call See three ways to work
First builds & POCs Production hardening Live metrics & reports

10 years of production software engineering at Twist Bioscience, Target, ICS, and Greenfield Workshop.

Prior full-time roles. Kaiser AI is an independent practice.

380,000 Devices receiving weekly production deployments across retail point-of-sale systems.
Billions API requests served by an A/B testing platform supporting 400+ concurrent experiments.
11 → 4 Days of product delivery time, after workflow automation at a biotech manufacturer.
Top 4% Global placement, Kaggle histopathologic cancer detection: 97.99% accuracy, CNN ensemble.

Engagements

Three ways to work

Kaiser AI splits work by stage. Find out if the bet is worth making, prove a first version, or harden what already runs and already breaks.

Feasibility Sprint

For teams with a problem statement, a dataset, or an AI idea nobody has pressure-tested.

  • Problem brief with success criteria the team can score
  • Data inventory, access map, and risk register
  • Go / no-go recommendation with a scoped roadmap if it's go
Book a 30-minute sprint scope

Fixed fee. One to two weeks.

First Build

For teams with an idea, a spec, or a notebook that already works on a laptop.

  • Scoped feasibility memo with go / no-go criteria
  • Working first version in the stack the team already uses
  • Eval harness, baseline metrics, and a deploy the team can run
Book a 30-minute First Build scope

Fixed scope. Fixed milestones.

Production Hardening

For teams with a demo that fails under traffic, a model nobody monitors, or a pipeline one person understands.

  • Hardened deploy path with alerts, logs, and live dashboards
  • Metrics and reports that show health now and history over time
  • Tracked backlog of reliability and cost work month to month
Book a 30-minute hardening review

Monthly retainer.

Capabilities

What Kaiser AI ships

Kaiser AI applies production engineering discipline to AI systems. Deploy paths, metrics, and observability ship with the first working version. The team keeps its architecture. Kaiser AI fills the senior capacity gap.

AI systems engineering

  • Scope AI features with go / no-go criteria before build
  • Ship LLM and RAG features behind real APIs and deploys
  • Add evals, alerts, and dashboards to fragile prototypes
  • Instrument pipelines with live metrics and historical reports

Software engineering

  • Ship high-volume APIs and distributed services
  • Run Kubernetes and deployment pipelines at scale
  • Build full-stack product UI for AI features
  • Harden infrastructure so releases stay fast

Technical leadership

  • Own architecture decisions and write them down
  • Map milestones to goals before build starts
  • Align stakeholders on scope, risk, and tradeoffs
  • Leave metrics and reports the team can read without Kaiser AI

Background

How this experience applies

AI work fails without large-scale production engineering. Weekly device fleets, high-volume APIs, and delivery pipelines leave no room for theater.

Kaiser AI brings that discipline to AI systems. Prototypes get scoped, deployed, and instrumented so live metrics and reports show what is running now and what changed.

The ML credentials are real and lighter than the systems depth. Kaiser AI offers production engineering applied to AI. Most demos never close that gap.

How work starts

Wrong bets die early. Not AI theater.

Every engagement leaves artifacts the team can keep. Criteria, risks, milestones, and a system ready to hand off.

Discover

  • Problem brief with named owners
  • Constraints and non-goals memo
  • Success criteria the team can score

Assess

  • Data inventory and access map
  • Feasibility and risk register
  • Go / no-go recommendation

Roadmap

  • Scoped milestones with acceptance tests
  • Sequenced backlog tied to goals
  • Timeline and engagement shape

Execute

  • Working system in the team repo
  • Dashboards, alerts, and historical reports
  • Handoff the team can operate from live signals

Why Kaiser AI

Production discipline.
Applied to AI.

Product and engineering leaders hire Kaiser AI when AI work needs the same deploy, monitor, and observability standards as any production system. Live metrics and reports replace tribal knowledge.

Kaiser AI measures success by what the team can operate after the engagement ends.

Embedded in rituals. Not parked offshore.

Kaiser AI works inside standups, PRs, and the tooling the team already runs.

Production ships. Demos do not count.

Evals, metrics, and dashboards ship with the first working version.

Scope locks before build. Drift gets named.

Fixed milestones or a monthly retainer, written before work starts.

Live metrics stay current. Static docs go stale.

Reports show health now and history over time, so ownership does not depend on one person's head.

Contact

Ready to scope
the work?

Bring a spec, a demo, or a problem statement. In one call, Kaiser AI will say if there is fit, what the timeline looks like, and which engagement shape works.

Based in United States, remote-friendly worldwide

30-minute scoping call

Kaiser AI reviews goals and constraints, then picks the Feasibility Sprint, First Build, or Production Hardening if any fits.

Book a 30-minute scoping call