Kasten Technologies

Services

Three ways to work together. Each one assumes noisy data, uptime pressure, alert fatigue, and code someone will own after I step back.

Operational AI discovery sprint

Paid technical diagnostic over an agreed window. I inspect how data actually arrives, where the system starts and stops, and what a realistic build would take.

For you if you have telemetry or operational data but need a grounded read on feasibility before a big build.

Buy when architecture is fuzzy, stakeholders disagree, or you need a defensible answer on “can we ship this?”

You get written findings: data reality, recommended approach, risks, and a suggested next build—not a generic strategy deck.

Telemetry / anomaly build sprint

Fixed-scope implementation: ingest and features, baseline detection or models, scoring, and evaluation tied to how operators will actually respond to alerts.

For you if you have data access and a target workflow (API, dashboard, ticket)—you need working software, not another slide.

Buy when discovery is done (with me or on your side) or the problem is narrow enough to scope tightly on day one.

You get runnable code, pipeline boundaries, evaluation hooks, and handoff notes for serving, monitoring, and iteration.

Fractional AI systems lead

Ongoing part-time ownership of technical direction and hands-on execution across data, backend, and ML—one person accountable for design and build quality.

For you if you lack a full-time senior owner for ML/systems work, or you need consistent standards across vendors and internal engineers.

Buy when the roadmap is multi-quarter, multi-stream, or delivery keeps stalling on integration and operations reality.

You get architecture calls, implementation on critical paths, reviews, and clear criteria for what to automate, defer, or stop.

Problems I solve

Offline metrics lie. Models score well in the lab and misbehave on live telemetry—drift, missing sensors, label noise, or conditions you never sampled.

Alerts nobody trusts. Anomaly detection that floods operators or misses real incidents; no agreement on precision/recall tradeoffs for your environment.

Stuck between notebook and production. Pipelines undocumented, serving undefined, no path to monitor, retrain, or hand off without heroics.

Depth gap on the team. Strong domain and product people; you need senior hands-on help on time-series, high-dimensional signals, unsupervised methods, or production ML architecture—not another roadmap facilitator.

Who this is for

Founders and CEOs of industrial technology companies; CTOs; heads of engineering; heads of data or AI; product leaders building telemetry, monitoring, machine-data, or operational products.

Typical contexts: industrial SaaS, fleet or asset health, infrastructure monitoring, plant- or field-adjacent systems—where bad alerts or downtime have real cost.

Not a fit for pure consumer growth apps, unfunded exploration, or staff aug with no technical direction.

Process

Ground in constraints. Ops tempo, latency, hardware interfaces, security, who maintains what. Strategy shows up here as system design, not slides.

Design for deployment. Python you can run and review; APIs and pipelines with clear boundaries; evaluation that reflects field noise and false-positive cost.

Ship in slices. Small milestones with observable behavior—so you can decide what to scale and what to stop before you over-invest.

Kasten Technologies

Kasten Technologies is a solo practice. I’m a systems-level software engineer: I work end-to-end from ingestion and pipelines through backend services, modeling, and serving—not disconnected “AI features” or one-off notebooks that never leave a laptop.

I’m used to messy operational telemetry: gaps, drift, uneven sampling, and alerts that have to earn trust from people on shift. I design for maintainability and production operations, not demo metrics.

Python · data pipelines · backends · time-series & high-dimensional data · unsupervised learning & clustering · production ML · telemetry · system architecture

Contact

Good fit if you have telemetry or machine data and need a deployable anomaly-detection, monitoring, or operational ML path—or you’re unsure your data and architecture are solid enough to build on.

Start with discovery when you want a paid technical read on feasibility, risks, and sequencing before a larger build.

In your first message, include what data you have (sources, access, rough volume), the operational outcome you want, and where the current system breaks (accuracy, false positives, latency, reliability, handoff).

What happens next: I reply within 1–2 business days. If it’s a match, we schedule a short call to align scope and engagement—often discovery first, then a fixed build sprint or fractional lead if it makes sense.

Operational AI and anomaly detection for real-world telemetry