Turn stalled AI projects into
clear technical decisions.

Kasten helps companies evaluate AI initiatives that are stuck between demo and production. We identify whether the project is viable, what is blocking it, and whether leadership should continue, redesign, or stop investing.

For companies with stalled AI pilots, unclear vendor claims, unreliable prototypes, or no clear path to production.

10 daystypical
assessment
Continueredesign
or stop
Vendor-neutralevidence-based
review

01

The problem

AI projects do not usually fail in the demo.

They fail when the company tries to make them reliable, integrated, measurable, and operationally useful. The risk is not just failed software — it is wasted budget, unclear technical direction, vendor dependency, and leadership continuing to fund work that may never become a product.

The expensive mistake is not a failed prototype. The expensive mistake is continuing to fund an initiative after the evidence already says it will not become a product.

Most companies already have activity. What they lack is a clear answer on whether the initiative can realistically reach production — and whether it deserves more budget.

Recognizable failure patterns:

01The prototype works only on controlled examples.
02The data is incomplete, inconsistent, or not production-ready.
03The vendor cannot clearly explain limitations, architecture, or failure modes.
04Users do not trust the output.
05The AI system is not integrated into the real workflow.
06Nobody owns production reliability, monitoring, or feedback loops.
07Leadership cannot connect the project to measurable business value.
08Activity is being mistaken for progress.

Qualification

When to bring in Kasten

Kasten is a fit when an AI initiative has already consumed time, budget, or executive attention — but leadership still cannot answer whether it should continue.

A proof-of-concept worked, but production adoption stalled.

A vendor is making claims leadership cannot verify.

Internal teams are experimenting, but no product path exists.

Data quality problems are slowing or undermining the work.

Users do not trust the output.

The system is not connected to real workflows.

The expected ROI is unclear.

Leadership needs an independent go/no-go recommendation.

This is not for companies casually exploring AI. It is for organizations that have already invested time, money, or executive attention into an AI initiative and need a serious technical decision before committing more budget.

The failure pattern is common.

Research from Gartner, RAND, and MIT points to the same pattern: many AI initiatives fail after the proof-of-concept stage because of poor data readiness, unclear business value, weak integration paths, vendor overpromising, and lack of production ownership.

Kasten exists for that failure point.


02

The offer

AI Viability Assessment

Many AI projects work in a demo but fail before becoming reliable products. Kasten helps leadership determine whether an AI initiative is technically viable, commercially useful, realistic for production, and worth continued investment.

A focused assessment for companies that have already started an AI initiative and need to know whether it can become a real product.

What is reviewed

  • Use case and business-value assessment
  • Data readiness review
  • Prototype and model feasibility review
  • Architecture and integration review
  • Vendor and team capability review, if relevant

Assessment deliverables

  • Production risk assessment
  • Cost and ROI sanity check
  • Clear recommendation: continue, redesign, or stop
  • 30/60/90-day execution roadmap

Typically 10 business days. Ends with an executive-ready recommendation: continue, redesign, or stop.

What leadership receives

At the end of the assessment, leadership receives a written recommendation:

Continue

The initiative is viable, with a defined production path.

Redesign

The direction is promising, but the current approach will not work.

Stop

The project is unlikely to become a reliable product without disproportionate cost or risk.

What Kasten evaluates

01

Use case viability

Whether the problem is real, scoped correctly, and worth solving with AI — not just feasible in a demo.

02

Data readiness

Quality, availability, labeling, governance, and whether the data can support production use.

03

Model and prototype reliability

Whether current outputs hold up outside controlled examples and can be trusted by users.

04

Architecture and integration path

How the system connects to real workflows, systems of record, and operational constraints.

05

Vendor and team capability

Whether internal teams or vendors can actually deliver — and whether their claims match reality.

06

Production risk

Monitoring, failure modes, ownership, security, compliance, and what breaks at scale.

07

Cost and ROI reality

Whether projected value holds up against build cost, run cost, and time to production.

08

Execution roadmap

What must happen next if leadership chooses to continue — and what to stop funding if not.

Need this kind of decision on an active AI project?

Request an AI Viability Assessment

Not staff augmentation, generic AI consulting, or advisory-only work. Vendor-neutral technical judgment with a clear go/no-go recommendation.


03

Engagements

Two paths. One technical standard.

Start with the assessment. Continue with execution leadership only if the initiative deserves it.

Bounded diagnostic

AI Viability Assessment

A focused review of a stalled AI initiative — use case, data, prototype, architecture, vendor claims, and production risks — ending in a clear executive recommendation.

  • Evidence-based review of what exists today
  • Vendor-neutral evaluation of claims and gaps
  • Production readiness and integration assessment
  • Continue, redesign, or stop recommendation
  • 30/60/90-day roadmap if continuing

Typically 10 business days. Ends with an executive-ready recommendation: continue, redesign, or stop.

Ongoing engagement

Execution Leadership

Ongoing technical ownership to redesign, productionize, or oversee the initiative after the assessment — when leadership has decided the project is worth continuing.

  • Architecture redesign and production path
  • Integration into real workflows and systems
  • Vendor and engineering alignment
  • Monitoring, reliability, and feedback loops
  • Execution ownership through delivery

Retainer or project-based · After assessment or by direct inquiry


04

About

Hands-on engineering judgment.

Kasten Technologies is led by a hands-on software engineer and AI systems architect with experience building production software, AI and data systems, backend platforms, and operational technology.

The work is grounded in practical engineering judgment: what can be built, what can be trusted, what can be operated, and what should not receive more budget. Not advisory frameworks. Not vendor sales narratives. Evaluation grounded in what is realistic for production.

Background:

AI & Data Systems Backend Architecture Production Software Operational Technology Technical Leadership System Integration ML Production Systems

The question Kasten answers: Can this AI initiative become a real product — and is it worth continuing before more budget gets burned?


Next step

Before you fund another quarter of AI work, get a clear technical decision.

If your AI initiative is stuck between demo and production, Kasten can help determine whether to continue, redesign, or stop.


Contact

Request an AI Viability Assessment.

Describe the AI initiative, what stage it is in, and what is blocking a production decision. No pitch deck required.

What happens next: A response within one to two business days. If there is a fit, we schedule a direct conversation.

For CEOs, COOs, CTOs, CFOs, and leaders responsible for AI initiatives that are not producing value.

What is 6 + 16?