Selective Assessment
Apply for an AI Engineer Fit Assessment
A selective assessment for engineering organizations evaluating whether complex systems, recurring investigation burden, and context-heavy workflows are a fit for a deeper AI Engineer deployment.
Not every engineering organization is ready for a serious AI Engineer model. And not every team has the kind of system complexity where that model creates meaningful leverage. smpl is built for environments where meaningful AI-assisted engineering depends on understanding the system before acting inside it.
§ 01 · WHAT THIS ASSESSMENT IS
A first look at whether your environment is a fit for an AI Engineer model.
This assessment is designed to evaluate whether your system shape, workflow burden, and engineering context make you a strong fit for a deeper smpl deployment.
We are looking at whether the environment is complex enough, costly enough to repeatedly rediscover, and operationally constrained enough that a more serious AI Engineer approach could create meaningful leverage.
This is not a general AI consultation. It is a selective fit assessment for organizations considering a more grounded model of AI-assisted engineering.
§ 02 · WHO IT'S FOR
Best fit for organizations carrying real system burden.
Complex Codebase
Monorepos, multi-service architectures, or systems where investigation precedes implementation.
Senior-Engineer Drag
The same few people are constantly pulled into the same clarification loops on tickets.
Underspecified Work
Tickets routinely arrive without enough structure to safely begin implementation.
Restart Cost
Investigations frequently restart from zero because prior context is not preserved.
§ 03 · WHAT THIS MAY SURFACE
What this assessment may surface.
What we're looking for
- Whether the codebase is complex enough that grounded AI engineering meaningfully changes throughput
- Whether investigation drag is high enough to justify a heavier model
- Whether the existing workflow can absorb the additional output without bottlenecking elsewhere
- Where smpl should enter first — investigation, memory, or execution
- Whether a more serious AI Engineer engagement appears justified
§ 04 · WHY IT'S SELECTIVE
Why not every assessment is accepted.
An AI Engineer model is not the right answer for every team. Greenfield startups, simple codebases, and teams looking for a generic coding copilot are not the environment we are built for.
We prioritize organizations with real system complexity — environments where the cost of incorrect implementation, recurring investigation, and tribal knowledge is already showing up in delivery, onboarding, and support.
The goal is not maximizing applications. The goal is identifying organizations where a serious AI Engineer deployment can genuinely change how engineering work happens.
§ 05 · APPLY
Apply for an AI Engineer Fit Assessment.
Every application is reviewed manually. We respond when there appears to be a strong fit.
Submitted. We'll review and respond if there appears to be a strong fit.
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If there appears to be a strong fit, that is where the real conversation starts.
Every application is reviewed manually. If the fit looks strong, we'll follow up to better understand the environment and determine whether a deeper next step makes sense.
That next step may be a Codebase Intelligence Review, an Investigation Readiness Review, or a deployment-track conversation depending on where the leverage appears first.