Understand how your engineers work with AI.

Connect AI use to engineering judgment—then use the same bar to evaluate the engineers you hire next.

Engineering intelligence for CTOs and VPs of Engineering.

Built for engineering organizations where judgment matters

Platform
Forward deployed
Quant
Infrastructure
AI engineering

See engineering capability, not AI activity.

Americana connects approved agent signals to delivery, verification, review, and recovery. Leaders see where AI creates leverage—and where stronger engineering practice is still required.

Organization view

A working model of how your team builds with AI.

No prompt leaderboard. No employee ranking. Patterns are read in the context of the work they produced.

See the method

Engineering practice Platform team · recent work

How this team works with AI

Signals linked to engineering outcomes

Verification discipline: Consistent
Failure recovery: Developing
Delegation quality: Strong pattern
Review load: Needs context

Illustrative product view. Your team defines the evidence that matters.

One engineering bar—from the team you have to the people you hire.

The assessment comes from your organization’s actual engineering practice, not a generic question bank.

01

Understand real work

Connect approved agent, Git, pull request, test, and delivery evidence at the team level.
02

Define what matters

Turn recurring moments of engineering judgment into role-specific capabilities and review criteria.
03

Evaluate potential hires

Give qualified candidates a private work simulation before your engineers invest in interviews.

See how candidates work before the technical interview.

Candidates solve a realistic engineering problem in an ephemeral cloud environment. AI use is encouraged. Company code, data, and strategy stay outside.

Candidate brief

Explain and repair a baseline divergence.

Determine whether the failure is a defect, expected variance, or a methodology change. Document the evidence before changing code.

Environment: Synthetic repository
AI access: Encouraged
Session: Time-bound

src/reconcile.py tests: 8 passed · 1 failed

def reconcile(current, baseline):
    adjusted = normalize(current)
    delta = adjusted - baseline

    # establish cause before changing behavior
    evidence = classify(delta)
    return Resolution(evidence, adjusted)

Candidate to AI Before changing code, list the evidence that would distinguish a defect from an expected baseline move.

Session evidence Investigation sequence, tool choices, verification, and final rationale are retained for review.

Session ledger

Token budget: Visible
External LLMs: Allowed
Hidden tests: Enabled
Company material: None

Private by construction. No company source material enters the candidate environment.

Ephemeral by default. Workspace destroyed after the session.

Evidence over theater. Every finding traces back to work completed in the session.

AI use is part of the engineering capability being assessed.

Americana product principle

Start with one team or one open role.

We’ll shape a private pilot around the way your engineering organization actually works.