Measure real AI engineering habits with evidence, not guesswork

See how your team uses AI in real code, then turn that data into coaching, skill growth, and clear visibility for managers. No generic training. No guessing who's ready. Just real work, measured and coachable.

Schedule Demo See Upskill Use Case

Manager Visibility

See where judgment is strong, where AI habits drift, and where coaching is needed.

See manager visibility

Role Pathways

Map growth from foundations to senior AI judgment, matched to your stack.

See role pathways

Coaching Outputs

Give managers narrative reports, heatmaps, and clear next steps.

See coaching outputs

Whitelabel Ready

Run the full platform under your brand, domain, and workflow.

See whitelabel fit

See where AI judgment is strong, weak, and coachable

Learn turns AI-augmented engineering work into a surface managers can actually use. See how engineers frame context, prove outputs, stay inside trust boundaries, and leave a reviewable trail.

Context sufficiency, validation discipline, and ownership on one surface

Replayable evidence from traces, screenshots, and exact commands

Signals leaders can use for coaching, team planning, and rollout readiness

See manager visibility

Manager visibility

Signals from real delivery work

Team signal · Q2Trending up

Context sufficiency82%

Validation discipline64%

Ownership & verification71%

Evidence from traces, screenshots, and exact commands

Trusted by engineering teams worldwide

4.9

Manager visibility

See where AI judgment is strong, weak, and coachable

Learn turns AI-augmented engineering work into a surface managers can actually use. See how engineers frame context, validate output, stay inside trust boundaries, and know when human review should take over.

Context sufficiency

Review what engineers supplied before the prompt, from repo rules and constraints to the acceptance criteria that shape the run.

Evidence contract

Separate teams that ship screenshots, traces, and exact commands from teams that still rely on polished summaries.

Bounded delegation

Coach the difference between using an assistant for one constrained task and handing away ownership when the work gets ambiguous.

AI Readiness

AI engineering readiness score

The AI Readiness Score gives managers a readable baseline for how engineers work with AI under real delivery conditions.

/ 100

Overall readiness

Benchmark model

Team score vs. role baseline

Dimensions are scored on a ten-point scale and compared against the benchmark marker in each row.

Manager-visible

Evidence stays coachable

Prompt traces, test proof, and approval records keep AI work reviewable after the session ends.

Capability dimensions

AI readiness by operating behavior

Benchmark marker shown on each row

Harness Engineering

Sets repo instructions, validator loops, and approval defaults before AI touches real delivery work.

8.8

Team scoreBenchmark marker

Validation Discipline

Demands tests, Playwright proof, and exact commands instead of shipping what only looked right.

8.4

Team scoreBenchmark marker

Trust Boundaries

Treats prompt injection, retrieval filters, and MCP output as engineering controls, not prompt wording.

8.0

Team scoreBenchmark marker

Runtime Observability

Captures traces, screenshots, and run metadata so drift and failure modes can be replayed.

7.7

Team scoreBenchmark marker

Context Hygiene

Keeps repo-local guidance, specs, and operating rules current instead of leaning on heroic prompts.

7.5

Team scoreBenchmark marker

Agent Orchestration

Routes work to narrow specialists with stop conditions, validator contracts, and human gates.

7.2

Team scoreBenchmark marker

Readiness by team

Product engineering82

Frontend76

Platform71

Data63

Legacy systems48

Governance snapshot

Audit trail coverage100%

Prompt review ready94%

Avg AI contribution18%

Policy exceptions0

Recommended action

Validation discipline is already strong. The next coaching move is to tighten trust-boundary handling, role-specific orchestration habits, and when teams stop for human review.

Look for engineers who set the harness up properly, challenge plausible output, and keep ownership of validation, boundaries, and escalation when the assistant gets involved.

From foundations to senior AI judgment

Build role pathways around the work your team actually ships

Define what good AI-augmented engineering looks like at each level, then give engineers a path grounded in real work instead of generic AI literacy content.

Learning pathways

Progress engineers with visible evidence

Pathways stay grounded when assignments, bytes, and follow-up reviews sit next to completion data and capability lift.

342

Active enrolments

128

Completed

37%

Avg completion

+1.4

Capability lift

Frontend fluency

Target: Engineer

82%

Done · StateDone · TestingDone · UI polishNext · AI review

Next review

State review + accessibility follow-up

AI delivery harness

Target: Senior

74%

Done · InstructionsDone · ValidationNext · BoundariesNext · Observability

Next review

Prompt injection and trace review

Production readiness

Target: Engineer

66%

Done · LoggingDone · AlertingNext · RunbooksNext · Recovery

Next review

Incident-response byte

Architecture judgment

Target: Architect

48%

Done · Trade-offsNext · NFRsNext · Team upliftNext · AI policy

Next review

ADR review panel

Practice artifacts

Concrete examples inside each path

Take-home example

Real repo assignment

Frontend

Frontend Engineer

Tip Calculator

A candidate sees a short brief, a realistic UI target, and a repo they can clone, run, and submit as a reviewable change.

UI polishState handlingReviewable diff

TypeScript byte

Pirate Name

Short string challenge with readable tests and reviewer-visible proof.

Test result

9/10

tests passed

Visible proof9 of 10 checks passing

TDDEdge casesReadable tests

Skills heatmap

Code quality82

Testing77

Architecture63

Observability59

AI governance46

Capability lift

+1.4

average score increase across completed paths

18%

higher retention for engineers on active paths

Assignment-driven pathways work best when each module ends with a visible artifact, not a checkbox. That makes the next coaching move obvious to both the engineer and the manager.

Coaching outputs

Turn signals into reports, heatmaps, and clear next steps

Scores help you spot patterns. Narrative feedback tells managers what to do next, where to coach, and how to support growth across the team.

Harness maturity

See whether teams can set validators, approval defaults, and stop conditions before a delivery sprint starts.

Boundary discipline

Track prompt injection handling, retrieval filtering, and MCP trust decisions without hiding risk in the prompt.

Evidence quality

Measure whether claims are backed by screenshots, traces, diffs, and exact commands a reviewer can replay.

Narrative feedback

Feedback that builds capability

Team signal

8.1

Strong context engineering and consistent validation loops. The next coaching move is to push harder on delegation boundaries, MCP trust review, and what counts as enough evidence before a manager or reviewer signs off.

Team heatmap

Context sufficiency84

Validation discipline79

Evidence-based review76

Tool integration judgment73

Data boundary awareness71

Promotion reviews

Role-ready evidence

Manager 1:1s

Clear coaching hooks

Team planning

Heatmaps by capability

Deploy under your brand

Run Learn inside your domain, identity stack, and workflow

Internal development programs fail when the platform feels bolted on. Learn can run under your domain, brand, and sign-in flow so managers and engineers see one coherent system, not another vendor portal.

Custom domain and branded UI

Run Learn at your own domain with your logo, color system, and interface treatment so the experience looks internal from the first click.

SSO and access controls

Plug Learn into your identity stack so managers and engineers sign in through the systems they already use.

Same workflows, your operating model

Keep the same readiness, coaching, and pathway workflows while presenting them inside your brand and rollout process.

See Whitelabel Platform Schedule Demo

Brand settings

Primary color

Domain

learn.codesubmit.io

Identity

OktaSSO pending

CodeSubmit Learn

learn.codesubmit.io

Internal rollout

Manager visibility

Audit-ready coaching surface

Brand fit

100%

Custom domain

Keep invites, dashboards, and coaching reports inside your own domain structure.

Access control

Route sign-in through your identity layer instead of introducing one more vendor login.

One system for AI readiness, growth, and rollout

Baseline, coach, upskill, and deploy under your brand

Learn gives engineering leaders one place to understand team capability, govern real AI usage, coach with evidence, and deliver the whole system in a branded internal experience.

Schedule Demo See Upskill Use Case See Whitelabel Platform

Measure real AI engineering habits with evidence, not guesswork

See where AI judgment is strong, weak, and coachable

Build role pathways around the work your team actually ships

Turn signals into reports, heatmaps, and next steps

Run Learn inside your brand, domain, and access model

See where AI judgment is strong, weak, and coachable

Build role pathways around the work your team actually ships

Turn signals into reports, heatmaps, and clear next steps

Run Learn inside your domain, identity stack, and workflow

Baseline, coach, upskill, and deploy under your brand

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