Build capability that survives contact with the work.
LockedIn Labs implements AI systems and develops the practitioners who deliver them. Its training system builds capability across roles; forward-deployed engineering is the flagship: production-shaped missions, evidence gates, and a published qualification standard—not seat time or a multiple-choice finish. The assessment, authored curriculum, source-backed Labs, and learner workspace run now. Practitioner-led delivery and forward-deployed engagements activate only after staffing and signed terms.
A credential can prove scope.
Applied evidence makes an operating claim inspectable.
The split is not quality — plenty of courses and provider credentials are rigorous within their published scope. LockedIn adds a different burden: show the artifact, the rubric, the reviewer provenance, and the operating change the evidence can support.
Knowledge and applied evidence solve different jobs. Serious training can require both.
Explore the programsCredibility you can inspect.
No borrowed logos, unnamed client claims, or invented numbers. Start with the running product, the public curriculum topology and briefs, and the boundaries stated on the page.
A running platform, not a slide deck
The public assessment method, authored graph, Lab contracts, artifact-review controls, learner workspace, and verification surfaces run today. Full training and workbenches remain provisioned. Product claims can be inspected before a buyer signs anything.
Regulated constraints encoded in the work
Healthcare and executive tracks teach data boundaries, staged validation, named sign-off gates, rollback, and audit evidence. The public curriculum topology, briefs, and evidence contracts show the standard directly instead of leaning on an unnamed biography.
Model-portable. Provider-aware.
The curriculum teaches portable specifications and explicit comparison methods across model ecosystems while preserving the differences that matter: context handling, tool behavior, data boundaries, security, evaluation, cost, and product integration. Learners use approved access they supply; model runtimes are not bundled.
Claims separated by operating state
Running code, engagement method, and contract commitments are labeled separately. No pilot, completed cohort, signed expert, revenue, or learner outcome is implied where evidence does not exist.
Free to join.
Proof to advance.
Three levels of commitment, and an honest statement of what each one asks of you. Most people start free and step up when the work demands it — the cohort is designed to run the method at full strength after roster and delivery coverage are confirmed.
The community
FREE — NO CARDA real account and an honest look at the whole method. The door is open to anyone who intends to do the work.
The cohort
APPLICATION OPEN · TARGET STARTThe commitment. A fixed weekly cadence, rubric-based work review, and a credential path. Final start and live instruction are confirmed after signed delivery coverage; practitioner defense requires vetted roster coverage.
The enterprise program
SCOPED PER ORGANIZATIONA scoped forward-deployed engagement is designed around your teams, approved stack, and operating constraints. Delivery coverage, outputs, handover, and commercial terms activate only in a written agreement.
Requests are stored for manual review. No seat, response time, or paid enrollment is granted automatically; cohort capacity remains a target until delivery coverage is confirmed.
How live delivery is designed to run.
These formats activate only after instructor coverage, schedule, and—where relevant—venue are confirmed. No cohort has run yet. Each planned room has a specific job and evidence output.
The working session
Two people, one keyboard, an agent session open on the screen. A practitioner directs, then hands the keyboard back. Most proof-of-work starts as a draft built here.
The review floor
The work goes on the wall and gets argued with the people who own the system. A strong response names the blocker, shows the evidence, and ends with a concrete revision.
The lab
A three-day working-room format for a confirmed venue and vetted instructor team. The constraint is real, the stack is live, and the capstone follows the same published gate.
FMT-01 and FMT-02 are the planned weekly cadence after staffing is confirmed. FMT-03 also requires a confirmed venue and travel terms.
See the formats in the catalogFour rules the platform is built on.
Operating beats watching
Nobody becomes an operator in a weekend of videos. Live delivery is designed around a weekly working session, an artifact, and a review floor; that cadence activates only after staffing and schedule are confirmed. The platform is built for that shape, not for a binge.
Proof beats completion
A credential can prove its published scope. LockedIn adds evidence of applied operation: artifacts reviewed against a published rubric, with reviewer provenance and graph effects kept explicit.
Honesty beats hype
We say what doesn't work. No borrowed logos, no invented numbers, no guaranteed transformations. Nobody teaches or reviews here without clearing the published practitioner bar — and the roster is still recruiting, which the site says on every page it matters.
Community beats content
Content is abundant; accountable review is not. The learner workspace, Community HQ, and practitioner application surface run now. Scheduled sessions and practitioner review open only after staffing and signed terms are in place.
Curious to leader, in five levels.
Every level is a region of the skill graph, gated by verified proof-of-work — not seat time.
What people ask before they commit.
Q1Is this a certification course?
It is broader. A vendor certification can establish product knowledge within a published scope. AI-native training adds evidence of applied operation: the intended result is a system or practice inside real work, with artifacts showing what was evaluated. Official provider programs can be included as clearly labeled supplements.
Q2How is my training path personalized?
The assessment reads how you actually work: how you prompt, how you direct agents, and how you decompose a real recurring task from your week. It scores six capability dimensions against the platform's authored rubrics, places you on the L0–L4 framework, and assembles your path from the authored curriculum — each module it picks carries the evidence for why. A free account keeps the profile and the path.
Q3Who teaches the programs?
Teaching is reserved for vetted practitioners who currently build, deploy, or advise on AI systems in real organizations. Current practice is the entry requirement, and no one leads a room until roster coverage and written terms are in place.
Q4What does the free community include?
A real account, the learner workspace and skill graph, the playbook library, Community HQ, and The Signal. It is free to join with no card required. A cohort seat begins with a reviewed application; tuition and terms are confirmed in writing before payment.
Q5How do cohorts work?
You can apply now. If the fit is right, the program, tuition, controlling terms, staffing, and schedule are confirmed in writing before delivery begins. The planned cadence combines a briefing, working sessions, and artifact review, with advancement gated by proof-of-work rather than watch time. Cohort 07 is the planned first cohort and activates only after signed delivery coverage.
Q6Is the training tied to one model vendor?
No. The curriculum teaches portable specifications and comparison methods that can be used across approved model families. Learners use model access they or their organization supply; Claude, OpenAI GPT, Gemini, and other runtimes are not bundled. The point is judgment — knowing which available system fits the task, the evidence, and the policy.
Q7How do credentials verify?
An issued LockedIn Labs credential resolves to a public registry entry with the holder, level, and artifact trail that earned it. The public examples are labeled specimens. Issuance requires work evaluated against the published gate; any live defense requires vetted practitioner coverage.
Q8What is a forward-deployed AI-native engineer?
An engineer who embeds inside an enterprise organization, deploys agentic workflows against real constraints, designs the AI roadmap and the AI-native training plan around it, and transfers capability to the people who will own the system before leaving.
Momentum, on the record.
Completed entries carry dates. Future delivery remains explicitly labeled TARGET or PLANNED until its dependencies are confirmed.
Target dates are roadmap targets, not completed delivery commitments. Planned entries have no committed delivery date.