# AI-native training by LockedIn Labs

## Build AI capability your teams can use

LockedIn Labs brings its production engineering approach into practical AI training. Develop employees' AI fluency, engineering teams' technical depth and delivery leads' ability to work across a client environment. The learning connects a useful workflow to code, failure tests and work another person can receive.

The company builds AI systems for regulated environments. That discipline shapes the training: explicit access boundaries, careful data handling, evaluation, recovery and accountable ownership. Public labs use synthetic teaching cases; they are not client case studies or claims of a participant's production experience.

Explore: https://training.lockedinlabs.ai/training/ai-native
Plan a cohort: https://training.lockedinlabs.ai/enterprise#contact

## Choose the starting depth

| Learning need | Practice | Work to retain |
| --- | --- | --- |
| AI fluency at work — employees, managers and leaders | Decompose a workflow, choose appropriate AI assistance, check a result and retain the human decision. | Original workflow, changed design, observed limitation and next experiment. |
| AI systems engineering — developers and engineering teams | Build with agents, retrieval and integrations; test data access, evaluation and failure behavior. | Runnable code, exact commands/version, original and changed results, and an explanation of the boundary. |
| FDE and pod delivery — forward-deployed engineers and delivery leads | Practise discovery, integration, operations and transfer between builder and receiving team. | Separate individual contributions, receiving actions, assistance, unresolved dependencies and named responsibilities. |

These describe learning pathways, not new credentials or a promise of independent client placement. Existing enrollment, review and qualification requirements apply to the relevant training.

## Inspect the work before buying

Start with the no-code employee lesson: https://training.lockedinlabs.ai/training/labs/ai-at-work. Review a fictional draft against two sources, retain your first choices, then try the same draft against changed evidence. Save the local practice record and ask a colleague to locate a changed decision. The optional customer-message challenge separates a legitimate request from instructions that pretend to grant company approval. No model call or message delivery is involved.

- **Access and data:** https://training.lockedinlabs.ai/training/guides/identity#3a-run-the-source-to-context-comparison — compare actual selected source text and SQL, including a removed grant.
- **Reliable integration:** https://training.lockedinlabs.ai/training/guides/webhook-receiving — distinguish message verification from applying the same event twice.
- **Recovery:** https://training.lockedinlabs.ai/training/guides/systems — inspect restored records and the receiving runbook.
- **Team ownership:** https://training.lockedinlabs.ai/training/labs/pod-delivery — swap builder and receiver, keep separate accounts and attempt a concrete next action.

Free materials can be inspected without enrollment. The DEPLOY Method and evidence standard are at https://training.lockedinlabs.ai/framework. Academy access and any staffed delivery arrangements are agreed separately.

For an employee group, use the 60-minute facilitator plan at https://training.lockedinlabs.ai/templates/ai-native-employee-review-clinic.md and give each person their own worksheet at https://training.lockedinlabs.ai/templates/ai-native-employee-review-worksheet.md. The plan includes role swaps and a separate later task; a download does not book delivery or establish learner results.

## Bring this brief to a planning conversation

Use sanitized examples. Do not include client secrets, patient records, personal credentials or confidential operating data.

- Organization and accountable training owner:
- Learner roles, starting experience and accessibility/setup needs:
- One workflow or engineering responsibility the group needs to improve:
- Current ability: what can each participant do before the training?
- Selected pathway and exact lesson/lab version:
- Approved practice environment and data boundary:
- Individual task, difficult changed condition and evidence to retain:
- Receiving team, concrete receiving action and limits on author assistance:
- Identity/data, application, security and operations owners needed for review:
- Proposed learning time, support capacity and follow-up opportunity:
- How we will observe transfer into a new task after the learning:

Agree the relevant baseline and changed task before describing improvement. Attendance, a lab result or a self-review alone does not establish field readiness. Real-system access and release authority remain with the receiving organization.

## Confirm the delivery scope together

The proposal should name the participants and topics, faculty and support responsibilities, learning environment, enrollment/access, review arrangements, schedule, fees and cancellation terms. Instructor preparation uses a reproducible lab, an observed teaching attempt and scoped co-teaching before an accountable delivery decision. Availability is confirmed during planning; this brief does not announce dates, assign faculty or activate a cohort.

Discuss the plan: https://training.lockedinlabs.ai/enterprise#contact