A practical path from AI questions to engineering work.
Explore the firm's research, public workbenches, engineering guides, and tools. Each resource describes the work it supports, with practical exercises and engineering context.
Choose your next step
Compare AI operating cost
Token economics and an accepted-outcome scorecard
Define accepted work, account for retries and review time, and interpret cache-token shares. Includes a reusable CSV with clearly labeled synthetic arithmetic.
Explore the lab
Applied AI research at LockedIn Labs
Explore the firm's experiments, evaluation methods, model engineering, and public tools, with the evidence behind each kind of work.
Choose a model approach
LLM evaluation, fine-tuning and model engineering
Start from the task and baseline, compare retrieval and model adaptation, and inspect the evaluation and release evidence required to justify a change.
Prepare for enterprise review
AI production readiness and diligence
See the architecture, access, evaluation, operating and handover artifacts that accountable owners review before production authority expands.
Inspect a repeatable test
How MOEModels handles missing evaluation evidence
An engineering note with synthetic policy fixtures, pass/fail/inconclusive decisions, recorded source provenance, and a browser exercise for repeating the checks.
Scope an implementation
Enterprise AI consulting
Define the workflow, delivery responsibilities, integration boundary, and acceptance evidence with LockedIn Labs.
Accept production work
AI production acceptance framework
Use eight review dimensions and a downloadable worksheet to agree on what an implementation must demonstrate.
Design the lifecycle
AI SDLC
LockedIn Labs' publication for reference architecture, agentic engineering, lifecycle guidance, and accountable delivery.
Design an agent's context
Context Harness
A LockedIn Labs workbench for planning source authority, freshness, tool boundaries, evaluation, and handoffs. Export a reusable context brief for your implementation.
Inspect agent activity
Agent Console
MIT-licensed local observability for Claude Code and Codex: sessions, models, token usage, and list-price cost estimates. Explore the tool's operating scope and discuss it with LockedIn Labs.
Measure a model deployment
MOEModels
Experimental, Apache-2.0-licensed developer tooling for planning mixture-of-experts deployments, measuring endpoints, and packaging verifiable run evidence. Try its policy engine with synthetic evidence in the local evaluation bench.
Build capability
AI-native training
The firm's learning platform connects foundations, role pathways, individual practice, and team development.
Explore skills evidence
LockedIn FDE Benchmark
Learning paths and a public evidence framework from a LockedIn Labs initiative. Read each assessment's method and evidence status alongside its result.
Understand assurance
ControlFrame
The compliance engineering platform's public material explains evidence collection, traceability, and assessor workflows.
Evaluate local models
LeanLogix learning guides
Model engineering and evaluation material from the LockedIn Labs model foundry, with measured findings and their scope.
Follow the author
Sam M. Sweilem
The official professional profile and additional writing on enterprise AI, operating leadership, and modernization.
Original guides by Sam M. Sweilem
- An AI delivery-model worksheet: assign the work before you staff it.
Includes ai delivery-model worksheet.
- Agentic workflows in production need an operating contract for every action.
Includes agentic workflow operating-contract template.