Making expertise accessible when it matters
SLAN is built around a simple belief: expertise is earned through research, practice, and mistakes.
Our goal is to make that expertise available at the moment of decision, replacing guesswork with grounded guidance.
Why we built it
Concepts are taught. Application is where people get stuck.
- Define concepts, vocabulary, and frameworks.
- Show examples and outcomes in ideal conditions.
Under real constraints, "I understand it" becomes "I don't know what to do next." The missing layer is structured judgment: assumptions, tradeoffs, and checks.
- Turns expert materials into steps you can execute.
- Makes assumptions and tradeoffs explicit and reviewable.
- Ships with governance boundaries to protect serious content.
You don’t have time to re-learn the theory. You need the shortest path to a defensible output.
You must act without perfect information, so assumptions and risks must be made explicit.
Stakeholders optimize for different things; tradeoffs need to be surfaced early.
People disagree on what ‘good’ looks like. Structure makes success criteria visible.
Principles
What we believe in
Boundaries
What we are not
- Not a generic internet-wide chatbot.
- Not a tool designed to replace instructors or experts.
- Not a shortcut for answer dumping.
- Not a source of ungrounded, generic advice.
- Not a decision-maker you can delegate to.
- Not a black box where you can't see the logic.
- Builds structured paths with checks and completion criteria.
- Grounds guidance in your materials and teaching intent.
- Makes assumptions and tradeoffs explicit so outputs are defensible.
- Keeps humans accountable: supports decisions, doesn't make them.
- Makes the logic visible in steps you can review.
- Supports governance for proprietary content and cohorts.
Team
Who's behind SLAN
Selena Tabbara
Ex-AWS Professional Services (London). Shipped production AI systems across forecasting, anomaly detection, and GenAI workflows with customer teams.
At AWS, I learned that most "best practice" advice sounds like common sense, yet teams still get stuck executing it. Workshops didn't fix that. Coaching them through the decision process a few times did. SLAN turns that coaching into repeatable, structured guidance.
Read the longer version
I built SLAN after seeing the same failure mode everywhere: people understand concepts in theory, then reality shows up: time pressure, incomplete data, unclear incentives... and then they freeze.
At AWS, I helped customers ship production-grade AI that delivered business outcomes, not just prototypes. But the most important lesson wasn't technical: telling teams to "identify the right use case" or "find the right data sources" rarely changed behavior, even when packaged as a one-day workshop.
What worked was guiding them through the process repeatedly until the steps became obvious and repeatable. SLAN is built around that idea: expertise becomes usable when it's structured into a path you can follow, not a recommendation you're supposed to magically execute.
- Structured reasoning paths: steps, checks, completion criteria
- Decision quality under real constraints (not idealized conditions)
- Governance + IP boundaries for proprietary content and cohorts
Want to see if SLAN fits your course or academy?
We'll scope it to your materials, your governance constraints, and your learners.