Cutting-Edge AI, Not Bleeding-Edge
TRAINING
You ask for a cake, the team delivers a pie.
The work comes back from QA, and the team starts over. AI just makes that cycle faster. We train the people on each side of the handoff to break that cycle.
Engineering-Led AI
Live training for software engineers, including AI on legacy systems.
AI Product Academy
One-day training for product managers and product owners, from discovery to decision.
Agent Skills
A hands-on workshop for developers who work with AI regularly: write your first skill, then improve an existing one.
Engineering-Led AI
Engineering-led AI, not AI-led Engineering.
PAAD
Spec-driven development
Context management
Legacy & technical debt
Refactoring
Your developers are already using AI. At first it feels like magic. Then over time, the work gets more difficult: simple features take more time to implement, features do what was asked but not what was meant. Nothing went wrong all at once. Quality slipped, one decision at a time.
The tool is rarely the problem. What the AI needs to do good work isn't in the code. It lives in your engineers' heads.
When the AI suggests caching options, the junior engineer chooses; the experienced engineer asks why a cache is needed in the first place.
We train engineers to lead AI, not to be led by it. Then we work alongside them on your own systems, when the real questions surface.
What your engineers learn
Keep problems out
Lead the AI with engineering expertise, instead of letting it make architectural, security, or business decisions it can't understand
Replace vibe coding with spec-driven development: requirements and design reviewed before code is written
Keep what gets built aligned with what was specified, at every step, with PAAD
Some teams are already disciplined: bugs rarely make it into their code. That's the first half. The second is going after the debt that's already there, aggressively, with AI doing the work under your engineers' lead.
Pay down the debt that's already there
Refactor legacy code safely, with tests in place before anything changes
Find and remove dead code nobody dared to touch
Untangle architecture that grew without a plan
Strengthen existing systems: more test coverage, documented intent
AI Product Academy
Business-led AI, not AI-led Business.
Product discovery
Spec-driven development
Experiment design
PRDs & acceptance criteria
Refactoring
Decision records
When AI makes features fast to build, the risk shifts from "can we build it?" to "should we build it?"
We train your Product Managers and Product Owners to make that call. AI helps them plan. The decision and the accountability stay human.
What your product teams learn
Find where AI genuinely improves the business, and where ordinary automation is the better answer
Turn vague AI ideas into testable initiatives, with evidence, measurable outcomes, and realistic costs
Give engineering specs it can build right: PRDs, stories, and acceptance criteria where AI doesn't invent decisions
Keep accountability human: what AI may recommend, what it may do, and what needs approval
Scale what demonstrably works, not what demos well
Agent Skills
Agent Skills
Quality checks
PRD generator
Your teams use AI every day, each in their own way. Agent Skills turn the way your team works into reusable instructions any AI agent can follow, with consistent, verifiable results.
Your teams write and run a first skill, then learn what makes a good one by improving a PRD generator adapted to their needs: from a weak instruction to a skill that challenges weak requirements and keeps every decision in the final document.
They leave with a tool they'll actually use, and the basics to build the next ones.
What your teams learn
How to write, run, and validate skills
Check that results are complete and match the request
Turn a weak instruction into a skill that pushes back, records decisions, and keeps them
INTEGRATION
AI in Your Existing Systems
AI only where it earns its place, built into the architecture you already have, without rebuilding what works.
Perl
Python
Vector databases
LLM APIs
Embeddings
What we deliver
Semantic search with vector databases
LLM integration for content generation
Conversational AI for customer support
Integration into existing architectures, including Perl
Resilience & Independence
What happens when your AI provider goes down or quietly gets worse?
AI gateway
Golden datasets
Automatic failover
LLM evaluation
If your products depend on a single provider, outages, price increases, and quality drops become yours. You should test your backups before you need them.
What we deliver
An internal gateway your products point at, with automatic failover between providers
Evaluation that proves a replacement works before you rely on it
A safe response when models deteriorate, providers fail, costs rise, credentials are compromised, or regulations change
Lean in-house replacements for costly AI tools you barely use, with cost and reliability back under your control
SECURITY
On security, we'd rather be honest than impressive.
OWASP Top 10 for LLM Applications
Prompt injection
Least privilege
Guardrails
AI can't reliably tell your data from instructions. That's how these systems work, not a bug waiting for a patch, and OWASP itself says it's unclear whether any fool-proof prevention exists.
Anyone who says they've solved it is selling you something you don't want to buy.
So we design for the day it gets fooled. AI gets only the access it needs, every input is treated as untrusted, and safeguards sit between the model and anything that matters. Before AI faces your customers, we help you decide what's safe to expose.
And when the safest design is to keep AI out, we'll tell you.
HOW WE WORK
We're disciplined about AI so you don't have to gamble on it.
"Tools change weekly. Good process lasts."
AI licenses are rarely what drives your costs. Your team's time, rework, and years of maintenance are.
AI can bring those down, or push them up. Without a team leading it, it writes code only an AI can follow.
PROOF
We practice what we teach.
Before teaching anyone, we spent countless hours working with AI: testing what it does well, where it fails, when not to use it, and how to keep it under control. What we teach comes from that work.
PAAD
Open source
Along the way, we built our own tools. PAAD is open source, so you can see the approach for yourself.
See PAAD