IbuildAIsystemsthatworkwhileyousleep.
I work with founders, operators, and growth teams who are past the idea stage and ready to ship.
Get the exact n8n workflow that books 284 meetings/month
“Isaac's lead gen system booked 284 meetings in our first month, and we didn't even add a single SDR.”
- 12.4Kleads / mo
- 61%booking rate
- 284meetings booked
Live systems. Built, deployed, running.
A selection of production systems I've designed and shipped. Tap any to open the case study.
From idea to running system — in four moves.
Every system I ship goes through the same four-stage pipeline. No mystery, no theatrics.
IDEA
Capture the messy real-world problem before optimizing anything.
- Frame the operator's job to be done
- Sketch the smallest end-to-end path
- Pick the killer constraint that defines done
ARCHITECTURE
Draft the map. Choose primitives that won't betray you at scale.
- Pick orchestration engine (n8n / Make / custom)
- Decide datastore + memory + queues
- Define observability + retry budgets
AUTOMATION
Wire the nodes. Push events. Watch the system come alive.
- Build node graph from triggers → outputs
- Bake in idempotency + replay safety
- Ship the smallest live cut, then iterate
SCALE
Tune for cost, latency, and resilience. Watch the dashboard flatten.
- Profile bottlenecks, parallelize hot paths
- Cache the expensive thinking
- Hand off to the operator with a runbook
Build logs, teardowns, and unedited live builds.
The same lab, on tape. New drops weekly.
Building a 12-agent mesh in n8n + LangGraph
Building a 12-agent mesh in n8n + LangGraph
From a blank canvas to a running agent mesh that resolves real support tickets. Live, no cuts.
n8n vs. Make: which one survives at 2M events / day?
I ran both engines at scale for two weeks. Here's where each one breaks, and why I still use both.
Stripe metered billing in Bubble in 14 minutes
The pattern I use on every SaaS build. Includes the workflow JSON in the description.
Shipping a voice receptionist in one weekend
Weekend timeline of building, deploying, and calling my own receptionist agent.
An AI marketplace that prices itself dynamically
Pricing as a product feature. Embeddings + simulated demand to find equilibrium.
Agent memory: why your bot forgets and how to fix it
Per-agent shards, summarization passes, and the eviction policy that finally worked.
Cold pipeline 5x in 30 days — full autopilot
The exact graph, scoring rules, and Slack handoff that drove the lift.
My 2026 no-code stack (the one I actually use)
What stayed, what I dropped, and what I'd never use in production.
Currently building. In the open.
Half-formed prototypes that earn their way into the work above.
Self-healing workflows
Workflows that detect their own failure modes, ask an LLM to diagnose, and patch themselves before paging me.
Get notified when this ships
Agent simulator
A sandbox that replays real user traces against new agent prompts before deploy. Treats prompts like code.
Get notified when this ships
Voice → workflow
Speak the workflow you want. The system drafts a runnable n8n graph and asks for confirmation.
Get notified when this ships
Marketplace pricing oracle
An always-on agent that watches my AI marketplace, simulates demand, and proposes price changes hourly.
Get notified when this ships
Let's build something serious.
Tell me what you'd like to operationalize. I'll respond within 24 hours.