Before you go

How much did you lose
this week?

The average service business misses 62% of inbound leads. At $3,500 per deal, that's real money leaving every single day.

About Iron Logic

We build AI that
works while you sleep.

3–5
days to go live
22s
avg AI reply time
24/7
never off, never late
100%
messages answered
30 seconds · No signup · Real example
Live Preview
See the AI handle a real lead — in seconds.
Customer · WhatsApp
Hi, how much does a haircut cost and are you open Saturday?
AI Assistant · Iron Logic
Hey! 👋 Haircut is $25, shape-up is $15, full cut + beard $35. We're open Saturday 9am–6pm — want me to book you a slot?
Yes please — 10am works
Locked in! ✅ Saturday 10am — you'll get a reminder the day before. See you then! ✂️
Replied in 47 seconds · booked automatically
Under the Hood

How the AI actually
works.

Customer Messages
WhatsApp or Instagram DM
0s
AI Reads Context
Knows your prices, hours, FAQs
~2s
Understands Intent
Booking? Price? Question?
~5s
Sends Reply
Human-sounding, on-brand
<47s avg
100%messages answered
24/7never goes offline
<60saverage response
0extra staff needed
01 · Intake
Message Webhook
Latency
<200ms
WhatsApp Business APIn8n
02 · Retrieval
RAG Pipeline
Context match
88%
Vector searchBusiness KB
03 · Classify
Intent Engine
Accuracy
94%
BookingPriceFAQ
04 · Generate
LLM Response
Brand tone fit
96%
Claude / GPT-4Custom prompt
Phase 2 — Behavioral Scoring

After 30 days of live data, the system runs back-testing on conversation sequences to identify which message patterns correlate with completed bookings. High-engagement threads are weighted more heavily in future routing — effectively learning your customers' intent over time.

Raw conversations
Sequence labelling
Conversion model
Priority routing
Try the ML Playground → Interactive linear regression, gradient descent & Bayesian classification
Mathematical Foundations
Linear Algebra
Messages are embedded as vectors in high-dimensional space. Cosine similarity finds semantically related queries. SVD reduces dimensionality in RAG retrieval.
sim(A,B) = (A·B) / (‖A‖ · ‖B‖)
Vector embeddingsSVDCosine similarity
Calculus — Gradient Descent
Prompt optimization uses gradient-based techniques to minimize loss on classification tasks. The chain rule propagates error signals through the LLM fine-tuning pipeline.
θ ← θ − α · ∇L(θ)
BackpropagationLearning rateLoss minimization
Probability — Bayesian Routing
Intent classification returns a probability distribution over classes (booking, price, FAQ, complaint). The highest posterior probability determines routing — Bayes' theorem updates priors from conversation history.
P(y|x) = P(x|y)·P(y) / P(x)
Softmax outputPrior updatingConfidence thresholds
Statistics — OLS Regression
Lead-to-booking conversion rates are modeled as a linear function of response time. OLS fits the line minimizing squared residuals — used to project per-business ROI in the audit.
β = (XᵀX)⁻¹ · Xᵀy
R² goodness-of-fitResidual analysisConfidence intervals
What We Build

AI systems that
do the follow-up for you.

WhatsApp AI
Instant replies · 24/7 · Lead capture
Booking Systems
Calendar sync · Auto-confirm · Reminders
Lead Qualification
Filter · Score · Route to close
Instagram DMs
Auto-reply · Capture · Convert
The Founder

Damarley Powell.

I started Iron Logic after watching good businesses lose customers for one avoidable reason — nobody answered fast enough. A call missed at 7pm. A DM left on read. A quote sent a day too late. By the time they replied, the deal was already gone to whoever answered first.

So I built the fix: AI systems that answer every lead in seconds, qualify them, and book them automatically — 24/7, no extra staff, no missed money. I run it end to end myself, from the first call to the live system. When you work with Iron Logic, you work with the person who actually builds it.

Jamaica-based Self-taught Solo-founded Full-stack builder
What I build
WhatsApp AI systems · Voice agents (inbound + outbound) · Booking & appointment automation · Email follow-up · Full website builds + payments
Damarley Powell — Founder of Iron Logic
Why Iron Logic

Built different.
Deployed faster.

01 · Setup
Done for you.
Top to bottom.
No tech skills 30-min call We build it
02 · Speed
Live in 3–5 days.
Not 3–5 weeks.
Day 1: call Day 3: built Day 5: live
03 · Reach
Caribbean roots.
Global reach.
WhatsApp-first USD + JMD Jamaica → US
Our Approach

We install the system.
You run the business.

100%
done-for-you — you don't touch any tech
0
ongoing management needed from you
30min
your total time to go live
Credentials

Building the knowledge
to back the work.

A staged roadmap from NLP to computer vision to physical AI — toward full-stack robotics AI engineering, built around live IronLogic client work. Each credential maps to a real capability.

Phase 1 · Jun–Aug 2026
Starting June
Harvard CS50 +
Codecademy NLP
Python C / CS Fundamentals NLP SQL
Foundations — learning to code through NLP, applied straight to IronLogic chatbots and voice agents, with Harvard CS50 and a Codecademy NLP Professional cert. First Arduino + Raspberry Pi builds run alongside.
Phase 2 · Sep–Nov 2026
Pending
NVIDIA Isaac +
DeepLearning.AI
Deep Learning ROS 2 Isaac Sim Computer Vision
The full technical stack — Andrew Ng's ML Specialization, ROS 2 (the robot OS used by NASA and Boston Dynamics), and NVIDIA Isaac for photorealistic robot simulation.
Phase 3 · Dec 2026–Feb 2027
Pending
Google Professional
ML Engineer + AWS
AWS ML Specialty SageMaker MLOps Model Deployment
The credentials employers recognize globally — Google's Professional ML Engineer (the hardest ML cert, ~25% salary premium) stacked with AWS Cloud Practitioner, AWS ML Specialty, and NVIDIA DLI robotics certs.
Certifications are scheduled around live client work — applied systems ship first, credentials follow.
Free · No pressure · 30 minutes

Ready to stop losing
customers to slow replies?

Response within 24 hours · No commitment · Jamaica, US & beyond