_1751731246795-BygAaJJK.png){kind=logo}
For Startups
Playbooks, decision frameworks, and case studies written for startups.
How to Run a Shadow Deployment Before Your AI Feature Goes Live
Staging tests passed, but staging traffic looks nothing like production. Here's the shadow deployment playbook senior engineers use to validate an AI feature against real inputs before a single user sees an output.
Your AI Model Isn't the Product. Your Retraining Loop Is.
Most teams confuse deploying a model with building an AI product. The model you shipped is a depreciating asset — the retraining pipeline behind it is the only thing that compounds.
Event Sourcing for AI Products: Why Your Model Needs a Time Machine
Your CRUD database can tell you what your AI decided, but not why — because the world it saw at decision time is already gone. Event sourcing is the architecture that gives your model a time machine, and it's the prerequisite for any serious AI audit trail.
5 Mistakes We Made Shipping AI to a Live Pharmacy Marketplace
A field-level post-mortem on what breaks when AI substitution, routing, and recommendation features hit a real e-pharmacy catalog. Five specific mistakes, the symptoms you'll see in production, and how to recover without rolling everything back.
Ship a Drug Interaction Alert With a Local LLM in 7 Steps
A runnable tutorial for CTOs at e-pharmacy startups who need drug interaction alerts without sending patient data to OpenAI. Uses Mistral 7B locally, a versioned interaction dataset, and citation-grounded extraction.
How KarroFin Scaled AI Credit Scoring Without Killing Approval Rates
KarroFin's credit model wasn't broken. No alerts, no errors, no engineering fires. But approval rates were quietly compressing at scale — and the fix wasn't where the data science team was looking.
LangChain vs. LlamaIndex vs. Raw API: Pick One
Three days into a prototype, every LLM orchestration framework looks the same. Here's how to pick between LangChain, LlamaIndex, and a raw API wrapper based on where you want to own the complexity — not which one had the best quickstart.
Feature Stores Explained: Why Your ML Models Stale Out
Your credit risk model nailed backtesting but production accuracy keeps slipping. The culprit is rarely the model — it's a silent mismatch between how features are computed at training time and at inference. Here's what a feature store actually does about it.
How to Audit an AI Feature Before It Ships to Production
Your AI feature passed internal demos. That's not the same as being ready for real users. Here's the pre-ship audit playbook to either confirm your fear or clear the launch.
Your RAG Pipeline Isn't Failing. Your Chunking Strategy Is.
Most broken RAG pipelines aren't broken at the retrieval layer — they were broken at ingestion, when documents were split without respecting semantic boundaries. Here's why chunking is the silent failure mode no metric catches.
Questions to Ask Before Hiring an AI Development Partner for Healthcare
Every AI vendor claims healthcare experience. Here are 15 specific questions that separate teams who have actually shipped under HIPAA, HL7, and clinical scrutiny from those who built a wellness app and are overstating their credentials.
In-House AI Team vs. AI Development Partner: Pick One
You have 30 days to decide: hire two senior ML engineers or engage an AI development partner for your first core feature. Here's the decision framework that actually matters — and the one axis most founders get wrong.