Enterprise-Grade AI Solutions
Our team brings 20+ years of combined experience from leading tech companies, building AI solutions backed by production-grade infrastructure and scalable data platforms — all engineered to deliver measurable business outcomes.
Senior Leadership
Engineering Management
Enterprise Scale
Proven Track Record
What We Do
AI Solutions
Custom AI agents, NLP, computer vision, and intelligent automation
Data Engineering
Petabyte-scale data pipelines, real-time streaming, and data quality platforms
ML Infrastructure
Production-ready ML systems with MLOps, model monitoring, and automated retraining
Cloud Architecture
Multi-cloud solutions on AWS, GCP, and Azure with cost optimization
Who we are
Yeda AI is a small, senior software and data studio. We split our time between two things: building AI and data systems for clients, and shipping our own products — YedaChat, our embeddable chat widget; YCAudit, an AI code auditor; and the YedaAgents suite of governed, named specialist agents. Working on both sides keeps us honest. The products are shaped by the constraints of real client work, and the client work benefits from tools we run on ourselves every day.
We come at problems as engineers first. Most AI projects do not fail at the model — they fail in the long gap between a demo that impresses and a system a business can actually depend on. That gap is where we do our best work: the data pipelines, the evaluation harnesses, the cost controls, and the monitoring that decide whether an AI feature survives contact with real users and real data.
We also believe in showing our work. We open-source parts of our stack — the YedaFlow pipeline substrate and our Foundry agents among them — and we write openly about what our own tools find, including on our own code. If you want to see how we think, the blog is a good place to start: most posts walk through a real audit or build, findings and all.
Why Choose Yeda AI?
Battle-tested at scale (petabyte-level data)
Deep FAANG engineering experience
End-to-end solution delivery
Cost optimization focus
How we work
In plain terms: we treat an AI system like software that has to keep working. Most projects stall in the gap between a promising prototype and something a business can actually trust, and closing that gap is the whole job. Here is how we approach it.
Step 1
Start from the outcome
We scope the problem and the data before the model. The goal is a result you need, not a technology demo.
Step 2
Build on solid data
Pipelines and quality checks come first, because an AI system is only as trustworthy as the data underneath it.
Step 3
Ship with guardrails
Evaluation, monitoring, and cost controls are wired in from day one, so the system reports on itself instead of hiding.
Step 4
Keep it correct
AI drifts as the business changes. We stay involved to keep it accurate, audited, and worth trusting over time.