We've shipped pipelines at Uber Eats, built systems on $429M government contracts, and deployed LLM-powered applications from prototype to production. That's what we bring to your project.
Learn more about our capabilities and vision.
From strategy to deployment — we cover the full data and AI lifecycle.
Design and deploy production-grade LLM applications — from RAG pipelines and AI agents to fine-tuned models and voice AI — so your team ships AI features that actually hold up under real workloads.
OpenAI, Gemini, Deepgram, TensorFlow, PyTorch
Build reliable, scalable data infrastructure that your analytics and ML teams can actually trust — high-fidelity ETL/ELT pipelines, data quality frameworks, and warehouse architecture that perform at enterprise scale.
Apache Airflow, dbt, AWS Glue, Snowflake, BigQuery
Identify and close the security gaps that emerge when you move AI into production — from prompt injection defense and model access controls to data encryption and API hardening.
OAuth, JWT, AWS KMS, SIEM, Prompt Injection Defense
Full-stack development of AI-native applications: internal tools, customer-facing products, and API-first backends that integrate cleanly with your existing data infrastructure.
React.js, FastAPI, Flask, Docker, Kubernetes
Stand up the cloud infrastructure and operational tooling to deploy, monitor, and scale your AI systems — so models stay performant after launch, not just during the demo.
AWS SageMaker, Terraform, Prometheus, Datadog
We run tight, low-overhead engagements. Here's what working with us looks like.
We start by understanding your data environment, existing stack, and the actual problem — not the one in the brief. This keeps us from building the wrong thing well.
You receive a concrete technical proposal with architecture decisions explained, trade-offs documented, and a timeline grounded in realistic estimates.
We ship in increments, not big bangs. You see working deliverables early and often — so feedback happens when it's still cheap to act on it.
We document what we built and why. Your team should be able to own it after we leave. If they can't, we haven't finished the job.
