AI Products

Custom AI Products,
Built for Your Business.

NDN Analytics designs, builds, and deploys focused AI products on Google Cloud — from demand forecasting and patient risk scoring to last-mile routing and SaaS churn prevention. Production AI without the consulting overhead.

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What you get when you work with us

Production model + deployment
A trained model deployed on Vertex AI or Cloud Run, with retraining pipelines, evaluation hooks, and a serverless inference endpoint your stack can call.
Working integration with your data
BigQuery / Firestore / Postgres connectors, ETL where needed, and a feature pipeline calibrated on your historical data — not a generic demo.
Monitoring and guardrails
Drift monitoring, prediction logging, fallback rules, and spend guardrails so the model behaves predictably in production, not just in training.
Direct founder-engineer access
No account-management layer. The engineer architecting your build is the one in the working sessions, fixing the bugs, and on the post-deploy support call.

Products in this practice

Who this is for

Retail
Healthcare
Logistics
SaaS / B2B Software
Real Estate
Financial Services

Common questions

Do you build new AI products from scratch, or just deploy off-the-shelf models?

Both. Most engagements blend the two: we use foundation models (open-source LLMs, time-series transformers, vision backbones) as a base, then fine-tune or compose them around your data and your specific problem. Pure off-the-shelf rarely beats a focused fine-tune on your own historical data.

How long does a typical AI product build take?

A focused MVP build is usually 6–12 weeks from kickoff to a deployed endpoint your team can call in production. A larger multi-team rollout — with EHR/ERP integration, compliance work, and multiple training pipelines — is typically 3–6 months. We give you a realistic timeline in the discovery call.

What does an AI Readiness Assessment include?

A working session that produces a problem framing (what you are actually trying to predict or automate), a data audit (what signals you have vs. need), a reference architecture, a phased roadmap, and a realistic ROI projection. The output is something you can take to your exec team for a build/no-build decision.

Do you work with companies outside the US?

Yes. We work with companies globally. The build runs over async + scheduled working sessions; the deployment lives wherever your data needs it to (US, EU, or other GCP regions for residency requirements).

Ready to scope a build?

A 30-minute discovery call gets you a problem framing, a reference architecture sketch, and a realistic timeline. No commitment.

Book a Discovery Call →