A practical breakdown of what AI agents, AI assistants and AI learning each actually require from your data, and why treating them the same is where most AI projects lose months.
Read the guideWalk through every line of the contract written from your brief, and what happens if one is wrong.
GUIDEA practical look at feature leakage, and how to test for it before your model does.
GUIDEHow to design agent tools that inherit the caller's access rights instead of a shared service account.
GUIDEA short decision framework for when relationships matter more than similarity search.
GUIDEWhy data incidents deserve the same discipline as application incidents, and how to bound autonomy safely.
GUIDEA checklist for deciding how much of the stack to hand off, and when to keep it in-house.
Most tools in this space solve one piece well. Here's how KESDONE's approach compares on the things that matter for shipping a data product that a real consumer can rely on.
| KESDONE | Warehouse-native semantic layers | Generic data catalogs | |
|---|---|---|---|
| Output shaped for the consumer (dashboard, API, model, assistant) | Built in | Reporting-focused | Not applicable |
| Covers collaboration tools (tickets, wikis, chat) | Yes | No, warehouse only | Varies |
| Proof attached (provenance, independent double checks, pixel-level source) | Yes | Partial | Lineage only |
| Operation after deploy: diagnosis and fixes | Built in | No | No |
| Regional and in-VPC deployment options | Available anywhere | Platform-dependent | Varies |
Based on public documentation as of our last review. Categories evolve quickly, tell us if something's out of date.
The AI-augmented data engineering platform. You give it a business requirement or a manifest; it designs, builds, validates and deploys the data product a specific consumer needs (a dashboard, an application or API, a machine learning model, an AI assistant or agent, or an audit), then operates it in production.
No. If you have a cloud account, KESDONE generates and deploys the infrastructure there. If you don't, KESDONE provisions and runs everything itself: compute, storage, orchestration, monitoring and secrets. You never have to set up a data platform to get a data product.
No. It takes the part of their work that gets redone on every request: finding the sources, agreeing on what each field means, writing the same patterns again, testing, and repairing incidents. They keep the two decisions that matter, approving the plan and approving before production, and they can write or edit the manifest directly, review the generated infrastructure and read every validation report.
KESDONE keeps watching the product. It detects schema changes, freshness misses and drift, diagnoses against the manifest and lineage it built, and prepares a fix as a new validated version. You set the autonomy level: diagnose only, prepare the fix, or apply it automatically for known low-risk incidents.