A cleansed layer, a metrics layer, a governed API, a feature store, a retrieval corpus, a set of auditable facts: not the same data reshaped, but different products, with different guarantees. You name what will consume it. KESDONE builds what that consumer needs, and shows you the first call before anything ships.
A data product is not a table. It is data in the shape the consumer reads, with the guarantees the consumer needs, delivered where the consumer runs.
"Our Kafka events land raw in S3. Every team re-parses them their own way, and every team gets a slightly different answer."
The transformation layer everything else stands on: ingestion, then cleansing. Deduplicate on a business key with a late-arrival window, cast every field to a declared type and reject rather than coerce, normalise units and currencies to one reference, split a composite field into its parts, rename source columns to the agreed business name, resolve time zones to UTC with the original offset kept, and quarantine what fails instead of dropping it silently. The rules are declared once, versioned, and applied at every run.
Give every downstream team the same clean table, with one definition per field, instead of five teams parsing the same raw events five ways.
"Our dashboards disagree. Finance, Sales and the board each have their own revenue number."
A governed metrics layer: one definition per metric, agreed with the owner of the domain and written down with the reason behind it. Reconciled across systems, computed once, served to every tool that asks. Freshness promised and monitored, lineage from the number back to the source row.
Answer "what was net revenue in March?" with a number nobody argues about, and show where it comes from.
"Our product team needs customer and order data, and I do not want them querying the warehouse directly."
A canonical model of your domain (customer, order, contract) with one definition per field, exposed as a versioned API and, where useful, as an MCP server. Permissions per caller, validation, rate limits, an audit trail, and deprecation rules so consumers do not break when the model evolves.
Let any application, service or assistant read the same customer, without re-implementing what a customer is.
"We retrain a churn model every month. It needs features as they looked at each scoring date."
A feature store: features per entity with their definition, window and lineage. Point-in-time correctness, so every feature is computed with only what was known at the moment the prediction would have been made. Training sets with labels and time-based splits, the same features served online for inference, and drift watched per feature.
Train, evaluate and serve a model whose offline score still means something in production.
"We want a support assistant that answers from our contracts and resolved tickets, with sources."
For an assistant: a retrieval corpus cut along the structure of the documents, a hybrid index for meaning and exact terms, permissions applied at query time, and an evaluation set re-run at every refresh. For an agent: a canonical view of the entity it acts on, typed tools with a scope and a rate limit, and every call carrying the identity of the person who asked.
Answer "what are the termination conditions of Acme's contract?" with the right clause, the right page, and only for someone allowed to see it.
"The auditors asked how we computed this figure last year, and we spent two weeks rebuilding the answer."
A set of certified facts with their full audit trail: which sources, which rules, which version, who approved it and when. Historised, so a figure published last year can be reproduced exactly as it was, even if the rules have changed since. Personal data minimised by design, retention applied automatically.
Answer an auditor in an afternoon instead of two weeks, and show the rule that was in force at the time.
You provide the requirement, the context and access to your data. KESDONE builds the data product behind it, whichever consumer it serves. How it does that, step by step, is on the Product page; what it costs, on Pricing.
A data product needs compute, storage, orchestration, secrets and monitoring. KESDONE generates all of it. If you don't have any of it, KESDONE provides it.
KESDONE generates the infrastructure as code from vetted modules, your team reviews it, and the product runs inside your cloud, GCP or Azure account.
No cloud account, no data team, nothing to set up. KESDONE provisions compute, storage, orchestration, monitoring and secrets, builds the product on top, and runs it for you.
Need data residency or in-VPC inference with open-weight models? Both are configuration options, in either mode, for customers who require them.