Product

Give KESDONE the context. It does the engineering.

One request, one week, one data product: churn features for a retention model. The same six steps apply to a metrics layer, a governed API or an auditable figure. Here is what Marion did, and what KESDONE did, step by step.

How it works

Seven moments, one request.

One request, one week, one data product: churn features for a retention model. The same seven moments apply to a metrics layer, a governed API or an auditable figure. Each window below is the studio itself, playing that exact chapter.

Monday 09:02

01 · Describe what you need

In your own words, or drop the specification you already wrote.

KESDONE reads what you write and the document you attach, flags what contradicts what, and asks only the questions a machine cannot settle: at what grain, over what window, what counts as churn, where the result is delivered. It also answers yours: what already exists on these sources, whether the product will be fresh before your Monday job.

Marion did
  • Wrote what her model needs, in her words
  • Attached the 11-page specification
  • Answered four questions, asked two of her own
KESDONE did
  • Read the specification, found 2 conflicts with the message
  • Named the product following the workspace convention
  • Asked only what it could not decide, then stopped
KESDONE Studio · chapter 1 of 7Describe what you need
Scroll here to play this chapter
Monday 09:11

02 · It reads your data, and you confirm

Every field carries its level of truth. Nothing is presented as a fact that is not one.

It connects to the sources you authorize, reads their schemas and your existing models, and proposes a business meaning and an owner for every field. Each one is labelled observed, inferred, confirmed or governed, so a deduction never passes for a fact. You correct a meaning in place, and it moves from inferred to confirmed.

Marion did
  • Corrected one meaning, confirmed two others
  • Accepted the exclusion of a field carrying personal data
KESDONE did
  • Read 4 sources, 61 tables, 38 existing models
  • Proposed a meaning and an owner per field
  • Flagged what it had only inferred, and a GDPR question
KESDONE Studio · chapter 2 of 7It reads your data, and you confirm
Scroll here to play this chapter
Monday 09:14

03 · The plan and the sample, before anything is built

Nothing gets built until you approve both.

It shows how it intends to proceed, step by step, and what the result will look like on a real sample. The estimate is on the right: sources read, tables built, features, checks, time. Approving this plan is the first of the two decisions that stay with you.

Marion did
  • Read the six steps and the sample of 200 customers
  • Approved the plan
KESDONE did
  • Proposed a plan and an estimate before consuming a credit
  • Produced a sample to look at, not a promise
KESDONE Studio · chapter 3 of 7The plan and the sample, before anything is built
Scroll here to play this chapter
Monday 09:16

04 · It engineers the product

Four folders: the infrastructure it provisions, the implementation, the tests, the manifest.

An isolated workspace opens for the job: machine, repository, read-only access to the sources, secrets in a vault. It provisions what it needs in your cloud, writes the pipelines, computes the features and runs the tests, on a sample rather than on all your data. The workspace is destroyed when the build ends.

Marion did
  • Nothing. She served two customers.
KESDONE did
  • Opened an isolated workspace, provisioned, built, tested
  • Ran on 5% of the data first
  • Destroyed the workspace, kept the code in her repositories
KESDONE Studio · chapter 4 of 7It engineers the product
Scroll here to play this chapter
Monday 09:28

05 · Prove it before it ships

Twenty-one checks derived from your brief, and a compliance report.

Every promise from the approved brief is checked and scored: one row per customer and per date, no information from after the scoring date, amounts reconciled to the cent, no field carrying personal data. Compliance and the full audit trail sit beside it. Approving before production is the second and last decision that stays with you.

Marion did
  • Read the report, 20 passed and 1 warning
  • Approved and deployed
KESDONE did
  • Checked 21 promises, scored each one
  • Wrote the processing register, kept 412 operations logged
  • Deployed v1.0.0 to her warehouse
KESDONE Studio · chapter 5 of 7Prove it before it ships
Scroll here to play this chapter
Day 12, 03:04

06 · It keeps it running, and asks before touching production

Six monitors armed. An autonomy level you set.

Freshness, source schema, feature drift, row volume, run cost and access rights are watched at every run. Twelve days later a source is renamed upstream: it finds the cause in three minutes, prepares the fix, verifies it on a sample, then waits for you, because you set autonomy at level 2. Incident closed in 29 minutes, and no wrong data served in between.

Marion did
  • Approved the fix
  • Never opened the pipeline
KESDONE did
  • Noticed the rename before anyone else
  • Found the cause, prepared and verified the fix
  • Deployed v1.0.1 and recorded the cause
KESDONE Studio · chapter 6 of 7It keeps it running, and asks before touching production
Scroll here to play this chapter
11:40

07 · Everything it learned stays with you, and can be shared

A context repository, versioned and reviewed like code.

Definitions, decisions with their reason, relationships and fixes are kept. They are versioned in a context repository, on your own branch, and shared to the main branch only after the owners of each domain have reviewed them. Your code repositories do not change: pipelines and fixes are versioned there, as always. This is the context, versioned separately, and it is what the next request reads.

Marion did
  • Chose what left her branch
  • Opened a context request, reviewed by two domain owners
KESDONE did
  • Kept 47 definitions, 14 decisions with their reason, 22 relationships
  • Merged to the shared branch after review
  • Estimated the next request at 4 minutes instead of 9
KESDONE Studio · chapter 7 of 7Everything it learned stays with you, and can be shared
Scroll here to play this chapter
Guarantees by design

Proof isn't a report you request. It ships with the product.

Trusted

Lineage, provenance and citations on every field, with a full audit log of what produced it.

Deterministic

Deterministic steps wherever possible. AI is used only where judgment is genuinely required.

Reliable

Critical numbers are computed twice by independent paths and must agree, plus continuous tests and monitoring.

Secure

Each session runs in its own ephemeral sandbox (isolated VM, browser, IDE), with IAM and encryption everywhere by default, not as an add-on.

Explainable

Every extracted value traces back to its exact source, down to the pixel on the page.

Private

Data minimization, regional deployment and DPAs, configured per customer and per product.

Security & trust

Your data is worked on in a sandbox, with your permissions, on the record.

Every data product session runs in its own sandboxed workspace: an isolated virtual machine with its own web browser and its own development environment, created for that session and destroyed after it. Nothing is shared between customers, nothing survives the session except the product and its audit log.

Permissions travel with the data

Every product keeps the permission model of its sources. Agent tools run with the calling user's own identity, never a shared service account.

Secrets stay in a vault

Credentials are injected into the sandbox at runtime and never written to code, configuration or logs. Network egress is limited to the sources the manifest declares.

Every action is logged

Who called what, with which identity, what came back, and what each role did during a build or a fix. The audit log ships with the product.

Model choice per product

Hosted frontier models under a data processing agreement, or open-weight models inside your VPC for sensitive data. Chosen in the manifest.

Data residency on request

Regional residency and in-VPC inference are configuration options in either deployment mode. KESDONE is deployed globally.

What we never do

Train models on your data. Share anything between customers. Reach a system the manifest didn't declare. Change production beyond the autonomy level you set.

Security questionnaire, DPA or a walkthrough of the sandbox model with your security team: mention "security review" in your requirement.

Sources we connect

Structured, semi-structured and unstructured. Many connectors.

SaaS & business
  • HubSpot
  • Shopify
  • Zendesk
  • Stripe
Ads & marketing
  • Google Ads
  • Meta Ads
  • TikTok Ads
  • LinkedIn Ads
Cloud & data platforms
  • Snowflake
  • Databricks
  • BigQuery
  • AWS / GCP / Azure
Databases
  • PostgreSQL / MySQL
  • MongoDB
  • Elasticsearch
  • Redis
Collaboration & docs
  • Slack / Teams
  • Confluence / SharePoint
  • Google Drive
Dev & ops
  • GitHub / GitLab
  • Jira / Linear
  • APIs, files, PDFs, logs
Get started

See a manifest become a running, proven product, on your own data.