Autonomous change management for data

Data Changes - Proven and Reliable.

VitiOps is an autonomous, intelligent layer for data reliability.
Built for high-risk data changes.
Delivers safe and auditable releases.

Impact analysis Approval gates Rollout Reconciliation Verification

Become a design partner See how it works

The problem

Every critical data change is a high-risk production event.

Pipeline changes trigger complex migrations, backfills and downstream failures. The result: slower releases, costly rework, and incorrect data reaching the people and systems that rely on it.

Impact is guesswork

Before approval, no one can see every report, contract and system a change will touch.

Coordination is manual

Backfills, dual writes and deprecations get run out of tickets, docs and Slack threads.

Recovery is improvised

When a rollout goes wrong, teams reconstruct what happened instead of replaying a plan.

Why now

Data changes have outpaced manual review.

Data now powers revenue, customer products, regulatory reporting and AI decisions — a wrong number is a business problem. As data systems grow, no team can manually trace every report, workflow and decision a change affects.

68%

of organizations deploy database changes weekly or faster

18.8%

deploy database changes every day

AI-assisted development is accelerating this further.

The VitiOps solution

Make critical data changes safe, governed, and verifiable.

01 / SEE WHAT COULD BE AFFECTED

Blast radius, before approval.

The reports, systems and teams a change may impact
Enforced contracts and executive dashboards downstream
Similar past changes, and the incidents they caused

02 / GENERATE THE ROLLOUT

A migration plan, not a checklist.

Backfill, dual-write and deprecation stages
Release gates routed through human approval
A rollback path defined before anything ships

03 / CONFIRM THE RESULT

Proof the rollout worked.

Parity and reconciliation checks after deployment
Evidence that downstream metrics stayed correct
When reality diverges, the agent produces the root cause

The architecture

Grounded in facts, reasoned with AI.

Layer 1 · Facts
Computed, not inferredLineage, contracts, checks and parity results, derived deterministically from the code, the runtime and the platform metadata.
Layer 2 · Reasoning
Agents over factsAgents reason over those facts to produce the impact assessment, the rollout plan and the post-deployment verdict.
Layer 3 · Approval
Human sign-offEvery plan is a reviewable artifact, approved by a human before anything touches production.

The agents never invent the facts.
They reason over them.

Observability tells you what is wrong. Lineage tells you what is connected. VitiOps uses those facts to manage the change itself — from impact analysis through rollout to verification.

Building trust

Autonomy, earned in stages.

Trust is the real adoption curve. Every action lives in one of three modes — and the bounds widen only as the product proves itself.

Analyze Plan Execute

Shadow

Reviews, never touches

Lineage · contracts · impact

Advisory

Plans, human decides

Human approves

Controlled
Autonomy

Executes, within guardrails

Within release gates

shadow → advisory → controlled autonomy

The wedge

Start with critical PySpark changes.

Most data-change tooling is optimized for SQL transformations. Complex PySpark changes on Databricks and AWS Glue need deeper context — so that is where we start, inside the pull request, before a change is approved.

Now

Build & validate

Pull-request impact review for high-risk PySpark changes, tested with enterprise data teams.

Prove in production

Five design partners; first supervised releases with approval records and rollback plans.

Long term

The change layer

The autonomous change-management layer for enterprise data systems.