Data quality · Metadata · AI context
A data quality, metadata and AI context layer over the systems you already run. It catalogs, tests and traces your data, and gives AI agents the metadata to use it without guessing. Read-only. Configured for your industry in week one.
Week 1
Read-only connection. No write path, no vendor change.
4 to 6 weeks
Fixed-fee pilot on one domain. Decide with evidence.
100% exportable
Your metadata, in open formats, any time.
Your environment
Or private cloud, or hosted. Data never moves.
Why now
Your next data consumer is not a person. It is an agent that takes your data at face value at machine speed.
For twenty years a human caught bad data by noticing something looked off. AI agents do not notice. They propagate ambiguity, confidently, into every downstream decision.
Metadata is now an API contract. The organizations agents are told to trust are the ones that can say, for any number: where it came from, what checked it, who owns it.
2016
A person squints at a spreadsheet cell and sends an email.
2026
A stream of agent requests hits the dataset. Each is answered from a provenance card, or from nothing.
Anatomy of a trusted number
Every arrow is a place trust can leak.
Submitted
Hundreds of producers, each with their own conventions.
Verified
Rigorous for the product, ad hoc for the pipeline that publishes it.
Published
The same fact in four systems, reconciled by hope.
Consumed
By regulators, subscribers, members, and now agents.
quiet defect 01
Identifier drift.
The ID on the physical thing and the ID in the database started life identical, and drifted. Ten years later, search only works if you type less of it.
quiet defect 02
Two defensible numbers that disagree.
Not a wrong number. Two systems, each right by its own logic, discovered by someone outside the building.
quiet defect 03
The silent absence.
A wrong value eventually earns an angry email. A missing record just looks like “no result found.”
The layer
One layer. Three planes. Nothing replaced.
Quality plane
Test the pipeline the way you test the product.
Automated tests on every dataset: completeness, format, freshness, cross-system consistency, reconciliation. Failures arrive with root cause, not a meeting invite.
registry_export, nightly run
pass
reference_number_present
pass
rating_matches_regulator
fail
matched_combinations_complete
Root cause: 14 rows missing upstream
Metadata plane
A single account of what exists, what it means, and where it travels.
A catalog that finds things the way people describe them. A glossary linked to the columns that carry each term. Column-level lineage that answers “if I change this, what breaks.”
Lineage for certified_rating
source
lab_results.value
registry
registry.rating
published
regulator_feed, public_api
Change here touches 3 feeds and 2 dashboards
AI context plane
Your estate, queryable in plain English, by people and by agents.
The whole layer exposed over MCP, the open protocol AI assistants speak. Ask which datasets failed this week and what they feed; get an answer built from catalog, tests and lineage. Access policy follows the data.
An agent asks over MCP
ask
which datasets failed this week?
found
matched_combinations
feeds
public_api, member_portal
Answer built from catalog, tests and lineage
DQHQ reads from what you already run. It is not in the write path and it does not replace your systems of record.
Configured for your vertical
Ships configured for your industry. Customized to your rules.
Each pack ships with a glossary, test library, classifications, data product templates and agent context for your industry. Week one adjusts it; nobody builds from scratch.
A certification mark is a promise that a number is true. DQHQ keeps that promise cheap to keep.
Patterns we keep seeing
Re-rating waves without impact analysis.
A method or standard changes. Nobody can name every place the old number travels.
Member-submitted data at the gate.
Hundreds of external producers, validated by diplomacy.
Data that is sold.
Subscribers do not file tickets. They churn.
In the pack
- Glossary: certified rating, reference number, matched combination, re-rate, effective date
- Classifications: public, member-submitted, confidential
- Test library for registry pipelines
- Data product template: registry subscription with owner, SLA, quality score
- Agent context for registry queries
Sample tests
No certified record is published without a reference number.
Model numbers match the program's pattern.
Public registry rating equals the regulator feed, nightly.
Expected matched combinations equal published; absences alert.
The pilot delivers
- One registry pipeline cataloged and searchable
- Test suite on schedule, with one cross-system check
- One methodology change traced with impact analysis
- One executive quality dashboard
Operating model
Built for organizations with governance people, not governance departments.
The enterprise suite model
A multi-year rollout.
A methodology binder.
A stewardship team to hire.
AI bolted onto a catalog.
The DQHQ model
Live in weeks.
One busy owner, plus AI that profiles, suggests tests and runs root cause.
The platform is the department.
Agent access native, over MCP.
The honest design goal: one careful person, properly armed, defending millions of records.
How this starts
Four to six weeks. One domain. Read-only. Fixed fee.
Week 1
Read-only connection, vertical pack applied, catalog builds itself, AI profiles what it finds.
Weeks 2 to 4
Tests tuned to your rules, lineage traced on the change you care about most, classifications applied.
Weeks 5 to 6
Executive dashboard, impact analysis walkthrough, decision.
Four deliverables, always four
Domain cataloged and searchable
Automated test suite on schedule, including one cross-system check
One real change traced end to end with impact analysis
One leadership quality dashboard
If it is not obviously worth continuing, we shake hands and you keep the catalog.
30 minutes. Bring the pipeline that generates the most support questions; you already know which one.
Open by design
Your metadata stays yours. Exportable forever.
Your metadata is held in open, documented formats and exportable in full at any time, so the walk-away cost is near zero by design.
DQHQ deploys inside your environment, in a private cloud, or hosted. Your data never moves for the layer to work; the connection is read-only.
Agent access uses MCP, the open protocol, so what DQHQ knows is usable by whatever AI tooling you standardize on next year.
M
“I spent fourteen years running data platforms where a bad number did not mean a bad dashboard; it meant payments went wrong at real-world speed. Scale was the easy part. The hard part is standing behind any single number in five minutes, not five days. That is what DQHQ exists to solve.”
Mahesh, founder, Dataring
Questions we get
The questions, answered.
A number should be able to account for itself. Let’s start with one of yours.
Or send us the pipeline that hurts most, under “Your industry” above.
DQHQ is a configurable data quality, metadata and AI context layer that sits over the systems you already run, so every number your organization publishes can explain itself: where it came from, what checked it, who touched it.
A Dataring product. ShareData Inc., Delaware · Datawarp Technologies Pvt. Ltd., India.
© 2026 ShareData Inc.