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Governance, quality, metadata, privacy and master data, running as agents inside your own environment, on Databricks, Azure, AWS, DataHub or Informatica. This is your estate: grab any node to see it hold, then press run the agents and watch three copies nobody registered get found and brought home.
Orphan copies 3 · Edges held 10
Hover a node to inspect it · drag to feel the springs Tap a node to inspect it · drag to feel the springs
The data work still gets done by hand: someone profiles the tables, writes the quality rules, traces the lineage, finds the sensitive columns, and keeps the catalog current. Nine of our agents already do that work in production, inside your own environment, with a steward approving every change.
Runs the day-to-day work of your catalog whenever you ask in plain language.
23 tools in production, catalog search through glossary publish
See the technical layerData Governance · Function: Governance Agent
Locate the asset and its connection before anything is touched.
Configured against the live connection, not a copy.
Statistics computed directly from source where the platform supports it.
Completeness, validity, consistency and uniqueness rules tailored to what the profile found.
Rule definitions written natively into your data quality tool.
The job that runs the rules at scale.
One command takes a new dataset from unknown to governed.
7-step orchestration live, with per-step status and timing
See the technical layerData Governance · Function: Data Onboarding
Check whether the asset is already known.
Compute the statistics everything downstream depends on.
Identify sensitivity before the data is exposed to anyone.
Recommend and author rules from the profiling evidence.
Register the asset, its scores and its rule results.
Create or link the business terms that make it findable.
Optionally publish as a data product, ready to be ordered.
Profiles any table, drafts the metadata, writes the rules, and proves each one.
45 rules executing · 1,622 records scanned · 3–5× throughput at ~$0.03/rule
See the technical layerData Quality · Function: DQ Agent
Every column profiled and flagged with evidence: nulls, distributions, patterns, date ranges, duplicates.
Business descriptions and domain terms drafted automatically, at table and column level.
DQ rules with ready-to-run SQL and the evidence behind each.
A steward reviews, edits the SQL, and approves or rejects.
Approved rules run on schedule.
Watches quality scores over time and tells you what is drifting, and what to do.
4 tools in production, trend detection against score history
See the technical layerData Quality · Function: DQ Monitor
Current quality scores for any registered asset.
Detect degradation and drift relative to previous runs.
Turn raw failures into a structured picture of what broke and where.
A specific next action, not just a red number.
Register alerting so coverage does not depend on somebody remembering.
Writes the descriptions nobody ever writes, for every table and every column.
AI-written, steward-approved, no authoring backlog
See the technical layerMetadata Management · Function: AI business & technical descriptions
Column names, types, sample values, table context and neighboring columns.
Technical and business descriptions at both table and column level.
Approve, edit or reject.
Approved descriptions land in the catalog and the glossary.
Builds and defends the business glossary: terms, domains, and the collisions between them.
5 tools in production, scanning the live glossary for collisions
See the technical layerMetadata Management · Function: Glossary Manager
Proposed from a technical asset’s columns and context.
Business terms added to your catalog with their definitions.
Domain, subdomain and term, created and linked as one operation.
The live glossary scanned for duplicates, orphans and definition gaps.
Traces where data came from and what breaks if you change it.
3 tools in production, tracing into Snowflake, Databricks and Microsoft Fabric
See the technical layerData Architecture & Integration · Function: Lineage Reporter
Walk back through every transformation to the systems of record.
Find every consumer, report and copy that depends on the asset.
Impact reports ordered by consequence, not alphabetically.
Identify the true origin behind a derived asset.
Converts integration estates to a new stack, with every manual review item flagged.
11 component mappings covered, 4 generated artifact types per path
See the technical layerData Architecture & Integration · Function: AI Migration Services
Mappings, tasks, chained jobs, processes and connections read out of the source platform.
Each component mapped to its target equivalent: flows, transformations, scheduled workflows, SQL models, workflow steps.
Build-ready output: project structure, dependencies, configuration, deployment descriptors.
A coverage report names what converted cleanly and what needs manual review.
Finds sensitive data and weighs four layers of evidence before anything is tagged.
40+ PII patterns · dual registration to your platform and governance catalogs · audit trail per tag
See the technical layerData Privacy & Compliance · Function: AI Data Classification
Catalog API enumerates the estate.
40+ PII patterns: Social Security number, credit card number with checksum validation, email, phone, postal code, IP address, international bank account number.
Column name, 20 sample values, table context and neighboring columns go to the model.
Layers 1 and 2 agreeing auto-tags.
An agent behind each job those disciplines cover. Type to find one, or let the spotlight walk you through them a proof line at a time.The full portfolio is below.
Runs the day-to-day work of your catalog whenever you ask in plain language.
23 tools in production, catalog search through glossary publish
The nine are running on client data today, and each card carries the result it produced. The rest deploy into your environment on the same framework. Adopt one, adopt a dozen; each one works on its own.
Operating the catalog itself, on Databricks, Azure, AWS, DataHub or Informatica.
6 agents
Function: Governance Agent
Runs the day-to-day work of your catalog whenever you ask in plain language.
23 tools in production, catalog search through glossary publish
23 tools · 8-step pipeline · key-pair auth · full audit trail
Your catalog · Your data quality tool · Any connected source
Function: Data Onboarding
One command takes a new dataset from unknown to governed.
7-step orchestration live, with per-step status and timing
1 command · 7 stages · non-fatal failure handling
Databricks · Snowflake · Microsoft Azure · AWS · Informatica
Runs governance natively inside Databricks Unity Catalog, no external suite in the loop.
Catalog operations · tags and comments · system tables · governed tags · ABAC
Databricks · Unity Catalog
Operates Microsoft Purview and Fabric governance from a conversational interface.
Scans and classification · glossary · lineage · Fabric domains · sensitivity labels
Microsoft Purview · Microsoft Fabric · Microsoft Azure
Governs the AWS Glue Data Catalog and Lake Formation without a third-party catalog.
Crawler orchestration · table and column metadata · Lake Formation tags · Athena validation
AWS Glue · AWS Lake Formation · Amazon Athena · Amazon Redshift
Stands up and operates open-source DataHub, a full catalog with no license line item.
Ingestion recipes · domains and tags · ownership · deprecation · policy as code
DataHub · Any source DataHub ingests
Function: DQ Agent
Profiles any table, drafts the metadata, writes the rules, and proves each one.
45 rules executing · 1,622 records scanned · 3–5× throughput at ~$0.03/rule
8 profiling dimensions · SQL validation gate · steward queue · budget cap
Databricks · Snowflake
Function: DQ Monitor
Watches quality scores over time and tells you what is drifting, and what to do.
4 tools in production, trend detection against score history
4 tools · degradation and drift detection · remediation recommendations
Your quality scorecard · Unity Catalog · Informatica
Puts quality gates inside the pipeline, so bad data stops before it lands.
Stage gates · go/no-go on counts and completeness · dbt tests · failure routing
Azure Data Factory · AWS Glue · Apache Airflow · dbt · Databricks Workflows
Function: AI steward workflows
Reads a failed rule, works out why, and routes it to the person who owns it.
Root-cause analysis · pattern detection · org-aware routing from your directory
Databricks · Microsoft 365 · Microsoft Teams
Profiling and rule generation native to Microsoft Fabric and OneLake.
Lakehouse profiling · rule generation · Purview registration · Power BI surfacing
Microsoft Fabric · OneLake · Azure Synapse
The DataIQ pattern, running natively on Redshift and Athena.
Redshift-native profiling · generated SQL · scheduled execution · Glue registration
Amazon Redshift · Amazon Athena · AWS Glue
Function: AI business & technical descriptions
Writes the descriptions nobody ever writes, for every table and every column.
AI-written, steward-approved, no authoring backlog
Table and column level · approve/edit/reject queue · catalog publish
Unity Catalog · Microsoft Purview · DataHub · Informatica
Function: Glossary Manager
Builds and defends the business glossary: terms, domains, and the collisions between them.
5 tools in production, scanning the live glossary for collisions
5 tools · 3-level taxonomy · duplicate and orphan detection
Your catalog · Your business glossary
Turns physical schemas back into models people can reason about.
Conceptual and logical reconstruction · naming conformance · drift detection
Any relational source · Databricks · Snowflake · Amazon Redshift
Business glossary, domains and ownership as code, inside open-source DataHub.
Glossary nodes and terms · domain hierarchy · ownership · term propagation
DataHub
Function: Lineage Reporter
Traces where data came from and what breaks if you change it.
3 tools in production, tracing into Snowflake, Databricks and Microsoft Fabric
3 tools · upstream and downstream · severity-ranked impact · root-source resolution
Databricks · Snowflake · Microsoft Fabric · Informatica · DataHub · Microsoft Purview
Function: AI Migration Services
Converts integration estates to a new stack, with every manual review item flagged.
11 component mappings covered, 4 generated artifact types per path
2 migration paths · 11 component mappings · coverage and review reporting
MuleSoft Anypoint · dbt · Apache Airflow · Databricks · Informatica
Finds the copies. Maps the sprawl you did not know you had.
Landscape reconstruction · copy-sprawl detection · target-state proposal
Any · Databricks · Microsoft Azure · AWS · Snowflake
Watches the operational health of the estate your governance program depends on.
Growth and partition health · retention conformance · cost anomaly detection
Snowflake · Databricks · Amazon Redshift · Azure Synapse
Keeps Delta and Iceberg tables healthy, and stops them quietly costing a fortune.
OPTIMIZE and VACUUM scheduling · liquid clustering · small-file detection · cost attribution
Databricks · Delta Lake · Apache Iceberg
Classification, sensitivity, access policy and unstructured content.
5 agents
Function: AI Data Classification
Finds sensitive data and weighs four layers of evidence before anything is tagged.
40+ PII patterns · dual registration to your platform and governance catalogs · audit trail per tag
4-layer pipeline · 5 PII categories · 4 sensitivity tiers · reviewer feedback loop
Databricks Unity Catalog · Microsoft Purview · Informatica
Turns classification tags into access policy, then watches the drift.
Entitlement drift · least-privilege recommendations · ABAC policy generation
Unity Catalog · Snowflake · AWS Lake Formation
Answers a privacy request by knowing where every copy of a person lives.
Subject discovery across systems · DSAR packet assembly · consent state · deletion evidence
Any · Databricks · Snowflake · Salesforce
Extends governance past the database, into contracts, reports and the rest of your documents.
Entity extraction · sensitivity tagging · retention · catalog registration
SharePoint · Amazon S3 · Box · Google Drive
Governs who can reach what across S3 and Lake Formation, tag by tag.
LF-tag policy generation · S3 exposure detection · cross-account review · access audit
AWS Lake Formation · Amazon S3 · AWS IAM
Builds the golden customer record, and shows its working for every merge.
Candidate matching · survivorship · cluster review · downstream sync
Databricks · Snowflake · Salesforce · Informatica MDM
One trustworthy supplier and material master, across every plant and every ERP.
Duplicate supplier detection · material harmonization · ERP reconciliation · spend rollup
SAP · Databricks · Snowflake · Informatica MDM
Function: Reference data governance
Governs the code lists everyone forgets are data.
Reference set discovery · ownership assignment · controlled change
Any · Informatica MDM · Databricks
Proposes the match rules and survivorship logic, with duplicate clusters as evidence.
Candidate rule generation · survivorship proposals · cluster review queue
Any master data hub · Databricks · Informatica MDM
Proves which AI systems are running on governed data, and which are not.
EU AI Act and NIST AI RMF evidence · model-input lineage · policy enforcement points
Any catalog · Databricks · Azure ML · Amazon SageMaker
Watches what your models are being fed, and flags it when the data shifts under them.
Training-input lineage · feature drift · data-quality gates on inference · model cards
Databricks · Azure ML · Amazon SageMaker · MLflow
Stops your internal chatbot answering from documents it should never have seen.
Corpus classification · permission-aware retrieval · citation audit · exclusion policy
Any vector store · SharePoint · Databricks · Azure AI Search
Turns a useful table into a documented, owned, contract-backed data product.
Product definition · contract and SLA · owner assignment · marketplace publish
Databricks · Snowflake · Informatica CDMP · DataHub
Function: Governed delivery
Order data like a product. Get a scoped view, not a copy.
Marketplace ordering · automated approval · no-copy provisioning · timed revocation
Databricks · Snowflake · ServiceNow · Informatica CDMP
Tells you which dashboards are built on data you would not stand behind.
BI asset certification · report-to-source lineage · uncertified-source flagging
Power BI · Tableau · Microsoft Fabric
Read the estate as it actually is: lineage, landscape, schema, gaps.
4 agents
Propose rules, terms, descriptions, tags and matches, each with its evidence.
8 agents
Execute, monitor, route failures and hold policy in place.
8 agents
Register decisions to the catalog of record and keep the taxonomy honest.
12 agents
Put governed data in the hands of the people who asked for it.
4 agents
No agents match that.
Clear the search, or tell us what you were looking for. If it is not in the portfolio, that is usually a scoping conversation rather than a no.
Step 1 of 3 · nothing captured
Where does your data actually live?
Pick every platform that applies.
Step 2 of 3 · nothing captured
What industry are you in?
This shapes the shortlist.
Step 3 of 3 · nothing captured
What is actually hurting right now?
Pick as many as are true.
Your shortlist
Domain quality scores from the pilot and DataIQ’s production counters, drawn the way the agents publish them to the catalog. The feed replays the kind of events a steward sees.
Pilot scorecard
Rules executing 45 · Records scanned 1,622 · Anomalies found 66
Pull the handle and watch what changes.
Before · ungoverned
After · with Incept
Customer record
Duplicated across 4 systems
Mastered
Data quality
Ad hoc checks, no scorecards
Scored on every run
Lineage
Tribal knowledge and spreadsheets
Fully traced
Access
Email requests, weeks of waiting
Policy-based
AI readiness
Pilots stall on dirty data
Production
They fund it to get claims paid correctly, to file a study on time, or to answer a regulator without weeks of reconstruction.
Healthcare & Payer
Claims data lands in a lake with no lineage back to the source of record, enterprise DQ rules are written but never applied, and every downstream consumer takes another copy.
Open the business caseThe situation
Claims data lands in a lake with no lineage back to the source of record, enterprise DQ rules are written but never applied, and every downstream consumer takes another copy. Nobody can answer a regulator quickly because nobody can prove where a number came from.
The result
Measured against your own baseline during the pilot, the way the throughput and score numbers above were.
Life Sciences
Study, site, safety and product data sit in separate systems with bare schema and no business descriptions.
Open the business caseThe situation
Study, site, safety and product data sit in separate systems with bare schema and no business descriptions. Onboarding a new domain takes a quarter, and the quality rules that exist are applied by hand to a fraction of the tables.
The approach
The result
Measured against your own baseline during the pilot, the way the throughput and score numbers above were.
All industries
Models and copilots are already in production, reading from data nobody has classified.
Open the business caseThe situation
Models and copilots are already in production, reading from data nobody has classified. There is no evidence pack for what a model was trained on, and an internal chatbot will happily answer from a document the asker was never entitled to see.
The result
Measured against your own baseline during the pilot, the way the throughput and score numbers above were.
Financial Services · Healthcare & Payer · Energy & Utilities · Government
A report is challenged and it takes weeks to reconstruct how the number was produced.
Open the business caseThe situation
A report is challenged and it takes weeks to reconstruct how the number was produced. Lineage stops at the warehouse, controls are documented in a spreadsheet, and evidence is assembled by hand every cycle.
The approach
The result
Measured against your own baseline during the pilot, the way the throughput and score numbers above were.
Retail & CPG · Financial Services · Healthcare & Payer
The same customer is duplicated across systems with no agreed golden record, and when a privacy request arrives nobody can say with confidence where every copy of that person lives.
Open the business caseThe situation
The same customer is duplicated across systems with no agreed golden record, and when a privacy request arrives nobody can say with confidence where every copy of that person lives.
The result
Measured against your own baseline during the pilot, the way the throughput and score numbers above were.
All industries
A legacy integration estate has to move, and nobody has a reliable inventory of what it does.
11
component mappings covered
The situation
A legacy integration estate has to move, and nobody has a reliable inventory of what it does. The lift is quoted in years because discovery alone is quoted in months, and every copy created along the way re-creates governance from zero.
Open any line and see how we approach it, which agents do the work, and what it measures. Every one of these comes from delivered or in-flight engagements.
AI-driven DQ rule generation
All industriesThe approach
The agent profiles a table, recommends rules with the evidence attached, and writes the SQL. A steward approves.
Run by
Enterprise DQ framework rollout
Healthcare & Payer +4The approach
Parameterized checks at every pipeline hop, domain by domain, with go or no-go gates on each load.
AI business and technical descriptions
All industriesThe approach
Bare-schema catalogs described at machine speed, every draft through an approve, edit or reject queue.
Run by
What it measures
Every draft steward-reviewed, authoring no longer the bottleneck
Sensitive-data classification for a regulator
Healthcare & Payer +3The approach
Four layers of evidence before anything is tagged, dual-registered to platform and governance catalogs.
Run by
DQ observability and drift alerts
All industriesThe approach
Scores tracked across runs; a slow decline becomes a routed remediation, not a surprise.
Run by
Steward workflows in Microsoft Teams
Healthcare & Payer +4The approach
Failures routed to the right owner from the live org chart. Approve or reject without leaving Teams.
Run by
Reference data governance
Life Sciences +3The approach
Code lists and controlled vocabularies get owners, versions and a controlled change path.
Run by
One supplier master across every ERP
Manufacturing +3The approach
Duplicate vendors surface, material codes harmonize, and spend finally rolls up to a number the CFO can use.
Run by
Data products with governed delivery
All industriesThe approach
Order a product, receive a scoped secure view, access lapses on a timer. No more private extracts.
Lineage for a challenged KPI
Financial Services +3The approach
Trace a number back through every hop to its system of record, ranked by blast radius.
Run by
Copy-sprawl discovery
All industriesThe approach
Find the unregistered extracts and shadow pipelines nobody documented, with the governance gap named.
Run by
Integration estate migration
All industriesThe approach
Estates converted to dbt and Airflow or MuleSoft, coverage reported, manual review items flagged.
Run by
AI-readiness evidence pack
Healthcare & Payer +3The approach
Which models read which governed data, with the policy gates and the record a reviewer asks for.
Run by
Governed retrieval for internal copilots
All industriesThe approach
The corpus classified, retrieval filtered by the asker’s entitlements, every citation audited.
Customer 360 and consent
Retail & CPG +2The approach
A golden record with its working shown, and privacy requests answered from a map of every copy.
Run by
The thing you are actually afraid of is a permanent consultant dependency. This is how the work changes hands.
Cost falls as autonomy rises. Self-sufficiency is the design, not a hope.
Every agent runs inside your own environment on credentials you issue and can revoke. Nothing is processed or stored outside infrastructure you control.
Agents execute in your own Databricks, Snowflake, Azure or AWS environment. Nothing is copied out to make them work.
Key-pair against scoped service roles, least privilege by default. Revoke the role and the agent stops.
Every rule and description waits in a steward’s queue. A tag applies on its own only where independent evidence agrees; anything uncertain queues for a person.
Every operation logged against the identity that performed it. Every model call recorded with inputs and cost.
Per-domain monthly ceilings you set before the first run.
No screen scraping, no unsupported paths that break on a vendor upgrade.
Drag through the engagement and watch who carries the work.
Incept seniors lead with the proven playbook. Agents run alongside, measured. Your stewards learn by approving.
The work flips: agents execute at scale. Incept tapers each quarter to oversight and edge cases. Stewards take the judgment.
Incept stays for advisory only. Agents sustain coverage. Your stewards own every decision.
An illustrative plan. Your own split is agreed before the pilot starts.
Five days of read-only assessment with your system owners. We agree the first domain and tell you which agents are worth your time.
One anchor domain end to end, agents deployed inside your network, measured against your manual baseline.
Domain by domain on your priority order. The team tapers each quarter as agents and your stewards absorb the work.
Primary point of contact for implementations, pilots and solution guidance.
470-234-1172sam.jasper@inceptds.com
Discovery sessions, technical deep dives and customer alignment.
217-974-0104benaka.harish@inceptds.comDiscovery is read-only, takes five days, and costs nothing. We assess your environment, agree the first domain, and tell you honestly whether this is worth your time.
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