7 stall on data3 ship
~0%
of AI projects never reach production
The models are rarely the blocker. Fragmented sources, inconsistent definitions, and ungoverned access stall projects before they ship.
Most AI solutions fail on the data, not the model. We fix the data first.
We make your data usable for AI — parsing the documents, cleaning the records, and wiring the pipelines. Our AI agents draft the extraction and enrichment; our engineers own every schema, merge rule, and quality gate. Trusted data, governed end to end.
3–0 wks
From data audit to first governed data product
0%
Of schema and pipeline changes pass a human review gate
0%
Visibility on how the data is handled and consumed
0
Shared context layer every agent in the domain can read from
First data product means one governed, documented dataset in production. Bigger estates take longer — and we'll say so up front.
Why data work is stuck
The model is ready. The data usually isn't.
Most teams don't lack data — they lack access to data that's clean, connected, and governed. Sources are fragmented, definitions disagree, and the most valuable data hides inside documents nobody has time to parse. That's the gap Data AI closes.
unstructured
structured
docs · email · calls · scans
~0%
of enterprise data is unstructured
Contracts, emails, transcripts, and scans hold most of the value — and most stacks handle them worst. That data stays locked away.
AI-ready
the rest is tangled, siloed, or ungoverned
~0%
of orgs call their data AI-ready
Nearly everyone has data but only a few have data that's clean, connected, and governed enough to trust in a model or a decision.
How we work
Five phases from raw data to a governed asset.
Every phase is a hand-off loop — AI does the first pass, our engineers own the schemas, the rules, and the sign-off.
01/ 05
01AuditKnow what you have
Source audit · quality scan
crm.contacts
pii
billing.invoices
support.emails
pii
orders.export
AI drafts
Profiles every source, scores quality, and flags sensitive fields
Humans own
Decide what's worth fixing and what stays out of scope
02OnboardBring it in cleanly
Ingestion pipeline
AI drafts
Parses documents, emails, and exports into structured rows
Humans own
Own the schema and the contract every source maps to
03PrepareMake it trustworthy
Entity resolution
Acme Inc. · acme@co
ACME, Inc · billing@acme
Acme Incorporated · acme@co
Acme, Inc. · golden record
AI drafts
Proposes dedupes, entity matches, and enrichment across sources
Humans own
Approve the merge rules before a golden record is written
04ProductizeShip a real asset
customers · data product
owner
data platform
freshness
≤ 6h
rows
1.24M
schema valid
no null keys
freshness met
AI drafts
Drafts documentation, freshness checks, and quality tests
Humans own
Set the owner, the SLA, and what 'good' means for this dataset
05OperateMonitor and maintain
Pipeline monitor
freshnesson time
row volume+2.1%
schema driftcaught
null rate0.0%
Drift quarantined · engineer notified
AI drafts
Watches freshness, drift, and volume; clusters anomalies
Humans own
Hold the call on remediation — nothing silently degrades
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How we work
Five phases from raw data to a governed asset.
Every phase is a hand-off loop — AI does the first pass, our engineers own the schemas, the rules, and the sign-off.
01AuditKnow what you have
Source audit · quality scan
crm.contacts
pii
billing.invoices
support.emails
pii
orders.export
AI drafts
Profiles every source, scores quality, and flags sensitive fields
Humans own
Decide what's worth fixing and what stays out of scope
02OnboardBring it in cleanly
Ingestion pipeline
AI drafts
Parses documents, emails, and exports into structured rows
Humans own
Own the schema and the contract every source maps to
03PrepareMake it trustworthy
Entity resolution
Acme Inc. · acme@co
ACME, Inc · billing@acme
Acme Incorporated · acme@co
Acme, Inc. · golden record
AI drafts
Proposes dedupes, entity matches, and enrichment across sources
Humans own
Approve the merge rules before a golden record is written
04ProductizeShip a real asset
customers · data product
owner
data platform
freshness
≤ 6h
rows
1.24M
schema valid
no null keys
freshness met
AI drafts
Drafts documentation, freshness checks, and quality tests
Humans own
Set the owner, the SLA, and what 'good' means for this dataset
05OperateMonitor and maintain
Pipeline monitor
freshnesson time
row volume+2.1%
schema driftcaught
null rate0.0%
Drift quarantined · engineer notified
AI drafts
Watches freshness, drift, and volume; clusters anomalies
Humans own
Hold the call on remediation — nothing silently degrades
Governance isn't a final phase — privacy, lineage, and access controls run through every step, not bolted on at the end.
Why MintMux
We don't sell you a platform. We make your data usable.
AI in the pipeline, humans on the contract, and everything visible — without the oversell.
Your stack, not a replatform
We build on the warehouse, lakehouse, or database you already run. No rip-and-replace, no platform we're quietly reselling — an AI layer on top of what you have.
Replatform
rip out warehouse
migrate every table
Mintmux
Your warehouse
Your lakehouse
+ AI layer
AI in the pipeline, humans on the contract
Parsing and enrichment run as pipeline stages. Every schema and merge rule is engineer-approved.
Pipeline · one review gate
parse
extract
enrich
→
every schema + merge rule signed off
Unstructured is first-class
PDFs, emails, and calls become queryable rows — not a someday backlog.
claim.pdf
call.mp3
invoice.png
queryable row
queryable row
queryable row
You can see everything
Lineage, quality checks, and run history are shared from day one. No black box.
LineageTRACKED
Quality checksOPEN
Run historySHARED