Input
1,000,000,000 records
Cleaned one way, with a single approach applied to every record.
The Cleanse Cascade
Billion-Record Data Cleansing at 1/45th the AI Cost
The only data-cleansing engine that combines 180M+ proprietary records, waterfall deduplication, System One decision models and a dynamic multi-vendor LLM router. 3 of every 4 decisions are settled before any AI runs, and only about 1 in 100 records ever reaches a frontier model.
- Incoming records
- Settled in a layer, no frontier model
- Verified by the Model Router
- Frontier model (fewer than 1 in 100)
Records flow down one pipe through three stages: code and rules, reference data, and decision models. Most records settle there and go straight into one Golden records store. A thin stream, about 8 in 100, branches to the Model Router, where two models and a judge check each record; fewer than 1 in 100 records ever reach a frontier model. Every record ends in the Golden records store.
Incoming records fall down one pipe through three stacked layers: 01 Scrub and Dedup, 02 Reference and 03 System One. Each layer settles the records it can and drops them into its own column. Only the unsettled hard records, about 8 in 100, leave the pipe for the Model Router, which assigns a model by record type at the lowest price: Model A and Model B from different vendor families, a rank judge on disagreement, a frontier model only on material disagreement, then a requirement check. Every column then passes 04 Supplement, proprietary data first, and 05 Seal, lineage plus a 90-day undo, before landing in one Golden records store.
Incoming recordsCRMERPFiles
01 DedupScrub + waterfall match · code
02 Reference180M+ proprietary records
03 System OneTyped decisions
Unsettled onlyabout 8 in 100
Model Router
8 in 100
Model assignment by record type & lowest price
Model Avendor family 1
Model Bvendor family 2
Rank judgeon disagreement
+ 2% audit
+ 2% audit
Requirement checkjob + ontology rules
fail → human review
Frontier modelon material disagreement
Fewer than1 in 100 records
04 Supplementproprietary data first
05 Seallineage + 90-day undo
Golden recordsOne store · lineage on every record
SettledDedup
SettledReferenceRef.
SettledSystem OneSys. One
VerifiedModel RouterRouter
- 180M+proprietary records
- 3 of 4 decisionsbefore any AI
- 2,083 AI modelsacross 14 provider connections
- Built on Cloudflareglobal edge
- Direct pipelinesAWS, Azure, Google Cloud, Snowflake & Databricks
- 90-day undoon every merge
Key numbers
- Pre-AI settlement 75% of decisions settled before any AI runs
- Model catalog 2,083 AI models across 14 provider connections
- Frontier escalation < 1 in 100 records reach a frontier model
The problem
One billion records through one frontier model: $4.8M – $10.5M.
Every shortcut lands in one of two places: an AI bill that grows with every record, or data that is wrong in every report.
Path 1 · cost
Frontier model on every record
Every record is billed at frontier rates.
AI bill
$4.8M – $10.5M
One frontier model, one billion records.
Path 2 · rules
Rules tools miss ambiguity
Exact-match rules keep two spellings of one company apart.
Path 3 · manual
Manual cleanup takes years
The review queue falls behind while the data drifts again.
Path 4 · mass merge
One bad mass merge
An irreversible merge breaks every downstream report.
Result
Bad data in every report
Duplicates, wrong merges and stale rows reach the dashboards leaders read.
The way out
The Cleanse Cascade
Code and reference data settle 3 of every 4 decisions before any AI runs; only hard records reach a model.
Output
Clean, lineage-tracked records
Every merge previewed, every field traced, and undoable for 90 days.
Five stages, cheapest first
How it works: the Cleanse Cascade
Every record takes the least expensive path that can settle it with confidence. Only what code and reference data cannot resolve moves on to the next stage.
01 · ScrubNo AI call
-
01
Scrub
180M+ proprietary records and waterfall deduplication remove up to 37% of records as duplicates and standardize the rest, with no AI call.
No AI call · up to 37% removed -
02
Sort
System One decision models answer typed questions (same entity? which role? real record?) at $0.025 per 1,000 decisions, up to 190× cheaper than a frontier LLM.
$0.025 per 1,000 decisions -
03
Solve
A dynamic router over a 2,083-entry model catalog across 14 provider connections sends each hard record to the cheapest qualified models. Two small models from different vendor families answer independently. The rank judge runs only when they disagree (plus a 2% audit of agreed answers), a larger model is tried only on material disagreement, and a requirement check tests the answer before anything is stored.
About 8 in 100 records get a two-model check -
04
Supplement
Missing fields are filled from 74M+ proprietary professional profiles and 1.5M company profiles first, up to 55% cheaper than per-record data APIs.
Up to 55% cheaper -
05
Seal
100% field-level lineage, change-set preview and a 90-day undo on every merge.
90-day undo on every merge
Dynamic multi-vendor model router
Two models answer, a third judges.
About 8 in 100 records get a two-model check; fewer than 1 in 100 ever reach a frontier model.
- Model Router. Each record type goes to the best model for it at the lowest price.
- Model A and Model B. Two small models from different vendor families answer independently.
- Rank judge. A third vendor family runs only when A and B disagree, plus a 2% audit of agreed answers. A larger model is tried first only on material disagreement.
- Requirement check. The answer is tested against the job's requirement and your ontology rules.
- Pass: stored with lineage. Fail: human review.
Agreed answers skip the judge. Every answer passes the requirement check before it is stored.
Different record types enter the Model Router, which sends each record to the cheapest qualified models, never to every model. For a judgment-style decision, two small models from different vendor families answer independently.
The router draws on the 2,083-entry model catalog across 14 provider connections, repriced daily and benchmarked by a cross-vendor judge panel. A final requirement check tests the chosen answer against the job's requirement and the client's ontology rules (logic like "every ship-from site belongs to exactly one organization"). Region controls let a project use U.S.-hosted models only.
Ontology layer · current capability
Meaning, not just format.
Format checks catch a malformed value. They cannot catch a record that is well formed and still wrong. Cleanser.ai's ontology layer connects source definitions to the Cleanser.ai model and on to your own definitions, then checks records against that meaning, so it catches logical contradictions, not just bad formats.
Definitions are versioned. When a definition changes, every record it affects is rechecked. Records that break a rule are held for review, never silently dropped.
- Auto-discovery from your connected data
- Cleanser.ai starter model
- Import your own model
- Edit by hand
- Changes learned from grading
Five ways to build · nothing changes without your approval
Definition alignmentCurrent definition · v1.1
- Source definitionShip-from location
A site identifier supplied by the source system.
- Cleanser.ai modelSite → Organization
A site relationship resolved across systems.
- Your definition v1.1Exactly one organization
Every ship-from site belongs to exactly one organization.
Well-formed recordLive check
- ship_from_site
- Dock 4, Reno NV
- organization
- Unassigned
- format_check
- Pass
- ontology_check
- Hold
Logical contradiction. The value is valid. The relationship is not. Record held for review.
An approved change rechecks only the records it affects.
Acceptance sampling
You check our work.
Each finished lot is checked by a random sample that your own reviewers grade. The lot is accepted or rejected on those grades, and every grade feeds back into how the platform works.
-
Step 01
Finished lot
Every record in the lot is a candidate for inspection.
-
Step 02
Random sample drawn
The sample is drawn at random from the finished lot, under random acceptance sampling.
-
Step 03
- Record 0212Correct
- Record 0457Correct
- Record 0519Incorrect
- Record 0603Correct
- Record 0788Correct
Your reviewers grade the sample
Each sampled record is graded Correct or Incorrect by your team.
-
Step 04
Accept Reject
Accept when the defects in the sample are at or under the accept number, Ac. Above Ac, reject the lot.
Accept or reject, per lot
After a rejected lot, the next lot runs under tightened inspection.
Every grade feeds
- Rule fixesFailing rules get corrected.
- Router scorecardModel choices learn from graded results.
- Confidence thresholdsCalibrated to what reviewers confirm.
- OntologyDefinitions and relationships improve.
- Your private evaluation setCan train your own LoRA adapter, on grades you approve.
- Next lot's inspection levelTightened after a rejected lot.
Golden set
Upload records with known answers, and we report our accuracy against them.
Graded records stay in your workspace.
Nothing applies without your approval.
Sampling method: ANSI/ASQ Z1.4, general inspection level II, AQL 1.0 as the default. This names a sampling method. It is not a certification.
Proprietary data
Checked against the world before any paid call.
Nearly 1 billion
Nearly 1 billion reference records, including 180M+ proprietary records, checked before any paid lookup or AI call.
- Professional profiles 74M+
- Social profiles 54M+
- People-directory records 27M+
- Threat signatures 6.9M+
- Standardized job titles 3.1M
- Carrier registrations 2.0M
- Company profiles 1.5M
Enrichment · CRM · ACCT-00418
IncomingEnriched
CompanyCompany profiles · 1.5M
Halden Ind. IncHalden Industries, Inc.
Job titleStandardized job titles · 3.1M
dir opsDirector, Operations
ContactProfessional profiles · 74M+
no valueVerified profile matched
LocationProprietary records · 180M+
Chicago IL60601 · Cook County, IL
Time zoneProprietary records · 180M+
no valueAmerica/Chicago
Threat screenThreat signatures · 6.9M+
not screenedNo threat signature match
Illustrative record. Fields are filled from reference sets before any paid lookup or AI call.
Entity family trees
One entity, one family tree.
Across CRM, ERP and data lakes: ultimate parent → subsidiaries → sites → people. Every site carries ZIP, county, coordinates and time zone.
Proof · measured
Proven on our own data first.
On a Mojo Creator data project, Cleanser.ai's methods cleansed 839,000+ business records and consolidated 213,286 raw rows into 133,920 unique records — 37.2% duplicates removed by code alone, before any AI was involved.
Business records cleansed
839,000+
Raw rows → unique records
213,286 133,920
Duplicates removed by code
37.2%
removed by code alone, before any AI
0213,286 raw rows
Bar drawn to scale: the solid part is what remains, the hatched part is what code removed.
AI-cost calculator
Drag the dial. Watch the gap.
Up to 97% less AI cost, and up to $10M saved per billion records. Compare Cleanse Cascade AI processing ($0.10 per 1,000 records) with one frontier model on every record ($4.80 – $10.50 per 1,000 records).
Instrument · AI processing cost● Live
100,000,000 records
Cleanse Cascade AI processing
$10,000
$0.10 per 1,000 records
One frontier model on every record
$480,000 – $1,050,000
$4.80 – $10.50 per 1,000 records
You save
$470,000 – $1,040,000
1/45th the AI cost
| Records | Cleanse Cascade AI processing | One frontier model on every record |
|---|---|---|
| 1M | $100 | $4,800 – $10,500 |
| 100M | $10,000 | $480,000 – $1,050,000 |
| 1B | $100,000 | $4.8M – $10.5M |
Estimate based on internal modeling.
Pricing
Metered. Transparent. No seats.
$5
per 1,000 recordsVerify, standardize and score records against nearly 1 billion reference records.
$25
per 1,000 recordsWaterfall deduplication, System One decisions and entity family trees, with lineage on every merge.
$0.05
per verified field foundFill missing fields from proprietary profiles first. Pay only for verified fields found.
$2
per 1,000 records per monthKeep CRM, ERP and lake records clean as new data arrives.
How records are counted. Validate, Resolve and Maintain are priced per 1,000 records, and a record is one row. Price per record depends on how many columns each record carries; very wide records are quoted by column count.
Free 10,000-record data-health auditSee duplicates, gaps and what fixing them costs.
Get your free data-health audit Industries
Wherever records multiply.
Manufacturing
Unify supplier, part and plant records across every ERP instance.
Real Estate
Resolve owners, properties and sites into one clean ledger.
Travel & Transportation
Match carriers, shippers and travelers across booking and fleet systems.
Healthcare & Life Sciences
Deduplicate providers, facilities and sites with field-level lineage.
Retail & Consumer Goods
One view of every customer, store and supplier.
Communications, Media & High Tech
Reconcile accounts, subscribers and partners across billing and CRM.
Energy & Resources
Connect sites, assets and contractors to the right parent entity.
Public Sector
Clean constituent and vendor records without moving data out of your cloud.
Financial Services
Resolve entities and family trees for cleaner onboarding and risk views.
Safety by design
Nothing is permanent for 90 days.
- Preview every change before it touches your CRM.
- Undo any merge for 90 days.
- If a batch misses our 99.5% merge-accuracy goal, we reprocess it free and credit 25%.
CRM · ACCT-00418
Halden Ind. Inc
Chicago, IL · 60601
ERP · V-22917
HALDEN INDUSTRIAL
Cook County
Merged · golden record
Halden Industrial Inc.
60601 · Cook County · America/Chicago
Day 03060Day 90
Infrastructure
Built on Cloudflare, connected to everything.
Secure, encrypted direct pipelines to AWS, Microsoft Azure, Google Cloud, Snowflake, Databricks, your data lakes and object storage, plus Salesforce and leading CRMs and ERPs.
TLS 1.3 in transit · AES-256 at rest · only columns you approve leave your systems
- Share
Zero-copy, read-only, revocable.
- Egress
Our service connects out through an outbound-only Cloudflare Tunnel or from allowlisted IPs; mTLS where your endpoint accepts it.
- Ingress
Your systems call us behind Cloudflare Access with service tokens or mTLS, plus signed webhooks.
Your data stays where it lives.
Free 10,000-record audit