Trust Your Data
Data Foundation for AI
The same customer entered five different ways across your systems is why your reports disagree and your AI guesses wrong. We merge everything into one clean version of the truth — and keep it clean automatically.
Every AI initiative you fund fails silently if your master records are noisy. This is the cleansing engine that makes downstream AI actually work.
Potential value on day one
+$172,380/yr freed
Example — 3 people × $65/hr × 20 hrs × 52 wks × 85% straight-through processing.
Model this with your own headcount below ↓
The value math
Before, after, and what it's worth.
On the left is what it costs to keep doing this by hand. On the right is what happens after the package goes live — and what that frees up on day one.
Today
Today · The data-quality treadmill
How data quality gets handled today
- 1
New records land — customers, vendors, products from multiple sources
CRM sync, ERP feed, vendor onboarding form, spreadsheet upload. Everyone sends the same entity a different way.
- 2$
The data team writes dedup scripts against last week's mess
SQL joins, string similarity, hand-tuned thresholds. Every week the rules need re-tuning.
- 3$$$
Manual review of match candidates — is 'Acme Inc' the same as 'ACME Corp'?
Data engineers eyeball rows one by one. Some are obvious, some are judgment calls. Reviewer fatigue sets in.
- 4$
Downstream systems already broke on stale data — chase quality tickets
Finance can't reconcile. Sales sees duplicate accounts. AI pilots return weird results. All roads lead back to master data.
- 5
Ticket closed. Next batch arrives tomorrow.
Yesterday's cleanup is stale before it's done. The treadmill never stops.
Human in the loop
A small data team, full-time, forever chasing quality issues.
The hidden cost — silent AI failure
Dirty data compounds silently. AI pilots fail because they're grounded in noise — leadership blames the model, but it's really the data. Reports contradict each other. Migrations blow up in UAT because the source is a mess. The finance team never trusts the number on the dashboard, so they build their own spreadsheet. And that spreadsheet also drifts.
Yearly expense — today
−$202,800/yr
100% payroll burn on manual data entry.
3 people × $67,600 fully-loaded per seat.
After DEBCOR AI
With DEBCOR AI · Continuous cleansing
How data quality holds after the package ships
- 1
New records land via existing pipelines — no source change needed
Same feeds, same shapes. Nothing your source systems have to do differently.
- 2
AI profiles, dedups, and cleanses in real time
Embeddings + rules + enrichment run on every record as it lands. Match confidence scored on the way in.
- 3
High-confidence records auto-merge; the golden record stays clean
Anything above your threshold posts straight into the master. 85% of records never need a human touch.
- 4$
Low-confidence matches route to the data steward — but only once
The remaining 15% land in a reviewer UI with match evidence highlighted. Each decision becomes a rule — future matches resolve instantly.
- 5
Quality dashboards stay green — downstream reports and AI stay accurate
Continuous runtime holds the line. Reports agree. AI pilots succeed. Migrations run clean.
Human in the loop
One data steward, part-time, teaching the system on genuinely ambiguous cases.
Silent AI failure disappears
One canonical entity per real-world object. Every match is auditable — you can trace why any two records were merged. AI pilots succeed because they're grounded in clean data. Reports agree across departments. Migrations run clean because the source is trustworthy. Finance trusts the dashboard.
Yearly savings — with DEBCOR AI
+$172,380/yr
85% of your $202,800/yr labor pool redirected to real work.
Range: $152,100–$186,576/yr at 75–92% STP.
Directional example, not a quote. Fully-loaded rate assumes $65/hr — swap in your own if it's different. Freed capital = payroll cost the process stops burning, before package fees. Actual value depends on document mix and your straight-through target, set in the scope phase.
What's inside
Everything the package ships with.
Automated profiling of your master data — a quality heatmap by domain, out in week one
Dedup and fuzzy-matching agents that catch the same entity entered five different ways
Third-party enrichment against Clearbit, D&B, OpenCorporates, and government registries
A human-in-the-loop review portal for the ambiguous 10–20% — reviewer UI with match confidence
A continuous cleansing runtime — runs on a cadence, not one-shot, so quality holds over time
A governance layer with role-based access and full audit trail on every merge and enrichment
Sizing & published pricing
Three sizes. One architecture.
Scope compresses down, not features. S-tier is playbook-driven — a single senior engineer delivers against pre-built accelerators inside a fixed configuration boundary. M-tier extends the same systems into landscapes with multiple sources, custom rules, and real integration complexity. L-tier is the full enterprise engagement: dedicated environments, SOC 2 wrap, roadmap co-development, and — for SAP customers — clean-core BTP integration. Same architecture at every size.
Band
≤ 500 employees · one system, one team
Timeline
4–6 weeks
Published price
from $25k
Scope at this tier
One domain (customer OR vendor OR product), fixed source count, playbook-driven configuration by a single senior engineer.
Band
500–5,000 employees · multiple sources or rules
Timeline
6–10 weeks
Published price
$95–160k
Scope at this tier
3+ domains, multiple sources, custom match rules, third-party enrichment.
Band
5,000+ employees · enterprise scope, SOC 2, roadmap co-development
Timeline
10–14 weeks
Published price
from $275k
Scope at this tier
Full MDM stack, dedicated environments, SOC 2 wrap, continuous runtime with SLA, governance council.
Published bands are directional. Final commercial terms are per-client SOW. Managed operations are quoted separately.
Timeline
6–10 weeks from scope to live.
Every package follows the same four-phase delivery. What changes at each tier is the count of sources, rules, and integration surface — not the shape of the work.
Scope
Weeks 1–2
Profile your top 5 data domains. Produce a quality heatmap. Agree scope and match rules.
Build
Weeks 3–6
Deploy dedup, matching, and enrichment agents. Reviewer UI live for edge cases.
Validate
Week 7
Parallel run against a golden set — measure match precision, false-positive rate, review load.
Live
Weeks 8–10
Continuous cleansing runtime on your cloud. Governance handover + training.
For SAP customers · Bridge to SAP-native
The same cleansing engine that runs on your Salesforce, NetSuite, or homegrown DB today is the SAP AI Data Cleansing Tool inside our S/4HANA practice. If you move to SAP later, the agents, business rules, and enrichment sources migrate — no rebuild. See how DEBCOR extends this into SAP →
Package FAQ
Common questions.
Salesforce, NetSuite, HubSpot, Oracle EBS, homegrown Postgres/MySQL, S3 data lakes, and file drops. Multi-source is the default — the engine expects records to arrive from different places with different shapes.
The matching engine uses embeddings, not string similarity — so 'Acme, Inc.', 'ACME Corp', and other variants can be matched to the same entity if the underlying context supports it. Language-specific normalization is part of the scoping phase.
The runtime deploys on your cloud, in your region — data never leaves your environment. Enrichment against third-party sources is opt-in per domain, with configurable pass-through policies for GDPR-scoped fields.
Per-record inference cost is small through a tiered model architecture. A frontier reasoning model runs during scoping to derive the match rules and tune the embeddings against your data — a one-time cost. In production, most records match deterministically through the rules with no LLM call at all. Ambiguous matches use a high-efficiency low-cost model for pairwise comparison at fractions of a cent per pair. The frontier model is only re-engaged for genuinely novel entity types — rare after the first weeks. At production volumes, AI cost is a rounding error against the data-engineering time it saves.
This engine is the same architecture as the SAP AI Data Cleansing Tool inside our S/4HANA practice. Match rules, enrichment sources, and governance policies migrate directly if you move to SAP later — the engine goes with you, no rebuild.
Scope this package
Fixed scope, fixed price. Tell us the document types and the systems, and we'll come back with a one-page proposal.
Start scopingNot sure this is the one?
Book a 30-minute fit call with a senior architect. We'll tell you if this is the right package, a different one, or nothing at all.
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