We normalize and enrich CRM, marketing, and billing data for mid-market and PE-backed organizations at $30M-$500M ARR by deduplicating records, standardizing formats, and filling in enrichment gaps so every system is working from the same clean truth.
One clean record in [HubSpot|] We handle the entire cleanup. Working inside the systems you run.
Four teams, four versions of the same account.
Nobody can give you a confident customer count, because nobody's sure how many of those “accounts” are actually the same company typed in four different ways. Every ARR number built on top of that count inherits the uncertainty.
→ Customer count unreliableBefore any report or campaign list goes out, someone quietly fixes formatting, merges obvious duplicates, and fills a few blank fields by hand, every single time, because nothing upstream prevents the mess.
→ Manual rework every timeAn account tagged “Technology” in one field and “Software” in another gets excluded from a campaign that should have included it, not because the targeting logic is wrong, but because the data feeding it isn't consistent.
→ Targeting misses accountsDuplicate records mean two people calling the same company, stepping on each other's outreach, and confusing a prospect who's now getting two different pitches from the same vendor.
→ Reps collide on accountsEverything arriving from forms, lists, imports and integrations lands in one intake lane before it touches your CRM.
A documented ruleset checks format, completeness and plausibility the moment a record is created, not months later.
Fuzzy matching tuned to your data finds the duplicates exact-match rules leave behind, and every borderline merge is reviewed before it lands.
Naming, phone, address and industry values are forced to a single documented format, so new records can't reintroduce the mess.
Firmographic, technographic and contact gaps get filled and cross-checked against what you already hold rather than overwritten blindly.
Most data-quality work stalls at stage two. The difference between a cleanup and a standard is whether new records are forced to meet it.
The same company exists as three separate records.
Duplicate records get matched and merged automatically, so every team is looking at the same account instead of three fragments of it.
Phone numbers, addresses, and company names are formatted a dozen different ways.
Formatting rules apply consistently across every record, so exports and reports don't need an hour of manual cleanup before anyone can use them.
Missing firmographic data leaves segmentation and scoring incomplete.
Enrichment fills in missing company size, industry, and revenue data, so targeting and scoring models have what they actually need.
A data quality assessment quantifying duplication, inconsistency, and missing fields.
Before we clean anything, we measure how bad it actually is. We inventory your CRM and connected systems to quantify duplication rate, formatting inconsistency, and enrichment gaps across every core object, accounts, contacts, and deals.
Every source, every tool, every report resolves to the same normalized, deduplicated, enriched record.
We build standardization and matching logic directly into the CRM you already run, so clean data is the default, not a periodic cleanup project.
Missing firmographic and contact data gets filled from validated external sources, matched against your existing records rather than overwriting them blindly.
Used to identify fuzzy-match duplicates and inconsistent formatting at scale, catching the “Acme Corp vs. Acme Corporation” cases that exact-match rules miss entirely.
Ingestion, orchestration and transformation wired end to end. Fivetran and Kafka into Airflow, dbt models into BigQuery and Snowflake, Cloudflare Workers at the edge, on EC2, Lambda and infrastructure-as-code underneath.
A quantified picture of duplication, inconsistency, and missing fields across your core objects.
Documented formatting and validation standards for every core field.
Merged, standardized, and enriched records, not a report recommending you do it.
Ownership, review cadence, and maintenance rules so data quality holds after we leave.
EnviroKlenz · Air & Water Purification
Leads landed in HubSpot with no automation behind them. Email deliverability was weak, aliases confused prospects, and scoring, deal progression and renewals were tracked by hand, so leadership had no reliable view of velocity or forecast. We ran a series of audits, defined the pipeline structure, wired 8x8, Microsoft Teams and LinkedIn Sales Navigator into the CRM, then automated qualification, onboarding, renewals and approvals on top of it.
Two pipelines defined and instrumented end to end
8x8, Microsoft Teams and Sales Navigator wired into HubSpot
Forecast, velocity, deal aging and loss reason reporting
Companies without a CRM or core system already in place. We normalize and enrich existing data, we don't stand up your CRM from nothing.
Teams unwilling to enforce standards at entry going forward. A one-time cleanup with no ongoing enforcement drifts back within a couple of quarters.
Businesses looking for enrichment alone with no interest in fixing underlying duplication and formatting first. Enrichment on top of messy data just enriches the mess.
A dedup tool flags what matches exactly, it doesn't decide whether “Acme Corp” and “Acme Corporation” are the same account, and it doesn't build the ongoing rules that keep new records clean. We combine matching logic with governance, so the cleanup doesn't quietly undo itself in six months.
Enrichment fills gaps, it does not fix duplicate records or inconsistent formatting underneath them. Enriching a messy database just makes a bigger, messier database. We fix the foundation first, so enrichment actually sticks.
No. Cleanup is a moment in time. We build the standard, the matching logic, and the pipeline that keeps new data meeting that standard permanently, so you're not paying for the same cleanup again next year.
A 30-minute audit call. We'll look at your current CRM data and show you exactly where the duplication, formatting inconsistency, and enrichment gaps are hiding. No deck.
The questions RevOps, Data, and Marketing Ops leaders ask us on the first call, answered directly.
No. In most cases we work within your existing CRM, building standardization and matching logic directly into it. A platform switch is only recommended if your current system genuinely can't support the validation rules needed.
Matching logic is tuned and tested against your actual data before any merge happens automatically. Borderline matches get flagged for review rather than merged blindly, so you don't lose a legitimately separate account by mistake.
We work with validated third-party enrichment sources appropriate to your industry and region, and cross-check new data against what you already have rather than overwriting existing accurate fields.
Cleanup and merges are staged and validated before going live, not applied blind to production data. We sequence the rollout so live reporting and campaigns aren't disrupted mid-cycle.
Yes. Many clients move into an ongoing RevOps retainer to maintain standards and refine matching logic as the business and data sources evolve.
The assessment and initial cleanup typically take 6-8 weeks. The ongoing validation pipeline and governance close out over the following weeks, depending on data volume and system complexity.
