Services
Data hygiene + process hygiene = business intelligence you can trust. That’s the premise behind everything below. Data hygiene is the state of your records — product masters that agree across systems, transactions that reconcile, fields that mean what they say. Process hygiene is what keeps them that way — the handoffs, EDI habits, item-setup routines, and weekly checks that produce the data in the first place. Fix the first without the second and you’re clean for a quarter.
Every engagement starts the same way: I look at the data, figure out what’s wrong, quantify the downstream impact, and deliver something your team can act on. The specifics depend on the problem.
Start Small: the Data Health Snapshot
The typical first engagement, sized for a first-time buyer. You send one dataset — product master export, twelve months of remittances, or a transaction extract. Seven business days later you have written findings: every defect class found, its annualized cost, and a top-10 fix list your team can start on immediately — seven business days end to end.
Fixed fee: $950. Credited in full toward a scoped audit within 60 days.
Start with the Snapshot →Data Hygiene & Validation
Most specialty food brands discover their data quality problems through chargebacks — automatic deductions triggered by mismatched case dimensions, missing GTIN-14 assignments, or serving size declarations that conflict with the nutritional panel. By then, the cost has been compounding for months.
A Lailara audit works backward from the financial impact. I trace every chargeback, reconciliation failure, and rejected submission to the specific field that caused it — across whatever systems and data sources are involved. The deliverable is a written report with the annualized cost of each defect and a prioritized remediation plan your team can execute without my involvement.
See it proved: Product Data Health Audit · Retail Readiness & Launch
Decision Frameworks & Analytics
A dashboard nobody opens is a waste of money. I build tools that answer a specific question for a specific person — the CEO who needs to know which SKUs to cut, the ops manager who needs Monday’s velocity view, the CFO who needs to see where trade spend is leaking.
Velocity analysis. Distribution strategy. SKU rationalization. Launch trajectory diagnostics. Pricing power assessment. Promo ROI. Each tool is built for the decision it serves — an interactive app for the analyst, a one-page tearsheet for the board meeting, a triage workbook for the person fixing the data.
See it proved: SKU Portfolio Audit · Channel Profitability & Capital Allocation
For ongoing competitive visibility, see Competitive Shelf Intelligence →
Data Quality Engineering
Validation that runs before the data enters your system, not after it breaks something downstream. I build audit scripts and automated quality scoring that catches mixed formats, phantom duplicates, placeholder floods, and the silent type coercion that pandas applies without telling anyone.
This includes GS1 and GTIN validation with format rules for Walmart, Costco, and Whole Foods; distributors UNFI and KeHE; and 1WorldSync GDSN syndication. FSMA Rule 204 compliance analysis, GS1 Sunrise 2027 readiness checks, and custom exception-handling protocols that give your team a clear path from defect to resolution.
See it proved: Product Data Health Audit · Data Hygiene Auditor · datascope
See the engagement: Validation Pipeline Build →
Systems & Infrastructure
The audit pipeline that runs every week without someone remembering to trigger it. The validation layer that catches defects before they enter the ERP. The reporting infrastructure that rebuilds itself when the source data updates. I build the tooling — Python CLI tools, automated scoring scripts, reproducible R pipelines, orchestrated workflows — so that data quality becomes a system property, not a manual process.
This is the work that makes everything else sustainable. An audit is a one-time finding. Infrastructure is the reason the finding stays fixed.
See it proved: Trade Spend & Deduction Recovery · Fulfillment & OTIF Diagnostic
Solution Architecture
Translating ambiguous business needs into clear technical specifications your team can implement. Requirements analysis, stakeholder alignment, and feasibility assessment — grounded in two decades of building and operating the systems, not just specifying them.
I’ve been on both sides: designing the system and operating it when something breaks at 10 p.m. on a Friday. That dual perspective — architect and operator — shapes every specification I write.
What You Get
Every engagement starts with a written intake, moves to a written scope of work, and ends with a custom solution built for your specific problem. The deliverable is yours — a written report, an interactive tool, a triage workbook, a validation pipeline, whatever the scope calls for. No hourly billing, no timesheets.
I use Claude Code as a pair programmer for audit scripts and data quality pipelines, which is part of why flat-fee pricing works for both sides.
Monthly retainers include ongoing operational support — competitive shelf monitoring (pricing, promo activity, and out-of-stock tracking across your category), recurring data quality sweeps, and decision-framework maintenance as your retailer mix evolves. From there, ongoing work is an option — not an expectation. This is now a defined engagement — see Competitive Shelf Intelligence.
Common Questions
What types of data does Lailara LLC audit?
Transaction and purchase data, incentive and rebate payment data, product master records (UPC/GTIN, GDSN, 1WorldSync), and operational and fulfillment data. We trace every downstream failure back to the specific field that caused it.
How long does a typical data audit take?
A scoped audit typically takes 4–6 weeks. It includes a written deliverable with findings, quantified financial impact, and a prioritized remediation plan your team can execute independently.
What engagement models are available?
Three, and they work as a sequence rather than a menu. Recover — a scoped audit (4–6 weeks, fixed fee) finds the problems and prices them. Prevent — a project SOW with defined scope and timeline builds the validation layer so they stop recurring. Preserve — a monthly retainer (deliverables-based, no hourly billing) keeps the data and the process clean as your retailer mix changes. Most brands start at Recover. Where you stop is up to you. All engagements are outcomes-based.
What industries does Lailara LLC serve?
Primarily incentive fulfillment, rebate processing, product data syndication, retail supply chain, and specialty food and CPG companies from roughly $3M to $50M in revenue. Every stage gets a different engagement: emerging brands are building their first data discipline; established ones are untangling scale. The work is scoped to the stage, not the logo.
Start with the data problem.
Tell me what’s broken in the form; I’ll tell you where the impact is — in writing, no call.