Data Done Right: Simplifying 3PL Operations with Embedded Precision
As a fast-scaling third-party logistics (3PL) platform, our client provides fulfillment, warehousing, and cost-optimization services to brands managing logistics across multiple vendors.With rapid growth came operational friction—fragmented data, tool instability, and lack of visibility—impacting both productivity and scalability.
Success Highlights
- 4 core systems validated daily to prevent downtime
- 100+ engineering hours freed per month
- 20+ brands supported with real-time UAT and QA coverage
Key Details
-
Industry: Logistics / Supply Chain / SaaS
-
Geographies: US
-
Platform: Airtable-driven ops system
Business Challenge
As the client scaled rapidly, operational inefficiencies surfaced — from fragmented invoice data to unreliable tool performance and bandwidth constraints across teams.
-
Fragmented Invoice Sources: Data arrived via email, Dropbox, and spreadsheets—each with its own format—creating constant inconsistencies.
-
Lean Internal Team: The small ops team couldn’t manage growing data loads, risking delays and reporting errors.
-
Tool Downtime: Frequent issues across Airtable, Metabase, and internal dashboards affected daily workflows and visibility.
-
Low Insight into Data Quality: Without a structured QA process, recurring data issues went unnoticed and unresolved.
-
Impact on Growth Focus: Core team members were pulled into ops firefighting instead of driving engineering, innovation, or client expansion.
Our Solution Approach
We embedded a QA and ops support team to streamline workflows, standardize invoice processing, and ensure all tools ran smoothly—every single day.
Identify Data Gaps & Workflow Bottlenecks
Mapped tool dependencies and pinpointed friction in invoice ingestion, system syncing, and reporting flows.
Normalize Data Across Brands & Sources
Extracted, cleaned, and structured invoice data across formats—feeding accurate info into Airtable, Metabase, and monday.com.
Implement Daily QA for Tools & Dashboards
Performed UAT across freight, SKU, and pick-pack logic. Ensured dashboard metrics aligned with Airtable records.
Real-Time Communication & Feedback Loop
Acted as embedded team members—flagging blockers, UX issues, and anomalies daily via Slack and shared trackers.
Technical Highlights
- Automated validation of invoice fields across structured and unstructured formats
- Airtable schema integrity checks via scripted QA flows Query-level data validation within Metabase dashboards
- API-driven monday.com sync for task and metadata updates
- Custom scripts for file renaming, categorization, and timestamped storage UAT test scripts for SKU-level, freight, and pick-pack logic verification IAM policy review and governance setup Scheduled cross-system audits between Airtable and reporting dashboards
def validate_invoice_data(invoice_file):
data = extract_fields(invoice_file)
normalized = normalize_format(data)
if is_valid(normalized):
update_airtable(normalized)
log_to_tracker(invoice_file)
return "Invoice Processed"
else:
flag_error(invoice_file)
notify_team()
return "Validation Failed"Business Outcomes
Our embedded solution enabled the client to scale operations and reclaim strategic focus without sacrificing accuracy or visibility.
core systems validated daily to prevent tool failures and mid-day disruptions.
engineering work hours freed monthly for higher-value platform development.
brands supported seamlessly with accurate data ingestion and real-time QA coverage.
- Improved cross-functional visibility
- Enabled faster decision-making
- Ensured platform reliability