Guide
How to plan a data collection project
Sources, fields, refresh rate, delivery and permitted use, agreed before anyone builds anything.
Read the playbook: How to plan a data collection projectInsights
Playbooks, calculators, and field notes
The practical work behind the services, what we've learned building data and AI capabilities inside real operations.
Articles
Latest from Insights
Data Foundations
Managed Data Service vs In-House Team
Compare managed data services and internal teams by strategic control, specialist workload, maintenance, quality ownership and long-term operating fit.
2 min read
Automation & Governance
Document Automation With Human Review
Design document automation with field validation, exception reasons, approval gates, audit records and clear human ownership for uncertain cases.
2 min read
Trade & ProcurementSupplier Monitoring Data: From Signals to Action
Design supplier monitoring around approved internal and external signals, evidence, ownership and actions instead of an unexplained risk score.
2 min read
Margin & PricingRebate Leakage: Agreements, Accruals and Claims
Connect rebate agreements, eligible transactions, accruals, claims, credits and settlements to identify exceptions for human validation.
2 min read
Margin & PricingHow to Run a Price-to-Invoice Audit
Compare approved prices, invoice lines, discounts, credits and customer terms to find reviewable commercial exceptions without claiming recovery.
2 min read
Trade & ProcurementLanded Cost Variance Analysis: Plan vs Actual
Reconcile planned and actual landed cost by goods value, FX, freight, duty, insurance, handling and local transport with traceable ownership.
2 min read
Trade & ProcurementHow HS Codes Affect Landed Cost
Learn how product classification affects duty calculations, evidence, review workflows and landed-cost scenarios while keeping approval with trade experts.
2 min read
Trade & ProcurementCIF vs FOB: What Changes in Landed Cost
Understand how CIF and FOB affect quoted responsibility and landed-cost inputs without treating an Incoterm as the full delivered cost.
2 min read
AI & Model DataHow to Evaluate an LLM Application Before Launch
Build an LLM evaluation plan covering representative cases, answer quality, safety, refusal, retrieval, human review and regression testing.
2 min read
AI & Model DataAI Training Data Quality Framework
A practical framework for AI data collection, labeling, reviewer calibration, disagreement handling, validation, versioning and acceptance.
2 min read
Margin & PricingProduct Matching for Reliable Price Monitoring
Learn how identifiers, variants, pack sizes, attributes and manual review make competitor price comparisons more reliable and explainable.
2 min read
Data FoundationsManaged Web Data Service vs Scraping API
Compare managed web data services, scraping APIs, proxy infrastructure and internal teams by ownership, maintenance, quality and delivery.
2 min read
AI & Model DataTesting an AI Assistant Before It Talks to Customers
A pre-launch testing programme for AI assistants: real-user eval sets, refusal testing, red-teaming, drift baselines and the escalation path.
6 min read
Margin & PricingCompetitor Price Tracking That Holds Up: Coverage, Match Rate and Alert Speed
Competitor price tracking fails on three numbers buyers rarely check: coverage, match rate and alert speed. Here's how to test a vendor's claim on each.
5 min read
Data FoundationsWhy Your Dashboards Disagree: Fixing KPI Drift Before You Add AI
Why dashboards show different numbers explained: metric definitions, timezone cut-offs, currency and join failures, and how to fix KPI drift before adding AI.
6 min read
AI & Model DataHow to Evaluate an AI Training Data Vendor: 12 Questions That Reveal Everything
How to evaluate an AI training data vendor with 12 direct questions, good and bad answers for each, and a table of annotation quality metrics to check.
6 min read
Automation & GovernanceChoosing Your First Workflow to Automate: A Scoring Method That Beats Gut Feel
A six-factor scoring rubric for deciding which workflow to automate first, with worked examples and how to design the exception path.
6 min read
Trade & ProcurementTotal Landed Cost: What Most Importers Leave Out of the Calculation
Total landed cost explained properly: the components most models miss, why HS classification is a data problem, and how to price before the PO.
6 min read
Margin & PricingFinding Profit Leakage in Contracts and Rebates: Nine Places Margin Disappears
Profit leakage rarely shows up as one big problem. See the nine places margin hides in contracts and rebates, and how to detect each one with data you already have.
6 min read
AI & Model DataWhat “Model-Ready Data” Actually Means — And Why Most Datasets Aren't
Model-ready data is a measurable standard, not a synonym for clean. Four criteria, worked examples, and why volume alone won't fix a fine-tune.
7 min read
Data FoundationsHow to Plan a Data Collection Project (Without Rebuilding It in Six Months)
Learn how to plan a data collection project properly: scope, freshness, ownership and quality criteria, so it survives past launch instead of needing a rebuild.
5 min read
Playbooks
Field-tested guides
Guide
What model-ready data actually means
Cleaning, labeling, review, splits and documentation your model team can rely on.
Read the playbook: What model-ready data actually meansGuide
Choosing your first workflow to automate
How to pick a repeated, measurable process with a clear owner and real numbers.
Read the playbook: Choosing your first workflow to automateGuide
Finding profit leakage in contracts and rebates
Where discounts, rebates, claims and billing quietly reduce profit, and how to check.
Read the playbook: Finding profit leakage in contracts and rebatesTools
Free diagnostics and calculators
Data and AI check
A ten-question check that scores how ready your data, workflows and controls are.
Open the data and ai check
Landed cost calculator
Work out duty, freight and total landed cost per unit before you buy or ship.
Open the landed cost calculator
Profit leakage estimator
Size the margin lost to pricing, rebate, contract and billing errors.
Open the profit leakage estimator
Guides
Concepts, explained
Case notes
How the work is applied
A global industrial manufacturer
Cut purchasing cost surprises and document work across plants
One trusted view of spend, supplier status and open commitments across plants.
Read case study
A North American wholesale distributor
Recover margin lost in rebates, billing errors and price inconsistencies
Margin leakage identified by cause and customer, not just a variance line.
Read case study
A multi-channel retail brand
React to market moves faster with clean product, price and seller data
Price and promotion changes surfaced the same day instead of days later.
Read case study
Client details are anonymized at the clients' request. No unsupported performance figures are presented.
Next step
Discuss your use case
Bring one pain point, a data source, a workflow, a margin question. We'll come back with a focused assessment and a clear ROI hypothesis.