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freight pricing AI automation

Price Smarter. Win More. Lose Less Margin.

AI-powered freight pricing intelligence — dynamic rate optimization, market benchmarking, and margin protection for sea and air freight forwarders.

AI that analyzes market rates, optimizes your pricing, and gives your sales team real-time intelligence — so you win more deals at better margins.

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Built For

Who This Is For

  • Freight forwarders whose pricing decisions are based on gut feel, outdated spreadsheets, or "what we charged last time"
  • Pricing teams managing 500+ active rate cards across multiple trade lanes
  • Companies losing margin because sales reps under-price to win deals or over-price and lose them
  • Forwarders on volatile trade lanes (Asia-Europe, Trans-Pacific) who need real-time market awareness

Before CargoIQ

You are either leaving margin on the table or losing deals you should have won

Freight pricing is one of the highest-leverage decisions in forwarding, yet most companies treat it as an afterthought. Rate data lives in disconnected spreadsheets, carrier portals, and individual reps' heads. Pricing decisions are made based on "what we charged this customer last time" or "what feels competitive" rather than real-time market intelligence. The result: you over-price stable lanes and lose volume to hungrier competitors, or you under-price volatile lanes and give away margin during surges. Neither outcome is visible until the P&L lands — by then, the damage is done. Meanwhile, your pricing team spends most of their time on data gathering and rate maintenance rather than strategic analysis.

Rate data scattered across 5–15 carrier portals, internal spreadsheets, rate management systems, and email confirmations — no single source of truth

Pricing decisions lag market movements by days or weeks because manual rate analysis cannot keep pace with volatile markets

No visibility into whether you are pricing competitively on specific lanes until you see win/loss patterns months later

Sales reps negotiate ad-hoc discounts without understanding the margin impact or how the lane is performing overall

Contract rate reviews happen annually or semi-annually, leaving 6–12 months of market shifts unaddressed

Cannot answer basic questions like "what is our margin on Asia-Europe FCL this month?" without a manual data pull that takes days

What We Build

Capabilities

1

Real-time market rate benchmarking

Integrates with Xeneta, Freightos Baltic Index, carrier spot rate APIs, and your own historical data to provide real-time visibility into where the market is on any lane. Benchmarks your current pricing against market rates, contracted rates, and competitor positioning (inferred from win/loss patterns) — showing you exactly where you are over- or under-priced.

2

Dynamic pricing optimization by lane, customer, and volume

Replaces flat markups with intelligent pricing that considers lane competitiveness, customer lifetime value, volume commitments, market direction (rates trending up or down), and your capacity position. The system recommends optimal pricing for each quote that balances win probability against margin targets — not just the lowest price that wins, but the highest price that still wins.

3

Margin analysis and protection alerts

Real-time margin visibility across your book of business — by lane, customer, mode, and time period. Automated alerts when margins drop below thresholds (globally or per lane), when carrier rate changes make your quotes uncompetitive or under-priced, or when a customer's volume-to-margin ratio deteriorates. This replaces the "surprise" in your monthly P&L with proactive intervention.

4

Carrier rate comparison and normalization

Automatically aggregates rates from all your carrier sources, normalizes them to a comparable format (handling different surcharge structures, currency, and validity periods), and presents apples-to-apples comparisons. Includes total cost analysis that factors in carrier reliability, transit time, and your historical claims rate — because the cheapest rate isn't always the cheapest shipment.

5

Historical trend analysis and rate forecasting

Analyzes 12–36 months of your historical rate data alongside market indices to identify seasonal patterns, lane-specific trends, and carrier pricing behavior. The forecasting model provides 30/60/90-day rate direction estimates with confidence intervals — helping your pricing team make proactive decisions rather than reactive adjustments.

6

Rate management system integration

Connects to your existing rate management systems (CargoWise rate module, Cargobase, Freightos, custom databases) to both consume rate data and push optimized pricing back. This means pricing recommendations aren't just reports — they can directly update your quoting systems so sales reps always work from current, optimized rates.

In Practice

Real-World Use Cases

Proactive repricing during a rate surge

Spot rates on Asia-North Europe jump 35% over two weeks due to capacity constraints. The system detects the shift in real-time via carrier API feeds and market indices, identifies all your pending quotes and active contracts on affected lanes, and alerts the pricing team with specific repricing recommendations. Instead of discovering the margin erosion in next month's P&L, you adjust within 24 hours.

Customer-specific pricing strategy

A key account is up for contract renewal. The system provides a complete analysis: historical volume and margin by lane, competitive positioning based on market rates, the customer's price sensitivity inferred from win/loss patterns on past quotes, and recommended rate cards that protect margin while maintaining competitiveness. Your pricing team walks into the negotiation with data, not guesswork.

Lane profitability analysis for strategic decisions

Management wants to know which trade lanes are most profitable and which are margin-negative. The system produces an analysis within minutes — total margin by lane, margin trend direction, volume stability, competitive intensity, and recommendations for where to invest sales effort versus where to raise prices or deprioritize.

Implementation

How We Deploy It

Timeline: 8–12 weeks from kickoff to production

1

Weeks 1–2: Data audit — catalog rate sources, historical data availability, current pricing logic, and margin reporting

2

Weeks 3–5: Rate aggregation pipeline, market data integration, historical analysis model training

3

Weeks 6–9: Dynamic pricing engine, margin monitoring dashboards, alert configuration, rate management system integration

4

Weeks 10–12: UAT with pricing team, backtesting pricing recommendations against historical outcomes, production deploy

Results

Real Numbers from Production Systems

12%

Average margin improvement

Achieved by replacing flat markups with dynamic pricing that optimizes for each lane-customer combination

25%

Better win rates on target lanes

Competitive pricing intelligence ensures quotes are priced to win where you want to grow volume

60%

Faster rate analysis

Pricing team spends 60% less time on data gathering and manual comparison, focusing on strategic analysis

0

Below-cost quotes sent

Floor pricing rules and real-time margin checks prevent any quote from going out below your cost basis

Tech Stack: PythonMachine LearningAzuren8nOpenAI GPT-4oPostgreSQLXeneta API
Integrations: Xeneta (market rate benchmarking)Freightos Baltic Index (FBX)CargoWise Rate ModuleCargobaseWebCargo by FreightosCarrier-specific rate APIsSalesforce / HubSpot (win/loss data)Internal rate databases via API/SFTP

Works with your existing TMS

Direct integration with CargoWise, SAP TM, Oracle TMS, Microsoft Dynamics, and Descartes.

View Integrations

Frequently Asked Questions

How does freight pricing AI work?
It ingests data from three categories: your internal data (historical rates, booking data, win/loss records, carrier contracts), market data (Xeneta, Freightos Baltic Index, carrier spot rate APIs), and operational data (carrier reliability scores, transit time performance, claims history). It then builds a pricing model for each lane-customer combination that recommends the optimal price point — the price that maximizes your expected margin by balancing win probability against markup. The model continuously learns from new data, so pricing recommendations improve as it sees more of your quoting outcomes.
Can it predict rate changes?
It provides directional forecasts with confidence intervals, not point predictions — because no one can predict exact freight rates. The system analyzes seasonal patterns in your historical data, correlates them with market indices and known schedule changes (GRIs, peak season surcharges, blank sailings), and produces 30/60/90-day rate direction estimates. Think of it as "rates on this lane are likely to increase 10–15% over the next 60 days based on historical patterns and current indicators" rather than "the rate will be $2,340 on March 15."
Does it work for both sea and air freight?
Yes. The pricing models are built for each mode with mode-specific factors. Sea freight: container type premiums, surcharge structures (BAF, CAF, THC, ISPS), GRI timing, equipment positioning costs, and seasonal patterns by trade lane. Air freight: weight break optimization, dimensional pricing, fuel surcharge indices, ULD rates versus per-kilo, charter versus commercial capacity, and airline-specific pricing behavior. The system handles multi-modal shipments where both sea and air legs need to be priced as part of a single quote.
How does it handle contract vs spot rates?
Separately and in relation to each other. For contract rates, the system monitors whether your contracted rates are still competitive versus the spot market — alerting you when spot rates drop below your contract (so you can negotiate or shift volume) or when spot rates spike (so you protect your contract allocation). For spot pricing, the system provides real-time market positioning and recommends pricing based on current conditions. It also identifies "contract leakage" — where customers on contract rates are spot-buying through you at higher rates, indicating contract utilization issues.
What data does it need to get started?
At minimum: 12 months of historical booking data (origin, destination, carrier, rates, surcharges, volume), carrier contract rates, and access to at least one market rate source. The more data you provide, the better the recommendations — 24–36 months of history with win/loss data and customer segmentation produces the most accurate pricing models. We can work with data from your TMS exports, rate management system, or even structured spreadsheets. The discovery phase identifies exactly what data is available and builds the ingestion pipeline accordingly.
How is this different from a rate management system?
A rate management system stores and retrieves rates — it tells you what a carrier charges. Freight Pricing AI tells you what you should charge. It adds the intelligence layer: market positioning, dynamic margin optimization, win probability modeling, trend forecasting, and proactive repricing alerts. Think of your rate management system as the data source and the pricing AI as the decision engine that sits on top of it. They complement each other — we integrate with your existing rate management system, not replace it.

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