Home → Blog

Modern supply chains run on promises. Whether you operate a TMS, a freight visibility tool, or an enterprise procurement workflow, your customers expect time-definite deliveries, predictable accessorials, and transparent penalties when things slip. But it’s nearly impossible to operationalize micro-SLA guarantees—such as “accept a tender within 5 minutes,” “dispatch in 2 hours,” “deliver by 10 a.m. local”—if your data inputs are scattered across separate systems for fuel prices, live freight rates, carrier verification, port congestion, and natural hazard alerts.

This post shows how to implement micro-SLA guarantees and penalty automation with a single API: FreightPulse. We’ll combine live US full-truckload (FTL) quotes, EIA fuel price data, IMF PortWatch–derived congestion metrics, FMCSA/FMC carrier lookups, and natural-disruption alerts behind one REST interface. Rather than bolting together five integrations and wrestling with different schemas, we’ll work with a unified JSON shape and consistent endpoints—giving developers, freight forwarders, 3PLs, and enterprise shippers a fast path from prototype to production.

You’ll learn how to:

Why FreightPulse

Developers need reliable, uniform logistics intelligence to wire directly into workflows and decision logic. FreightPulse removes the friction by consolidating:

Bottom line: micro-SLA and penalty automation require trustworthy, composable facts. FreightPulse brings those facts to you over one REST interface that’s fast to integrate, consistent to operate, and easy to observe.

Quick Start

All endpoints share the same base URL and response envelope. You’ll use standard HTTPS GETs, and responses include a success flag, a data object, and an optional error message when success is false.

Base URL: 

https://freightpulsehq.com/api/v1

Example: fetch national fuel prices to back a fuel-surcharge micro-SLA baseline. 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/fuel-prices"

Sample response: 

{
"success": true,
"data": {
"timestamp": "2026-07-30T12:00:00Z",
"source": "EIA (U.S. Energy Information Administration)",
"currency": "USD",
"unit": "gallon",
"data": {
"diesel": {
"national_average": 3.847,
"change_week": -0.023,
"change_percent": -0.6,
"regions": {
"east_coast": 3.912,
"midwest": 3.756,
"gulf_coast": 3.623,
"rocky_mountain": 3.891,
"west_coast": 4.456,
"california": 4.892
},
"historical": {
"30d_avg": 3.82,
"90d_avg": 3.75,
"yoy_change_percent": -8.2
}
},
"gasoline": {
"national_average": 3.156,
"historical": {
"30d_avg": 3.1,
"90d_avg": 3.05,
"yoy_change_percent": 1.1
}
}
}
}
}

Key fields:

Core Endpoints for Micro-SLA and Penalty Automation

1) Live FTL Freight Rates — back rate-to-market SLAs and automated overage penalties

Endpoint: 

GET /freight-rates

Key params:

Example request: 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/freight-rates?mode=trucking&origin_zip=10001&destination_zip=90210&pickup_date=2026-08-24&total_weight_lb=38000"

Sample response: 

{
"success": true,
"data": {
"mode": "trucking",
"service": "FTL (53' dry van)",
"source": "Warp",
"price_usd": 7207.92,
"transit_days": 4,
"pickup_date": "2026-08-24",
"delivery_date": "2026-08-28"
}
}

How to use:

2) Disruptions — dynamic SLA relaxation and waiver rules

Endpoint: 

GET /disruptions

Key params:

Example: fetch high-severity hazards near Los Angeles/Long Beach to decide whether to relax “must-pickup-by” micro-SLAs for drayage legs. 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/disruptions?port=USLAX&severity=high"

Sample response: 

{
"success": true,
"data": {
"results": [
{
"id": "gdacs:TC:1001305",
"type": "cyclone",
"severity": "moderate",
"title": "Tropical Cyclone SAUDEL-26",
"country": "Japan",
"starts_at": "2026-08-18T12:00:00+00:00",
"source": "GDACS"
}
]
}
}

How to use:

3) Port Congestion — schedule risk, appointment SLAs, and proactive exception handling

Endpoint: 

GET /port-congestion

Key params:

Important note: congestion is an approximation based on IMF PortWatch AIS data. It is a 7-day average of port calls compared to a 90-day baseline and lags roughly 6–7 days. It is not real-time AIS tracking and should be used for trend-aware SLA design rather than minute-by-minute dispatching.

Example: 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/port-congestion?port=USLAX"

Sample response: 

{
"success": true,
"data": {
"results": [
{
"port": "Los Angeles-Long Beach",
"locode": "US LAX",
"country": "United States",
"congestion": "moderate",
"as_of": "2026-08-14",
"metrics": {
"recent_avg_portcalls_per_day": 8.7,
"baseline_avg_portcalls_per_day": 10.2,
"ratio_vs_baseline": 0.85
}
}
]
}
}

How to use:

4) Carrier Lookups — automate compliance SLAs and blacklist logic

Endpoint: 

GET /carriers

Key params:

Example (trucking DOT): 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/carriers?q=54283"

Sample response: 

{
"success": true,
"data": {
"type": "trucking",
"query": "54283",
"results": [
{
"dot_number": 54283,
"legal_name": "SWIFT TRANSPORTATION COMPANY OF ARIZONA LLC",
"status": "active",
"safety_rating": "S",
"drivers": 12883,
"address": {
"city": "Phoenix",
"state": "AZ",
"country": "US"
}
}
]
}
}

How to use:

5) Fuel Prices — objective surcharge rules and SLA fairness

Endpoint: 

GET /fuel-prices

Use the national average, regional breakdown, and historical averages to define surcharge brackets. Your penalty engine can defer driver early-arrival pay or adjust accessorial expectations based on fuel volatility.

Example explained earlier in Quick Start. See the historical endpoint below for time series to build trend-aware rules and dashboards.

6) Historical — trend-aware SLAs and post-mortem evidence

Endpoint: 

GET /historical

Key params:

Example: 30-day diesel series for SLA trend analysis: 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/historical?metric=fuel_price_diesel_national&period=30d"

Sample response: 

{
"success": true,
"data": {
"metric": "fuel_price_diesel_national",
"period": "30d",
"series": [
{ "date": "2026-07-27", "value": 5.257 },
{ "date": "2026-08-03", "value": 5.343 }
]
}
}

How to use:

End-to-End Examples: Building Micro-SLAs and Penalty Automation

Example 1: “Rate-to-Market SLA” with automatic overage penalties

Problem: A broker or shipper wants every tender to be quoted within 60–120 seconds and priced within ±8% of the prevailing market. If the broker misses the time window or overprices, the system should assess a penalty or auto-reject.

Implementation:

Example 2: “Appointment SLA” with congestion-aware waivers

Problem: A forwarder schedules pickups at a congested port. They want to auto-waive late fees when port throughput is measurably below baseline, but only for lanes touching affected terminals.

Implementation:

Example 3: “Carrier Compliance SLA” for instant vetting at tender acceptance

Problem: Dispatchers must accept tenders only to active, safe carriers, or risk reassignments, missed pickups, and cascading penalties.

Implementation:

Comprehensive Endpoint Coverage, Fields, and Best Practices

GET /fuel-prices — field map and production tips

Business value:

Key fields:

Best practices:

GET /freight-rates — field map and production tips

Best practices:

GET /port-congestion — field map and production tips

Best practices:

GET /carriers — field map and production tips

Best practices:

GET /disruptions — field map and production tips

Best practices:

GET /historical — field map and production tips

Best practices:

Error Handling, Health, and Observability

Every response shares a uniform envelope. On failure: 

{
"success": false,
"error": "Human-readable message."
}

Recommended patterns:

Implementation Snippets: cURL, JavaScript, Python

JavaScript (Node.js) — Rate-to-market SLA gate 

import fetch from "node-fetch";

async function getFTLQuote(originZip, destZip, pickupDate) {
const url = new URL("https://freightpulsehq.com/api/v1/freight-rates");
url.searchParams.set("mode", "trucking");
url.searchParams.set("origin_zip", originZip);
url.searchParams.set("destination_zip", destZip);
if (pickupDate) url.searchParams.set("pickup_date", pickupDate);

const res = await fetch(url.toString(), {
headers: { "X-API-Key": process.env.FP_API_KEY }
});
const body = await res.json();
if (!body.success) throw new Error(body.error || "Unknown error");
return body.data;
}

async function shouldApplyOveragePenalty(contractRate, quote) {
const variance = Math.abs(quote.price_usd - contractRate) / contractRate;
return variance > 0.08; // 8% threshold
}

(async () => {
const quote = await getFTLQuote("10001", "90210", "2026-08-24");
const penalty = await shouldApplyOveragePenalty(6650, quote);
console.log({ quote, penaltyEligible: penalty });
})();

Python — Congestion-aware appointment window 

import os
import requests
from datetime import timedelta, datetime

def get_congestion(locode):
url = "https://freightpulsehq.com/api/v1/port-congestion"
res = requests.get(url, headers={"X-API-Key": os.getenv("FP_API_KEY")}, params={"port": locode})
data = res.json()
if not data.get("success"):
raise RuntimeError(data.get("error", "unknown error"))
return data["data"]["results"][0]

def appointment_buffer_hours(congestion_ratio):
if congestion_ratio < 0.80:
return 48
if congestion_ratio < 0.90:
return 24
return 0

payload = get_congestion("USLAX")
ratio = payload["metrics"]["ratio_vs_baseline"]
buffer = appointment_buffer_hours(ratio)
promised = datetime(2026, 8, 28, 10, 0, 0)
new_deadline = promised + timedelta(hours=buffer)
print({
"as_of": payload["as_of"],
"ratio_vs_baseline": ratio,
"buffer_hours": buffer,
"deadline": new_deadline.isoformat()
})

Real-World Use Cases

Fuel-surcharge calculator tied to micro-SLA credits

Use GET /fuel-prices and GET /historical to compute surcharge brackets and define a dynamic “fairness window.” If actual landed costs exceed your window for two consecutive weeks, auto-credit customers a percentage of accessorials on the next invoice. This binds cost volatility handling to transparent, rules-based adjustments instead of ad-hoc negotiations.

Port-congestion dashboard for a freight forwarder

Use GET /port-congestion for major terminals on your network and GET /historical with port_congestion:<portid> metrics to trend throughput changes. Color-code lanes, surface as_of dates prominently, and apply recommended “lead time” multipliers to bookings. When an exception occurs, your CS team can point to precise measurements used to set expectations.

Carrier vetting for a 3PL with real-time tender gating

Integrate GET /carriers to block dispatch and tender acceptance until status=active and safety_rating meets your thresholds. This preempts failed pickups and reduces “hot reassignments” that frequently trigger penalties and SLA breaches. For ocean drays, confirm NVOCC/OTI presence using type=ocean before lock-in.

Disruption alert system for shipment tracker

Use GET /disruptions with port filters to monitor hazards within a 300km radius of critical terminals; on high severity, trigger micro-SLA relaxations and email alerts. Capture event id and starts_at in your database and include them in automatic waiver memos attached to impacted shipments.

Tender-speed scoreboard for brokers

Combine GET /freight-rates (for context) with internal timestamps (tender_created_at, first_ack_at) to compute a “quote latency” KPI per broker. Enforce “ack in 90 seconds or auto-route to another team” as a micro-SLA, and apply penalties or spiffs based on sustained performance.

Putting It All Together: A Sample End-to-End Flow

Consider a door-to-door shipment touching USLAX:

  1. Tender received → call GET /carriers to verify the selected carrier is active and safe. If not, auto-reject and re-route; else, start the “ack in 90 seconds” micro-SLA timer.
  2. On ack → call GET /freight-rates to fetch a market quote for the lane. Compare to contract. If variance > 8% and no valid disruption exists, mark penalty eligibility.
  3. Call GET /port-congestion for USLAX to determine appointment buffers; if ratio_vs_baseline < 0.9, extend windows and record as_of + metrics.
  4. Call GET /disruptions with port=USLAX; if severity is high, extend windows further and tag any demurrage/late fees for auto-waiver.
  5. Call GET /fuel-prices and GET /historical to finalize surcharge bracket; snapshot this evidence with the shipment’s SLA ledger.
  6. At billing time → compute any penalties or credits, including late-ack, rate overage, and waived demurrage, with links to the captured JSON inputs.

Additional cURL Examples and Complete JSON Samples

Carrier lookup by name (ocean) with structured result handling 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/carriers?type=ocean&q=MAERSK"

Illustrative response (ocean directory fields vary by source; ocean returns follow the same envelope and will indicate the OTI/NVOCC registration context): 

{
"success": true,
"data": {
"type": "ocean",
"query": "MAERSK",
"results": [
{
"name": "MAERSK LINE A/S",
"status": "active",
"org_number": "OTI-XXXXXXX",
"address": {
"city": "Copenhagen",
"country": "DK"
}
}
]
}
}

Use this to confirm presence in the relevant directory before applying aggressive transload SLAs.

Historical port congestion series (trend charting) 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/historical?metric=port_congestion:USLAX&period=90d"

Illustrative response: 

{
"success": true,
"data": {
"metric": "port_congestion:USLAX",
"period": "90d",
"series": [
{ "date": "2026-06-01", "value": 0.96 },
{ "date": "2026-06-08", "value": 0.93 },
{ "date": "2026-06-15", "value": 0.88 }
]
}
}

This time series is ideal for anchoring pre-booking lead times and dynamic appointment SLAs.

Error demonstration: Missing required parameter 

curl -H "X-API-Key: YOUR_API_KEY" \
"https://freightpulsehq.com/api/v1/carriers"

Response: 

{
"success": false,
"error": "Missing required parameter: q"
}

Handle this by validating inputs before API calls; log and surface actionable error messages to developers and operators.

Governance, Routing, and Performance Practices

Micro-SLA engines run hot and continuous. Keep them stable with a few engineering guardrails:

FAQ

How often do FreightPulse fuel prices update?

Fuel prices are sourced from EIA and reflected in endpoints with a timestamp you can persist. Use the timestamp and the historical series to ensure you apply consistent surcharge logic and can reproduce decisions later.

Can I look up ocean carriers as well as trucking carriers?

Yes. Use GET /carriers with type=ocean and a name or organization number to check ocean carrier directory presence. For trucking, type=trucking supports DOT or name lookups with FMCSA-derived fields like status and safety rating.

Does the port congestion data reflect real-time vessel wait times?

No. Congestion reflects a 7-day average vs a 90-day baseline using IMF PortWatch AIS data and typically lags 6–7 days. It’s designed for trend-aware SLA adjustments, not real-time berth or yard management.

Can I filter disruptions to events near a specific port?

Yes. Use the port parameter on GET /disruptions to filter by radius around a port. Then use type and severity to tie policy-driven SLA relaxations or waivers to the specific hazard levels that matter to your operations.

How do I justify penalties or waivers to customers later?

Persist the exact JSON responses used for decisions (fuel prices, freight rates, congestion, disruptions, and carrier checks) with your shipment records. Include timestamps and IDs like disruption.id or port-congestion.as_of so every penalty or waiver has auditable evidence.

Conclusion + CTA

Micro-SLA guarantees and penalty automation work only when your data strategy is unified, reliable, and defensible. Stitching together separate EIA, FMCSA, AIS-derived congestion, freight quote providers, and hazard alerts creates schema friction, error surfaces, and slow operational change. FreightPulse consolidates the essentials—fuel, rates, carriers, port congestion, and disruptions—behind one REST interface with a consistent data shape, making it straightforward to implement, tune, and prove your SLA logic.

If you are building a supply chain SaaS platform, operating a 3PL, or running enterprise procurement, you can move from whiteboard to production quickly: start by wiring the endpoints above into your tender and dispatch workflows, attach evidence to every decision, and let policy tables do the hard work. With a single integration, your teams can commit to aggressive micro-SLAs, dynamically adapt to real-world conditions, and automate penalties or waivers with confidence.

Ready to build? Explore the endpoints, test real lanes, and start attaching evidence to your SLA ledger today. Try FreightPulse for free and get from prototype to production with less code and more certainty.