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No unified inquiry inbox — same inquiry arrives across email, WhatsApp, and phone, with no way to de-duplicate or track what's been actionedRate access depends entirely on pricing team availability — if pricing is busy, the customer waits, and the window to win quietly closesMargin decisions made on gut and memory — no reference for what was applied last time, what the customer's price sensitivity is, or what the minimum floor isEvery quote built from scratch — no templates, no version history, no record of what was sent to which customer on which laneLoss reasons are never captured — when a customer says no, there's no way to log why, so the same pricing mistakes repeat on the same lanes indefinitelyNo priority signal on incoming inquiries, a re-quote for a deal closing today looks identical to a speculative inquiry for next monthRate discovery is a portal-hopping exercise; each carrier has a different login, UI, and format, all manually searched and compiled for every inquiryContracted rate sheets are static, validity dates expire silently, and stale rates are only discovered after a shipment is already booked at the wrong costRe-quotes carry no memory. When sales asks for a better rate, the pricing exec starts from scratch with no record of what was already triedNo win/loss feedback, the pricing team never learns whether their rates were competitive or not, so there's no data to improve lane strategy over timeNo real-time pipeline visibility — inquiries received, quoted, pending, and gone cold are impossible to know without manually asking the teamQuote TAT is invisible — no data on how long inquiry-to-quote takes, so the bottleneck between sales and pricing can't be identified or fixedMargin discipline can't be enforced — reps apply margins on personal judgment with no floor, ceiling, or system guardrailWin/loss data is non-existent — outcomes are logged inconsistently, reasons almost never, making lane and customer profitability analysis impossiblePerformance measured on feel, not fact — without data on volume, response speed, conversion, and margin per rep, incentive and coaching decisions are based on perception
Walk me through the complete journey of a freight inquiry — from when it first arrives to when a quote is sent. Who touches it, what decisions are made, and where does time get lost? What are the parameters that are needed on inquiry - must-have and should-have ones ?If you had to automate the most time-consuming part of this journey first, what would it be? What data would the agent need to do this, and where does that data live today? How should the agent handle an inquiry for a trade lane where the FF has no contracted rate? What are the options, and what are the tradeoffs? Design the margin guardrail logic. What should happen when a salesperson tries to quote below the minimum margin threshold? How would you measure whether this agent is working? Define the top 3 metrics and their targets for the first 90 days.
Document the detailed document workflow of inquiry, rate procurement and quotingFor each persona: define their top 3 Jobs To Be Done and the biggest friction in their current workflow Sales Rep: needs to respond to inquiries fast, with confidence in the rate accuracy Pricing Manager: needs to ensure margins are protected and rates are up to date VP of Sales /Business Owner: needs real-time visibility into pipeline, win rates, and margin health
Inquiry Intake: How does the agent capture structured inquiry data from unstructured channels (email, WhatsApp)? Field Completion: What fields are mandatory to generate a quote? How does the agent handle missing fields? Rate Fetching Logic: define the rate source hierarchy — contracted rates first, then spot rates, then partner network rates. How does the agent stitch multi-leg routes? Margin Engine: How are minimum margins defined, applied, and enforced? Who can override and under what conditions? Quote Generation: What does the output quote look like? Define the fields, format, and delivery mechanism Win/Loss Capture: How does the agent track whether a quote was won or lost, and what data is logged for future learning?
Inquiry arrives with no origin or destination specified The rate for the requested lane is expired or unavailable Multiple valid rates exist — how does the agent recommend which to use? Customer negotiates after quote is sent — how is the revision tracked?
V1: What is the smallest useful version of this agent? V2: What gets added to make it meaningfully better? What would you defer entirely to V3 and why?
Define what success and failure will look like for each persona - sales, pricing and vp of sales Define what success and failure will look like to us will loo internally to us
A simulated inquiry intake — paste or upload an unstructured inquiry (e.g., a WhatsApp message) Structured field extraction — show what the agent understood: origin, destination, commodity, weight, container type Rate lookup simulation — show a quote being assembled from rate inputs Margin check — demonstrate the guardrail triggering if the margin is below the threshold Quote output — a formatted quote ready to send to the customer
Format: Notion / Coda / Google Doc + prototype link PRD should be implementation-ready — specific, not vague AI tools are encouraged — but be ready to defend every decision in the debrief
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The Broken International Freight Booking
From inquiry to quote to booking in minutes. Not hours. Not days
🌍 Industry Overview
Think about the last time you booked a complex international trip - flights, hotels, transfers, and a visa. You could have done it all yourself, or you could have called a travel agent who knows the options, has the relationships, gets better rates, and sorts things out when it goes wrong.
A freight forwarder (3PL provider) is exactly that, except instead of moving people, they move cargo. When Zara wants to ship 10,000 denim cloth rolls from Arvind Mills in Gujarat to Amsterdam, they call a freight forwarder who books the container, arranges the truck to the port, handles customs on both ends, and tracks every step. They are the travel agents, project managers, and subject matter experts of global trade.
While platforms like MakeMyTrip, Expedia, ‘s — of the world revolutionised the experience of how we book our travels and stays within a few minutes, and Amedius, Sabre’s of the world, gave the connected backend platforms to travel agents to give the same experience with more customised offerings. Most of the Freight Forwarders still run on disconnected legacy systems, WhatsApp groups, Excel sheets, and institutional memory, while an importer/exporter might wait for 2–3 days to get a shipping quote, in an era where you can book a flight to Tokyo in 90 seconds. That's the problem space you're walking into. You don't need to be a logistics expert. You need to be a rigorous product thinker who can find signal in a messy, complex domain.
🧐 Problem Overview
A freight forwarder's commercial engine runs on one thing: the ability to quote fast, price right, and win the business. Today, that process is catastrophically slow. A customer sends an inquiry on WhatsApp at 10 AM. By the time a quote lands in their inbox, it's often 4–6 hours later — or the next day.
Meanwhile, in industries like air travel and hotel booking, customers get real-time pricing instantly. Freight is the last major commerce vertical where a 48-hour quote TAT is considered acceptable.
Freight forwarders lose 30–40% of inquiries simply because they responded too late. Speed and accuracy of the quote is the #1 driver of win rate.
📖 Key Jargons
Term
What it means
Shipper (Exporter)
The business that manufactures or owns the goods and is sending them out of the country. Think of them as the seller in the transaction — for example, a textile factory in Surat shipping fabric to a buyer in Italy
Consignee (Importer)
The business or individual on the receiving end — they're buying the goods and importing them into their country. In the same example, the Italian fashion brand waiting for that fabric at the other end is the consignee.
Shipping Line
The company that actually owns and operates the ships that carry containers carrying the cargo across oceans — think Maersk, MSC, or CMA CGM. They're the airlines of the sea. The freight forwarder books space on their ships the same way a travel agent books seats on a flight.
Inquiry
A request from a customer to get a price for moving cargo from origin to destination
Quotation
The forwarder's price response — includes freight, origin charges, destination charges, and surcharges
TAT
Turnaround Time — how long it takes from inquiry receipt to quote delivery
Contracted Rate
Pre-negotiated rates the FF has with carriers for specific trade lanes
Spot Rate
Market rate fetched in real-time from carrier portals or aggregators
Margin / Markup
The spread between the FF's buy rate and the sell rate quoted to the customer
Trade Lane
The origin-destination pair for a shipment route (e.g., Mumbai → Rotterdam)
Win Rate
% of quotes that convert to confirmed bookings
There are no rows in this table
🔬 Problem Statement
Rate discovery and quoting is the heartbeat of a freight forwarder's commercial engine. Yet every inquiry that arrives via email, WhatsApp message, or a phone call that gets transcribed onto a notepad requires a human to read it, qualify it, chase missing details, forward it to the right person, wait for a response, add a margin from memory, build a quote manually, and send it out hoping it lands before the customer goes somewhere else.
An MSME Fruits exporter based out of Nashik sends a WhatsApp message at 10 AM asking for a rate to move 2 containers from Nhavasheva to Rotterdam. The sales exec reads it, realises the commodity is missing, chases the customer, gets the details at 11, forwards the inquiry to the pricing team over WhatsApp, follows up twice, receives a rate at 2 PM, opens Excel or multiple carrier portal, builds the quote, applies a margin based on gut feel, exports it as a PDF, and emails it out by 3 PM. The customer responds the next morning, asking for a better rate — and the entire loop runs again from scratch with no memory of what was already tried. Average time from inquiry received to first quote sent: 4–6 hours. Average number of touchpoints across sales and pricing to close a single inquiry: 12–15 exchanges across WhatsApp, email, and internal tools. And at the end of it all if the deal is lost, nobody writes down why.
Current Workflow — The Manual Chain
💊 Personas Involved & Pain Points
Persona
Description
Pain Points
Sales Executive
The frontline revenue generator juggling 50–80 inquiries a day across email and WhatsApp, with no system to prioritise, track, or follow up on any of them. Speed of response is their biggest competitive weapon — and today that weapon is blunt
Pricing Executive
The rate engine behind every quote, spending their day switching between carrier portals, contracted rate spreadsheets, and WhatsApp threads — manually stitching together buy rates from a dozen sources for inquiries that arrive with no standard format and no context.
VP of Sales
Accountable for revenue targets, win rates, and margin health — but assembling their view of the business from WhatsApp check-ins and a tracker sheet that's always a day behind.
There are no rows in this table
🧠 The Assignment
Design a Quoting Agent — an AI-native module within the Shipmnts Sales Cloud
That automates the journey from inquiry to quote, enforces margin rules, and learns from win/loss outcomes Key Components - Intelligent Queue / Inbox an AI-native layer that reads inquiries from multiple channel - Classification & Extraction - Classif emails received as inquiry or not inquiry → extracts structured data from email on inquiry → standardises the data from the documents - Intelligent Rate and Pricing Engine - which discovers the right rate and - Quoting Engine which decide what markup to add and generate the quote
🎯 Part A - White Boarding Round (60-90 mins)
There are no right answers — we are evaluating how you structure ambiguous problems, what questions you ask, and how you prioritise.
Some sample questions for you to get a head start
Lets see how you can use AI as companion before we get to actual White boarding round to research about the domain. To give you a quick head start you can look at companies like - cargo.one, webcargo by Freightos
🛠 Part B - Take Home Assignment (4-5 days)
This is your opportunity to go deep. We expect a working prototype — not just a PRD — because we want to see how you use AI tools to accelerate your thinking and communication.
Deliverable 1 — Product Requirements Document
🔬 Problem Space & Personas
🧠 Solution Design — The Quoting Agent
📒 Edge Cases
🕛 Phased Roadmap
🏆 Success Metrics
Deliverable 2 — Working Prototype
Use Claude, ChatGPT, Lovable, Bolt, or any AI-native prototyping tool to build a working prototype that demonstrates the core agent flow. A static Figma mockup is not sufficient.
Your prototype must demonstrate:
Submission: Loom walkthrough (5–7 mins) + live prototype link or repo
Submission Guidelines
Sample Documents
Sample Inquiries
Air
Sea
Sample Rate Contract Sheets
Sea -
One Line Rate Sheet (3).xlsx
60.6 KB
Air
Fw_ Air Contract and Tariff Management in Shipmnts.pdf
103 KB
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