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AI technical support for complex physical products

Every product ever sold needs support at some point. That support falls into one of two buckets. Bucket 1: Simple stuff. T-shirts, screen protectors, keyboards. You buy it, it shows up, maybe you ask “where's my order?” once. This is solved. Zendesk and a hundred other horizontal companies solved it. Bucket 2: Hard stuff. $20,000 industrial heaters. HVAC systems. CNC machines. Car parts. Products where buying wrong means your building doesn't have heat or your manufacturing line is down. Support for these products can only be performed by highly trained domain specialists and there aren't enough of them. If you're selling EV charging stations, your support person needs to be a certified electrician who understands local power grids, installation codes, and compatibility matrices. You can't hire this off the street. You can't outsource it overseas. You'd think LLMs would have solved this by now. They haven't. Three years into the LLM era, penetration in this industry is very low & the reason is twofold. First, off-the-shelf models don't actually understand these products. The knowledge lives in 48-page technical manuals buried on some manufacturer's website in terrible formatting — wiring schematics, compatibility matrices, installation diagrams that can only make sense visually. A general-purpose LLM can't draw you the diagram showing how to connect terminal A to terminal B. It doesn't have the spatial understanding or the product-specific reasoning to be a real technical advisor. So companies still rely entirely on human experts. Second, even if the models were good enough, there are no harnesses to make them useful in the business. No engine to capture deep technical knowledge about complex physical products and keep it updated. No way for a company to offload tribal knowledge from their senior technicians into a system. No way to see what questions customers are actually asking and feed that back into the knowledge base. No generative multimodal presentation and no expressive voice support. Prox is building the best technical product expert for extremely complicated physical products. A multimodal agent that can draw wiring schemes, share CAD models, process incoming videos from a technician in the field, and support people over the phone with voice that can pass the Turing test. To get there, we're solving multimodal knowledge graph building at a very deep level. A huge portion of your work will be developing SOTA knowledge engines that can truly understand complex physical products.
Active Founders
Dima Yanovsky
Dima Yanovsky
Founder
mit '25
Gregory Makodzeba
Gregory Makodzeba
Founder
> founded Rektoff (built security systems protecting >$100B; clients: Solana) > led DevRel at Runtime Verification (among clients: NASA, Boeing) > grew up in a family distribution business > studied aviation management and computer science
Company Launches
Prox: digital co-workers for logistics operations
See original launch post

Greg and Dima here - childhood friends, grew up in our families’ warehouses - now YC founders building Prox.

launch video: https://www.youtube.com/watch?v=9Ywl-K7ncj0


tldr:

Prox builds digital workforce for third-party logistics and fulfillment providers. We deployed our first agents at ShipBob (unicorn 3PL handling 100+ million packages per year) to automate carrier claims for lost-in-transit packages. They're now live in production, processing hundreds of claims in parallel and on track to recover millions in carrier refunds annually.

what is 3PL?

third-party logistics companies (3PLs) are the invisible infrastructure behind e-commerce brands: they warehouse inventory, fulfill orders, manage returns, and negotiate with carriers so brands don't have to.

the problem:

40,000+ 3PLs in the US all do the same thing: throw people at operational bottlenecks.

❌ Claims processing done manually, one by one
❌ Invoice reconciliation eating hours of labor
❌ Compliance paperwork blocking growth
❌ Every increase in package volume = more back-office hires

That's the margin trap. Logistics companies stuck at 3-5% margins because headcount scales linearly with volume of packages. You can't compound when labor costs are linear.

what we built:

We developed an n8n-inspired agent building platform - native to logistics infrastructure. It integrates with Salesforce, warehouse & transportation management systems, carrier portals, Microsoft Teams, and email → then executes SOPs end-to-end.

With this platform, we can deploy agents in production in a matter of days.

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use case: ShipBob Carrier Claims Automation

Here’s an example of how our agents handle lost package claims for ShipBob, one of the biggest 3PLs in the US.

first demo at ShipBob HQ in Chicago

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When a package goes missing - our agent orchestrates resolution: pull order data, calculates refund amounts, files with carriers, monitors responses, parses updates, syncs internal systems, escalates exceptions - hundreds running in parallel.

Our agents do not stop at task execution - we designed them to own outcomes. If a carrier doesn't respond, the agent follows up. If documentation is missing, it fetches it. Each agent has one objective: get the claim resolved.

about us:

Gregory Makodzeba:

  • Founded Rektoff: built blockchain security infrastructure protecting $100B+ in crypto assets (clients: Solana Foundation)
  • Led Developer Relations at Runtime Verification, working with NASA, Boeing, and Ethereum Foundation on formal verification for mission-critical systems
  • Grew up implementing ERP and WMS systems in the family distribution business

Dima Yanovsky:

  • MIT grad: Computer Science + Electrical Engineering
  • Robotics research at MIT CSAIL under Prof. Pulkit Agrawal on manipulation policies;
  • Built internal and customer-facing AI agents at Pulley (YC W20) during early GPT era
  • Spent childhood automating warehouse operations and business processes

We both grew up in our parents' warehouses. When we weren't moving boxes around, we were doing the mundane back-office work every warehouse has (filing claims, reconciling invoices, chasing carriers). This was the pre-LLM era, so we had no choice but to grind through manual workflows. We saw firsthand how coordination became the biggest constraint on growth - and how no software could actually handle it end-to-end.

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We deeply understand the ontology of logistics operations. That's why we can ship integrations in days, not months. We're building the digital workforce we wish existed back then.

If you're running logistics operations at a 3PL, fulfillment center, or freight forwarder, or know someone who is - reach out: founders@prox.inc


YC Photos
Jobs at Prox
San Francisco, CA, US
$200K
1.00%
Any (new grads ok)
Prox
Founded:2025
Batch:Fall 2025
Team Size:2
Status:
Active
Location:San Francisco
Primary Partner:Harj Taggar