Burt: supervised AI teammates for freight broker operations

Burt builds AI teammates for freight brokers and forwarders that are still doing tracking, quoting, scheduling, document chasing, and invoice work through email, calls, portals, and a TMS. The buyer decision is whether a configurable coworker can learn the team's process and earn autonomy without becoming an opaque logistics operator.

What it does

Burt describes four concrete workflows: track shipments and answer ETA requests, quote loads, schedule dock appointments, and chase missing PODs or carrier documents. Its agents work on top of existing systems, read emails, make calls, log into portals, update a TMS, and escalate when they do not know what to do. The implementation model starts with supervised corrections and gradually increases autonomy (Burt homepage; YC launch).

Fact	What the public sources say
Buyer	Freight brokers, forwarders, and logistics operations teams
Workflows	Track, quote, schedule, chase, document processing, and invoice operations
Systems	Email, phone, portals, TMS, and existing customer processes
Control model	Training wheels, approvals, corrections, and human escalation before autonomy
Founders	Bobby Zhong and Kurt Sharma

Why it fits

The best part of the product is its specificity about the daily grind. A broker does not need a chatbot; they need a missed POD chased on time, a pickup audit run before the customer asks, or a quote answered while the team is asleep. Burt's approach treats the process itself as the product and lets the team correct the agent like a new coordinator.

The tradeoff is operational variance. A buyer should resolve carrier and portal coverage, call recording, permission boundaries, TMS writebacks, exception handling, customer-facing messages, and what the agent can do without approval. The site says it can be live in days, but that is a company claim; pricing was not published and no delivery or quote workflow was tested.

Founder-market fit is strong. YC describes Zhong as having grown up in a logistics family and previously built coding-agent systems at Replo, while Sharma built data pipelines and sandboxed code systems at Replo (YC company profile). Their logistics context explains the wedge, while their developer background explains the agent architecture.

Short version: Burt is worth piloting on one high-volume, low-ambiguity workflow such as POD chasing or pickup tracking. Expand only after the exception queue is more reliable than the manual queue.

Sources checked — 2026-09-19

Cohort context

Burt is listed in Winter 2026. In our 2026-09-18 directory snapshot, 126 of 199 listed companies in that cohort have YC’s primary industry label B2B (63.3%). This is a current-directory comparison, not an original intake count or a performance ranking. Nine-cohort dataset.

Public website snapshot

Observed 2026-09-19T16:20:04.113Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

Signal	Homepage observation
Product description metadata	Observed
Canonical link	Observed
H1 or H2 heading	Observed
Typed structured data	Not observed in this response
Docs/developer link	Not observed in this response
Pricing link	Not observed in this response
llms.txt link	Not observed in this response
Markdown alternate	Not observed in this response

Public observations · Collection method. Missing links here do not establish that a capability or file is absent elsewhere.