How ShipBob Drove a 3x Increase in Output Across 200+ Engineers With Driver

About

ShipBob is becoming the default global fulfillment infrastructure powering both platforms and brands, like True Classic, Bloom Nutrition, Our Place, and Tonies. Working across the United States, Canada, the United Kingdom, the European Union, and Australia, ShipBob has fulfilled over 1 billion orders and maintains a 99.97% order fulfillment accuracy rate.

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Challenge

Unifying Context Across a Ten-Year-Old, Multi-Repo Codebase

ShipBob powers fulfillment operations for today’s fastest-growing ecommerce companies and with millions of dollars in order volume being processed each week, even the smallest regressions in shipped code could result in mis-shipments, inventory errors, and lost revenue for merchants. As such, ShipBob places a strong requirement on reliability and quality for each release.

As AI-assisted development became more central to ShipBob’s engineering workflows, that mandate came under an even more intense magnifying glass. Increasing the percentage of code written with AI would be beneficial only if agents could deliver accurate, reliable outputs based on up-to-date context. But manually gathering that context across a ten-year-old codebase that contained a mix of microservices, micro frontends, and monoliths was no easy task.

Kashyap Mukkamala, Principal Engineer at ShipBob, was all too familiar with this challenge. While he and his fellow engineers all exhibited deep knowledge across many of ShipBob’s repos, with over 450 to account for, much-needed context often remained siloed. As a result, his team was frequently chasing down other engineers or spending hours reading code and tracing dependencies manually to cross this context chasm. “Very early in our process with AI-assisted development, we learned that garbage in equals garbage out,” Kashyap shares. “We needed a way to give our agents the right context without the manual overhead.”

For Jacob Radkiewicz, CTO at ShipBob, this challenge also presented an opportunity to improve engineering efficiency across the organization. Developer surveys and DX metrics revealed how context switching and siloed code knowledge were major friction points hindering both output and cross-team collaboration. In some cases, when a senior domain expert moved teams, the previous team could lose 6 to 8 weeks of development progress while another engineer rebuilt the context that had been lost. Naturally, providing the context infrastructure needed to ensure institutional code knowledge was accurate, current, and available wherever engineering work occurs became his strategic imperative.

Kashyap and Jacob initially explored building an internal system that would auto-document code, keep it up to date, and expose it programmatically for planning, debugging, tasking, and implementation. However, they knew this would become a dedicated product effort for the better part of a year, with ongoing support, constant feedback loops, and a maintenance burden that would compete with ShipBob’s core engineering priorities. They needed an external context infrastructure partner that would own this effort from end to end.

That search ultimately led them to Driver.

We started building our own auto-documentation system and realized within a couple of weeks it would become a massive, ongoing project that pulled us away from higher-value work. Driver’s pre-compiled context infrastructure helped us avoid that perpetual bandwidth drain.

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Solution

Always-On Context Infrastructure Brings Determinism to ShipBob’s Development Workflows

ShipBob implemented Driver to transform over 450 repos and 20M lines of code into a shared, queryable context layer that helps teams plan changes, investigate incidents, and build automation from a single source of truth.

The partnership began with a limited pilot where the ShipBob Engineering Team validated whether Driver’s pre-compiled context could support ShipBob’s varied architecture in real-world workflows. After seeing strong outputs within days, that pilot quickly expanded, with Kashyap moving from 20 to 450+ repos in one night without pre-staging the work with Driver. Instead of asking ShipBob to slow down or split the rollout into smaller batches, the Driver team stayed in close communication and kept ShipBob moving through the rollout. Within 10 days, ShipBob’s entire codebase was indexed.

Driver’s deep integrations with ShipBob’s existing AI tools (like Claude Code, Claude Desktop, and GitHub Copilot) made onboarding even more frictionless. Architecture overviews, code maps, file-level documentation, and commit history are available the moment an engineer needs them—in whatever tool they are working in.

Today, Driver’s pre-compiled context empowers ShipBob’s engineers to understand unfamiliar repositories and cross-service dependencies before making changes. Product managers write AI-ready stories with a clearer understanding of current implementation, existing constraints, and the systems a change might touch. Platform teams treat Driver as a future integration layer for machine-to-machine workflows, no-touch automation, and ShipBob’s evolving SDLC tooling.

That context gives teams a clearer view of dependencies, affected services, and implementation history before changing software tied directly to merchant inventory, fulfillment workflows, and shipping operations. “When I work in an unfamiliar domain now, I can get a pretty good picture of what is happening within the first 10 minutes,” shares Kashyap. “Before Driver, we would have spent 30 to 40 minutes to come up with a plan to solve the same problem.”

Crucially, Driver made AI-assisted development more reproducible at ShipBob. Previously, a developer could ask Claude to inspect GitHub for relevant files, dependencies, and implementation paths, but the quality of the result depended heavily on the prompt and the person writing it. With Driver, every engineer, team, and workflow draws from the same context infrastructure. That consistency is what makes automation at scale viable.

ShipBob is already using Driver as part of a no-touch automation proof of concept for the initial phases of the SDLC, where internal tooling relies on pre-compiled codebase context to support research and planning workflows. That gives Jacob and Kashyap’s teams a repeatable foundation for scaling automation across the agentic SDLC, with agents drawing from the same reliable context infrastructure before coding execution work begins.

Driver singlehandedly helped us overcome the context chasm at ShipBob. The consistency, accuracy, and repeatability of our development workflows have soared since implementation.

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Results

ShipBob Ignites an Agentic-First Engineering Revolution With Driver

With Driver, more than 200 engineers, product managers, and adjacent technical teams at ShipBob unlocked an always-on context layer that powers faster and more accurate workflows across the organization.

• 3x increase in output across 200+ engineers (DX TrueThroughput™)
• 450+ repos and 20M+ lines of code contextualized with Driver
• <2 weeks from pilot to deployment

Looking ahead, Kashyap and Jacob plan to expand ShipBob’s agentic SDLC while measuring whether pre-compiled context drives a significant reduction in token usage over time.

If we turned off Driver tomorrow, I guarantee our token usage would rise significantly. They’re not just helping us with context infrastructure, they’re optimizing our resourcing to drive outsized value to our customers at a speed that nobody else in our industry can match.