A Series B logistics-tech platform had a roadmap its 14-person engineering team couldn't keep pace with — and a hiring process that was taking four to six months per senior engineer. An embedded Infomaze team joined their existing repos and sprint cadence, reached full contribution inside six weeks, and helped the company ship three roadmap-critical releases in a single quarter.
Logistics-technology SaaS platform, USA (name withheld under partner NDA)
Technology Development Partner
Dedicated engineering team
2 backend engineers, 1 AI engineer, 1 QA engineer
Existing GitHub, Jira, and CI/CD pipeline — no new tooling introduced
9 months, ongoing
The platform's core product — route optimisation and carrier management for mid-market logistics operators — had strong customer pull, and the product team had a backlog to match: a carrier-facing self-service portal, a rebuilt pricing engine, and an AI-assisted exception-handling feature that customer success had been requesting for two quarters. The 14-person engineering team was fully committed to keeping the existing platform stable and couldn't absorb three new workstreams without either slowing the roadmap or burning the team out.
Hiring was the obvious answer and the slowest one. Senior engineers with the right combination of backend and logistics-domain experience were taking four to six months to source, interview, and onboard — and the company's VP of Engineering didn't want to make three or four permanent hires for what might be an 18-month surge in roadmap demand rather than a permanent step-change in team size.
Rather than standing up a separate offshore workstream, Infomaze's engineers were onboarded directly into the company's existing GitHub organisation, Jira boards, and CI/CD pipeline — the same tools and the same conventions the in-house team already used.
The embedded team's first sprint was spent entirely in code review, architecture walkthroughs with the in-house team, and pairing on smaller bug fixes — deliberately low-stakes work chosen to build familiarity with the domain logic before anything roadmap-critical was assigned.
The embedded team joined the company's existing two-week sprint cycle and daily standup rather than running on a separate schedule that would have required the in-house team to context-switch between two processes.
The in-house team retained the core route-optimisation engine — the platform's most sensitive and highest-risk code — while the embedded team owned the carrier self-service portal and pricing engine rebuild as clearly scoped, independently deployable workstreams.
The exception-handling feature required close collaboration with customer success on edge cases, so the embedded AI engineer sat in on the same customer feedback sessions as the in-house team rather than working from a handed-down spec.
The usual failure mode with an outsourced team is treating it as a separate unit that receives finished specs and returns finished code — a structure that works for isolated projects but breaks down on a live product with deep domain logic. The decision that mattered here was refusing to isolate the embedded team from the rest of engineering. Shared sprints, shared code review, and a deliberate low-stakes onboarding period meant that by the time roadmap-critical work started, the embedded engineers weren't working around the in-house team's context — they had it.
Ramp time on an embedded team is a design choice, not a fixed cost. A team dropped straight into critical-path feature work with no onboarding period will always take longer to become genuinely productive than a team given two deliberate weeks to build context first. The fastest path to speed is not skipping the ramp-up — it's structuring it well.
The carrier self-service portal and the rebuilt pricing engine shipped in the same quarter, and the AI-assisted exception-handling feature followed six weeks later — all three landing inside the timeline the VP of Engineering had originally called unrealistic for the in-house team alone. The customer renewal tied to the exception-handling feature closed on schedule.
Nine months in, the embedded team has flexed twice: scaling up by one additional backend engineer ahead of a major release, then back down once that release stabilised. The company's Head of Product now describes the arrangement less as "outsourced development" and more as "a fourth pod on the engineering team that happens to bill monthly instead of drawing a salary."
We embed engineers into your existing repos, sprints, and standards — scaling up or down as your roadmap demands.
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