Rivian
Mock sales call — Head of Systems Engineering. 20 minutes.
Slide Deck
From R1 to R2
How Rivian Scales Requirements Discipline
CI/CD for hardware when a single requirement change cascades through 5,000+ parts and three parallel production lines.
R2 series production started April 2026. 155,000 units/year target by 2027. The design velocity has to match.
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Prospect Profile
Rivian ships five vehicles from two factories across three overlapping programs: R1T/R1S (premium adventure EVs, ~42K units/year from Normal, Illinois), R2 (mass-market SUV, production started April 2026, targeting 155K units/year by 2027), R3 (compact crossover, Georgia Stanton Springs facility, 2028), and the EDV delivery van for Amazon. They're past the "will this work" phase of R1 ramp and deep in "how do we run three programs at cost targets that sustain profitability."
The R2 is the test case. Designed clean-sheet for unit economics: 50% lower manufacturing cost than R1, achieved through part consolidation (2.3 miles of wiring harness eliminated), structural simplification (high-pressure die-casting cuts 2,000 lbs), and 72% lower suspension assembly cost. That cost discipline decomposes all the way down into requirements — which CAN bus message interval (the timing on Rivian's onboard vehicle network) saves $45 in battery management chip cost.
Systems Engineering Surface
Rivian owns the full stack. Battery cells, drive units, power electronics, the skateboard platform — no supplier black boxes for critical subsystems. A single change cascades through every domain they own. A new battery cell chemistry with 5% higher voltage triggers re-verification of: converter efficiency (3-4 hours of finite element simulation on AWS), pack thermal behavior (a 2-hour re-run in COMSOL, a physics simulation tool), battery management firmware regression (12-hour test suite), vehicle electrical schematics, ISO 26262 ASIL-D traceability (the automotive functional safety certification, highest level), and documentation for EPA, NHTSA, and DOT sign-off.
AWS case studies reveal something useful: Rivian's simulation infrastructure is already wired to run automatically on every code commit (CI/CD-connected). SimOS (Rivian's simulation orchestration platform) chains thermal, structural, and electrical models on every code commit. The bottleneck isn't compute capacity — it's requirements traceability velocity.
Pain Points
Faster iteration. R2 cost-down requires 10-15 design iterations before architecture freeze. Each cycle currently takes 3-4 weeks because requirement changes batch through IBM DOORS (their current requirements tool) or email-threaded interface negotiations. Compress that to 90 minutes and you run 10 iterations in the time you used to run two.
Fewer integration surprises.Pack-to-vehicle is Rivian's historical bottleneck. The battery management firmware has a 500ms over-temperature shutdown response. The vehicle safety logic expects 50ms. The mismatch shows up late in testing, after mechanical assumptions are already locked in. With Flow, cross-domain timing conflicts surface before anyone has written code.
Greater confidence at reviews.When the VP asks "is R2 ready for Design Freeze Review?" the answer currently requires aggregating sign-off from 12+ subsystem leads, each with their own tool. Flow makes requirement status, analysis margin, test completion, and the open change-request pipeline one query.
Flow Integrations at Rivian Scale
Thermal simulation as a gated requirement check. When a systems engineer proposes an ambient temperature change for a new market (52°C for the Middle East), an AI client queries the linked COMSOL model, triggers a re-run via the AWS API, reads the result, and proposes three restoration paths with cost and weight tradeoffs. Feedback loop: 90 minutes, not three weeks.
CI test results auto-linked to requirements. The drive-unit dyno runs a 15-minute constant-torque test every commit. Results go to S3 (AWS file storage). A Flow automation watches the bucket and creates a verification record linked to the relevant motor performance requirements automatically. No one files it manually.
R2-to-R1 requirement reuse.An AI client clones the R1 requirement tree into R2 as a baseline, flags which requirements need re-verification given the reduced cell count, and proposes new target values. Rivian's thermal lead reviews 40 flagged requirements instead of writing 247 from scratch.
Cross-domain bus conflict detection.The battery management system requires cell data over the vehicle's CAN bus every 10ms. Vehicle diagnostics expects pack state-of-health every 50ms. Same wire, timing conflict. An AI client traverses the requirement graph across domains and opens a change request before anyone has written firmware.