70-80%
Early-ramp scrap
Hazardous waste and lost value can reach this level in early battery-factory ramp-up.
Industrial intelligence for mission-critical manufacturing
Reduce battery manufacturing defects before scrap compounds
Lychee Labs helps battery manufacturing teams detect elevated defect risk earlier - so they can reduce scrap, accelerate root-cause investigation, and recover yield before more revenue is lost.
Built for battery manufacturing environments where delayed discovery can destroy millions in value before the root cause is clear.
Economic stakes
Why earlier visibility matters economically
Battery manufacturing is one of the harshest proving grounds in industry. Scrap can stay painfully high during ramp, cycle times delay defect visibility, and even a 1-point improvement can be worth millions per year at factory scale.
3 wks
Cycle-time delay
Formation and aging mean defects are often recognized only after weeks of added time and value.
32%
Cell-cost impact
Battery quality failures do not just hit inspection. They hit a large share of total cell cost.
~€10M/yr
Per 1-point gain
At 40 GWh/year scale, even a 1-point improvement can be worth about this much annually.
Delayed discovery is not just a quality problem. It is a throughput, margin, and capital-efficiency problem.
Earlier defect visibility
Surface elevated risk before downstream confirmation turns drift into more scrap.
Faster root-cause loops
Shorten the path from observed quality issues to likely upstream drivers.
Higher-value intervention
Help teams act while lots are still recoverable, not after value is already lost.
The problem
Battery factories discover defects too late - after millions have already been lost
In battery manufacturing, process drift can begin upstream while defects only become visible much later, after more material, labor, and cycle time have already been consumed. At factory scale, that delay becomes a multimillion-dollar revenue and margin problem. Lychee Labs helps teams surface defect risk earlier from fragmented process and quality data, so they can investigate sooner, intervene earlier, and reduce scrap, rework, and lost output before value is destroyed.
Upstream process drift
The earliest signals often begin before downstream failure is visible.
Lost value accumulates
Material, labor, cycle time, and engineering effort keep compounding before diagnosis is clear.
Defect discovered too late
Most workflows intervene after quality loss is visible, not when it is still preventable.
Why now?
Margin pressure is making defect reduction mandatory
Battery price compression is reducing tolerance for scrap, yield loss, and slow root-cause cycles. When prices fall and leaders push for higher manufacturing yields, earlier defect prevention becomes economic necessity.
$108/kWh
Global average pack price
2025
$84/kWh
Average in China
2025
95%
Yield potential for leaders
With manufacturing improvements by 2035
Pilot motion
Designed for a first pilot that plant teams can actually scope
Battery manufacturers need to know what gets deployed first, what data is required, and what the team gets back before they commit time and trust.
First pilot scope
Start with one line, one defect family, and one operating KPI tied to scrap, yield, or diagnosis speed.
Minimum viable data
Typical inputs include historian or equipment data, batch or lot context, QC results, and the practical notes teams already keep.
Typical pilot window
Run an initial pilot over a defined operating window in weeks, not a long systems program before value can be evaluated.
What teams get back
Ranked risk signals, likely upstream drivers, and intervention priorities for engineering and production teams.
Plant fit
Built to fit real plant systems and governance constraints
A credible battery-manufacturing deployment has to coexist with the stack that already runs the factory and handle data that is messy in practice.
Fits around MES, SPC, inspection, and QMS
Lychee Labs is not a rip-and-replace system. It sits across existing plant systems to make delayed process and quality signals more actionable.
Built for ugly plant data
Battery plants do not have pristine data. The product is designed for fragmented, incomplete, and uneven industrial history across tools and teams.
Product-first deployment
The goal is software that can be piloted and expanded with a defined scope, not a consulting-heavy science project disguised as a platform.
How it works
From fragmented history to earlier action
01
Ingest fragmented manufacturing context
Bring together process history, machine events, quality outcomes, and inspection signals that usually live in disconnected systems and disconnected moments in time.
02
Surface elevated defect risk earlier
Turn scattered history into earlier warning signals so teams can act before delayed visibility becomes additional scrap, rework, or lost cycle time.
03
Prioritize likely upstream drivers
Narrow the search space around likely upstream contributors so engineering teams can move faster from symptom to likely cause.
04
Support faster stabilization
Help manufacturing teams intervene sooner, learn faster, and recover yield and process stability with less diagnostic delay.
Use cases
Where teams use Lychee Labs
Core workflows for battery manufacturing and adjacent energy systems
Defect risk prediction
Surface elevated defect risk earlier from fragmented process and quality history before later-stage failure visibility compounds the cost.
Diagnosis acceleration
Shorten the path from observed quality issue to the upstream conditions most worth investigating first.
Ramp stabilization
Help teams stabilize lines faster during new-factory ramp, restart periods, and process transfers where drift is expensive and diagnosis time matters.
Cross-system visibility
Create a usable operating view across process, inspection, and quality systems that rarely line up cleanly in practice.
Why Lychee
Built for high-consequence industrial reality
Not another dashboard
The goal is earlier action in high-consequence manufacturing environments, not passive reporting after the damage is already visible.
Designed for fragmented industrial systems
Battery operations rarely suffer from a lack of data. They suffer from delayed, disconnected, and hard-to-use manufacturing context.
Built for battery manufacturing, relevant wherever delay is expensive
Lychee Labs is built for battery manufacturing, where delayed discovery is especially costly, and where earlier visibility can protect yield, throughput, and revenue.
Teams
For manufacturing, process, and quality teams
Designed for industrial teams responsible for yield, throughput, stability, and diagnosis speed.
Manufacturing Engineering
Process Engineering
Quality Engineering
Cell Engineering
Production Leadership
About Lychee Labs
Industrial intelligence for manufacturing environments where delay destroys value
Lychee Labs builds industrial intelligence for mission-critical manufacturing, with an initial focus on battery and energy environments where delayed discovery is especially expensive. The platform helps industrial teams detect defect risk earlier, accelerate diagnosis, and reduce waste, lost output, and revenue leakage.
Founder
Linda Hong Cheng
Linda Hong Cheng is the founder and CEO of Lychee Labs. A BBC-featured AI founder, former AI PhD, Clarendon Scholar at Oxford, and AI research fellow at Columbia, she leads the company’s ML systems, product vision, and commercial strategy.
See founder and company background
Lychee LabsWorking on battery yield, scrap, or diagnosis speed? Let’s scope a pilot.
For pilot discussions, technical introductions, and customer conversations.