Addionics
Year:
2024 - 2026
Service:
End-to-end Data infrastructure
Industry:
Energy Tech
Size:
70–100 employees
Client Website:
Addionics transformed their battery R&D operations from data chaos to strategic intelligence—eliminating scattered spreadsheets, manual searches, and fragmented analysis that were throttling innovation at a company racing to revolutionize energy storage.
Introduction
Addionics is pioneering the next generation of rechargeable batteries through innovative 3D current collector technology. Their breakthrough enables batteries that are cheaper, lighter, faster-charging, and longer-lasting—critical advances for electric vehicles, grid storage, and portable electronics. With over $85 million in funding and a rapidly scaling team, they've achieved remarkable technical milestones: 12% higher energy density, 60% extended battery lifetimes, and partnerships with major automotive manufacturers.
But as R&D operations scaled from startup to industry leader, their data management couldn't keep pace. The bottleneck wasn't scientific expertise—Addionics had world-class battery engineers. The problem was infrastructure. Years of relying on Excel spreadsheets and paper lab notebooks had created a fragmented data ecosystem where critical experimental results were scattered across hundreds of files, different teams used inconsistent naming conventions, and nobody could trace a battery formulation from concept through validation without hours of manual searching.
For a company whose competitive advantage depends on rapid iteration and data-driven optimization, this wasn't just inefficient—it was unsustainable.
Challenge
Data Scattered Across Silos
Addionics' battery development process generated massive amounts of data: electrochemical test results, material formulations, manufacturing process parameters, quality control measurements, and performance validation data. All of it lived in Excel spreadsheets, paper notebooks, shared drives, and email attachments. There was no unified system, no standard structure, and no way to see the complete story of any experiment.
When a scientist needed historical data on a specific formulation, they'd spend hours—sometimes days—hunting through folders, asking colleagues, and manually compiling information from multiple sources. When leadership wanted to report R&D progress to investors, someone had to manually aggregate data from dozens of spreadsheets. When manufacturing needed specifications for a validated process, engineers would track down the original researcher to get details that should have been instantly accessible.
Inconsistent Data Standards
Different lab teams recorded the same measurements in different formats. One team logged temperature in Celsius, another in Fahrenheit. Battery cycle counts were sometimes recorded per test, sometimes cumulative. Formulation recipes used different units and notation conventions. This inconsistency meant that even when data was found, it couldn't be reliably compared or aggregated without manual cleaning and transformation.
The lack of standardization created constant friction, introduced errors into analysis, and made it nearly impossible to identify patterns across experiments or validate results across teams.
Collaboration Bottlenecks
As Addionics expanded globally with teams in multiple locations, the data fragmentation became even more problematic. Scientists in one location couldn't easily access results from another. Collaborative projects required constant back-and-forth emails sharing spreadsheets. Time zone differences meant delays waiting for someone to find and send the right file.
The infrastructure that worked for a 20-person startup had become a scaling bottleneck for a 70+ person organization pursuing aggressive growth targets.
Invisible Progress
Leadership couldn't answer basic questions about R&D velocity: How many experiments ran last month? What's our success rate for new formulations? Which processes are most cost-effective? Where are we seeing the most promising results? These strategic questions required days of manual data compilation—by which time the information was already outdated.
Without real-time visibility into R&D operations, it was impossible to make data-informed decisions about resource allocation, project prioritization, or strategic direction.
The reality: Scientists were spending up to 50% of their time searching for data instead of generating insights. That's not innovation—that's waste.
Solution
Building a Unified Data Foundation
Atomic Systems designed and implemented a custom data infrastructure tailored specifically to Addionics' battery R&D workflows. Rather than force-fitting their complex operations into a generic LIMS, we engineered a system that understood the nuances of electrochemical testing, material science, and manufacturing scale-up.
Custom Database Architecture:
We created a structured database with 45+ interconnected tables capturing every aspect of battery development—from initial formulation concepts through electrochemical testing, manufacturing trials, quality control checks, and final validation. The schema was designed to handle the complexity of battery data while remaining intuitive for scientists to use.
Seamless Data Entry Platform:
We built user-friendly interfaces that scientists actually wanted to use. Data entry forms were designed around real lab workflows, with intelligent defaults, dropdown menus for standardized values, and validation rules that caught errors at entry time. The system worked equally well for team members in different locations, providing global access through secure web interfaces.
Automated Data Capture:
For equipment that generated digital output—battery cyclers, electrochemical analyzers, manufacturing sensors—we built direct integrations that automatically captured test results and wrote them to the database. This eliminated manual transcription errors, saved scientist time, and ensured that data was captured consistently every time.
Real-Time Analytics Dashboards:
We developed custom dashboards showing experiment progress, success rates, resource utilization, and performance trends—all updated in real-time as new data flowed into the system. Scientists could see their results immediately, lab managers could monitor operations, and leadership could track strategic metrics without manual compilation.
Implementation Approach
The transformation wasn't just technical—it required careful change management and user adoption:
Deep Discovery: We spent two weeks embedded with Addionics' teams, observing workflows, understanding pain points, and mapping their entire data ecosystem.
Collaborative Design: Key stakeholders from lab operations, R&D, manufacturing, and quality were involved in designing the system, ensuring it addressed real needs rather than theoretical requirements.
Phased Migration: We didn't flip a switch—we migrated data and rolled out features incrementally, starting with the most painful bottlenecks and expanding based on user feedback.
Comprehensive Training: Every user received role-specific training on their workflows. We trained internal champions who could support their peers after our formal engagement ended.
Continuous Refinement: For the first 30 days post-launch, we provided intensive support, gathering feedback and making adjustments to ensure the system fit naturally into daily operations.
Result
Measurable Impact After 12 Months
70% Reduction in Scattered Data:
From 15+ disconnected spreadsheet systems to one unified platform. Scientists now find any experiment, formulation, or result in seconds instead of hours. The single source of truth eliminated duplicate data, conflicting versions, and endless searching.
2X Faster Data Analysis:
What previously took days of manual compilation now happens instantly through automated dashboards and reports. Leadership can answer strategic questions in real-time. Scientists can identify patterns and optimize formulations based on comprehensive historical data.
Massive Scale & Adoption:
30,000+ data points added to the system
22,000+ platform interactions demonstrating heavy, consistent use
50+ monthly active users across R&D, manufacturing, and quality teams
Global adoption with teams in UK ,US & Israel using the system daily
Strategic Transformation
Beyond the numbers, Addionics achieved a fundamental shift in how they operate:
From Reactive to Proactive:
Instead of scrambling to find data when questions arise, teams now proactively analyze trends, spot opportunities, and make data-informed decisions about which experiments to run next.
From Siloed to Collaborative:
Global teams can now collaborate seamlessly, building on each other's work without waiting for spreadsheets to be emailed around. Knowledge that was previously locked in individual notebooks is now accessible to the entire organization.
From Manual to Automated:
Routine data handling—capturing test results, generating reports, tracking experiment status—now happens automatically, freeing scientists to focus on innovation rather than administration.
From Opaque to Transparent:
Leadership has real-time visibility into R&D operations. Investors get accurate, up-to-date progress reports. Manufacturing knows exactly which processes are validated and ready for scale-up.
Business Impact
The data infrastructure became a competitive advantage:
Faster iteration cycles: Scientists can test more formulations and optimize faster because they're spending time on science, not data hunting
Better decision-making: Strategic choices about which battery chemistries to pursue are backed by comprehensive data analysis rather than gut feel
Scalability: The infrastructure supports Addionics' aggressive growth plans—adding new team members, expanding to new locations, and ramping production—without drowning in data chaos
Investor confidence: Clear metrics and transparent reporting demonstrate progress and professionalism, supporting future funding rounds