Materials Testing & R&D Intelligence

Infinita Lab

Infinita Lab is a materials testing and R&D intelligence SaaS — fast, accredited testing and expert interpretation for engineering teams across semiconductors, batteries, and advanced materials. A 2,000+ lab network compresses traditional testing lead times into R&D-cycle speed.

R&D TechMaterials ScienceSaaS
Pradyumna Gupta

Chief Executive Officer & Founder

Pradyumna Gupta

Infinita Lab

Founder and Chief Scientist making accredited materials testing radically accessible. Operates a 2,000+ lab network to accelerate R&D in semiconductors, batteries, and advanced materials — compressing months of lead time into engineering-cycle speed.

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BVC ENGAGEMENT

A three-step process inside Infinita Lab

Benchmark, Rebuild, Compound — sequenced deployments, not recommendations.

STEP 1 — BENCHMARK

Quantify the data + ops opportunity

BVC ran the Data Maturity Assessment and GTM Efficiency Audit. Identified proprietary value hidden inside 2,000+ lab network data, mapped leakage in delivery cycle time, and ranked the highest-ROI interventions across revenue, product, and cost — prioritized by EBITDA impact.

Benchmark · Gap ScanData Maturity30%Cycle-Time Efficiency38%Network Data Leverage20%GTM Repeatability52%BVC benchmarkOutput → prioritized remediation map (EBITDA-weighted)
SAASMACHINE™ · INSTALLEDREVENUEENGINEGTM EngineeringEnterprise MotionOutcome PricingCOSTENGINEOffshore SubstitutionAI Report GenVendor CompressionPRODUCTENGINEData Moat BuildAI-Native WorkflowsRetention Loops
STEP 2 — REBUILD

Data Moat + GTM + AI-native delivery

Deployed Data Moat Build — warehouse, governance, and quality discipline converting lab-network data into a proprietary asset. Layered GTM Engineering for a structured enterprise motion and AI-Native Workflows for cycle-time compression on reports. BVC operators embedded inside Infinita, not advisory.

STEP 3 — COMPOUND

Network economics + NRR expansion

Installed metrics infrastructure for unit economics across customer segments and built retention loops tied to R&D workflow embedding — every engagement deepens rather than churns. Margin improved through process automation, and proprietary testing data compounds into a durable structural moat.

NRR · ARR COMPOUNDProduct-paced ↑130%115%100%85%NRR >110% targetBaselineData moatGTM motionRetention loopsCompoundR&D-embedded retention compound

Research

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