Deepgram’s $130 million Series C at a $1.3 billion valuation positions the company as the foundational API platform for the emerging B2B Voice AI economy, with over 1,300 organizations already building voice AI functionality on its real-time infrastructure.

Led by AVP with participation from existing investors including Alkeon, Tiger, Wing, and strategic partners like Twilio, ServiceNow, and SAP, the funding accelerates Deepgram’s mission to become the “Stripe of Voice AI”—delivering the critical infrastructure layer that enables billions of simultaneous voice conversations at human-level naturalness and reliability.

Torq closed a $140 million Series D funding round at a $1.2 billion valuation, marking the cybersecurity platform’s entry into unicorn territory as enterprises accelerate adoption of autonomous AI agents for security operations.

The round positions Torq as the definitive leader in the emerging AI SOC (Security Operations Center) category, where traditional reactive security models are being replaced by agentic AI systems capable of autonomous threat investigation and response at enterprise scale.

Cyera raised $400 million in a Series F funding round at a $9 billion valuation, addressing what the company calls “probably the biggest security hole” in enterprise AI adoption: legacy security models that are fundamentally incompatible with autonomous, intent-driven AI agents.

The funding announced January 8 positions Cyera as the leading player in a nascent but critical infrastructure category—agentic AI security—as enterprises struggle to balance AI acceleration with data protection. Founded by Israeli Military Intelligence veteran Yotam Segev, Cyera has now raised over $1.7 billion while securing 20% of the Fortune 500 as customers.

LMArena raised $150 million in Series A funding at a $1.7 billion valuation, nearly tripling its worth in eight months on the back of surging demand for trustworthy AI model evaluation infrastructure.

The UC Berkeley research project turned commercial platform addresses a critical enterprise bottleneck: how do you know which AI model actually performs best for your specific use case? While lab benchmarks show theoretical capabilities, LMArena’s community of 5+ million monthly users provides real-world performance data that enterprises need for deployment confidence.

Enterprise AI deployments using “context stuffing” anti-patterns cost 100x more per agent run than tool-based architectures—a $0.50 versus $0.005 difference that TrueFoundry’s new MCP Gateway infrastructure aims to eliminate.

TrueFoundry’s announcement positions the Gateway as critical infrastructure for a market realizing that maximum context does not equal maximum intelligence. The platform directly targets the deployment bottlenecks that prevent organizations from scaling agent systems beyond experimental pilots.

Problem: The 100x Cost of the ‘Context Stuffing’ Anti-Pattern

A fundamental architectural flaw emerged from the “Context Window Wars” of 2024-2025: instead of building robust tool integrations, many early agent deployments adopted “context stuffing”—forcing models to process entire datasets in-prompt to find specific information.

BigBear.ai’s $250 million cash acquisition of Ask Sage, completed December 31, 2025, represents the largest defense AI infrastructure investment to date, signaling that secure AI deployment for government and regulated industries has evolved from experimental capability to mission-critical infrastructure requirement.

The acquisition addresses a fundamental bottleneck preventing enterprise AI adoption in sensitive environments: while general-purpose AI platforms prioritize features and user experience, defense and intelligence agencies require purpose-built infrastructure that maintains data sovereignty, supports classified operations, and provides audit-ready governance frameworks that commercial AI tools cannot deliver.

Meta’s agreement to acquire Singapore-based AI agent platform Manus for over $2 billion represents one of the most significant infrastructure moves in enterprise AI this year — completed in just 10 days and demonstrating how quickly the market is shifting from agent development to execution-proven platforms.

The acquisition timeline reveals the urgency: Manus was actively raising capital at a $2 billion valuation when Meta approached, negotiations concluded within 10 days, and the deal marks Meta’s largest AI acquisition to date. This speed signals that major platforms are no longer waiting for agent capabilities to mature — they’re acquiring systems that already demonstrate commercial execution at scale.

QA Wolf secured $36 million in Series B funding led by Scale Venture Partners, bringing total capital to $56 million as the company addresses a critical bottleneck in AI development: comprehensive test coverage that enterprises can actually achieve and maintain.

The timing reflects a breaking point in software testing. While teams struggle to reach even basic coverage thresholds, QA Wolf promises 80% end-to-end test coverage within four months through an AI-native platform that combines autonomous test generation with human verification—a hybrid approach designed to eliminate the flaky results that plague traditional automation.

Artera secured $65 million in growth investment led by Lead Edge Capital while reaching $100 million in Contracted Annual Recurring Revenue (CARR), positioning the company to accelerate agentic AI adoption across healthcare’s $100 billion administrative communication crisis.

The Santa Barbara-based company serves over 1,000 healthcare organizations including specialty groups, FQHCs, and federal agencies, processing more than 2 billion patient-provider communications annually for 200+ million patients through its human-AI coordination platform.

Tidalwave raised $22 million in Series A funding led by Permanent Capital Partners, with strategic investment from homebuilder D.R. Horton, to automate the $1.46 trillion mortgage industry through agentic AI that reduces approval times from 45 days to hours.

Founded by ex-DoubleClick CTO Diane Yu, the company addresses a fundamental infrastructure bottleneck where manual verification processes create anxiety for borrowers and operational inefficiency for lenders in America’s largest consumer lending market.