Enter raised a Series B of $100M+ at a $1.2B post-money valuation in May 2026, led by Founders Fund, with participation from Ribbit Capital, Sequoia Capital, ONEVC, Atlantico, and Kaszek.

Prior to the Series B, Enter closed a Series A of $35M at a valuation above $350M in April 2025, also led by Founders Fund, with Sequoia, ONEVC, Atlantico, and Kaszek participating.

The company's seed round of $5.5M was led by Sequoia Capital, Sequoia's first investment in Brazil in 12 years.

Total disclosed funding across all rounds exceeds $140M.

Enter sells AI software for enterprise mass litigation. Large companies such as banks, airlines, insurers, and fintechs can face tens of thousands of lawsuits per year, and the standard process for handling them is slow, fragmented, and expensive. Its platform, EnterOS, automates the end-to-end workflow for each case rather than focusing narrowly on drafting.

When a new lawsuit arrives, EnterOS connects to the relevant court systems and the client's internal records, pulls the case file, reads the documents, and matches them against the company's own data. For a bank, that can include cross-referencing a consumer complaint against account records and transaction history. For an airline like LATAM, it includes pulling flight data, passenger records, assistance logs, and operational screenshots into a single case view.

The platform then runs more than 30 automated checks for fraud and procedural abuse, including forged proof of address, invalid powers of attorney, deceased plaintiffs, irregular bar registrations, and networks of serial litigants reusing the same documents. This anti-fraud layer is a core part of the product for enterprise defendants in Brazil's high-volume litigation environment.

EnterOS drafts a tailored defense response based on the full case file, the company's internal legal inputs, and patterns from prior rulings in the same court and claim type. A licensed attorney, either in-house or through a partner law firm in Enter's network, reviews and approves the draft before filing. The system flags low-confidence outputs for mandatory human validation rather than routing every matter automatically.

Enter has two domain-specific agent systems on the same architecture. The consumer litigation product covers civil claims across the case lifecycle, from intake through appeal. The labor litigation product adds financial modeling for employment disputes, including worst-case exposure, recommended settlement figures, and hearing briefing packets with witness selection recommendations and claim-by-claim defense strategy drawn from HR records, payroll data, and collective bargaining agreements.

The platform also functions as a closed-loop learning system. Every ruling is captured, analyzed, and fed back into future case strategy, so recommendations improve as the system processes more cases. Enter describes this as a legal data lake combining court data, industry data, and legal-ERP data to identify which arguments and evidence correlate with wins in each court and claim type.

Enter sells to large enterprises on annual B2B contracts priced in relation to lawsuit volume and workflow coverage. The company does not publish a pricing page, but customer disclosures indicate that contracts are large, expansion tracks the share of a client's litigation portfolio running through the platform, and customers with strong outcomes often expand quickly.

The delivery model is deliberately high-touch. Each enterprise deployment involves Enter's engineers, legal strategists, and workflow specialists, plus integration work connecting EnterOS to courts, legal ERPs, HR systems, and internal evidence sources. Nubank's deployment used more than 30 API integrations per case and more than 400 AI models supporting answer generation. That implementation depth creates switching costs and embeds the platform in legal operations rather than leaving it adjacent to existing workflows.

Enter also manages the tradeoff between customization and scalability. The company has set an internal target that deployment costs should represent less than 20% of gross revenue at contract maturity, and that more than 80% of engineering effort should go toward improvements that benefit all customers rather than bespoke client work. Those targets distinguish the model from a legal services business and keep the software layer broad enough to scale.

Expansion is the key structural feature of the model. Once integrated into a client's systems and trusted for one category of claims, Enter can take on a larger share of the litigation book, add new claim types, and extend into adjacent workflows like outside-counsel coordination, reserve management, and pre-litigation prevention. The company's stated goal is to capture a meaningful share of the budget that large enterprises currently allocate to both technology vendors and outside legal service providers in the litigation space, not just the software line item.

Enter competes across three overlapping layers: Brazilian incumbents with installed enterprise relationships, AI-native local challengers with strong localization, and global legal AI platforms moving into workflow automation. The biggest near-term threats are local players with existing procurement relationships and incumbents that can bundle AI into platforms enterprises already use.

Thomson Reuters Legal One is the clearest incumbent threat in enterprise procurement. Its pitch is not litigation AI alone, but a single integrated platform spanning legal operations, contracts, and contentious matters. That lets it sell AI as an add-on inside an existing relationship, rather than asking customers to adopt a new operating model. Thomson Reuters has also been building out CoCounsel, which integrates with Westlaw, Practical Law, and Microsoft 365, giving it a path to pull research, drafting, and workflow orchestration closer to the system of record. The broader legal tech consolidation trend, Thomson Reuters acquiring Casetext for $650M, Clio acquiring vLex for $1B, Workday acquiring Evisort, DocuSign acquiring Lexion, points to buyer preference for fewer vendors spanning more of the legal stack.

eLaw competes more directly on contentious-workflow automation and covers the process from pre-litigation through judicial-decision analysis. Its pre-litigation products capture signals from consumer complaint channels to reduce judicialization before a case becomes formal litigation, which matters because Enter's core value today is strongest once litigation exists. TOTVS Jurídico is a different kind of threat, less a litigation AI rival than a distribution and bundling risk, given that it already serves more than 250 legal departments and can win on procurement inertia and platform standardization.

ForeLegal competes on AI-native legal operations with a different angle: data sanitation and standardization before activating AI, plus total isolation of each client's data. That can resonate in enterprise sales cycles where buyers are concerned about hallucinations or messy legacy data. LISS overlaps directly with Enter's portfolio-management narrative, using big data to reduce costs and financial risk in mass-litigation portfolios, and is better suited to environments where buyers want analytics attached to established legal-ops workflows rather than a new agentic operating model.

These local challengers matter less because they match Enter's product depth today and more because they can win accounts where the buyer's problem is data readiness and process transformation rather than pure case throughput, then expand from there.

Harvey is used by top law firms and in-house teams and is building litigation-specific workflow agents. It is not purpose-built for Brazilian mass litigation operations, but it threatens Enter in two ways: a GC may choose one broad legal AI platform for many workflows, and as Harvey becomes standard inside law firms, outside counsel may internalize more of the AI layer that Enter currently provides through its partner-firm review model. Harvey has raised roughly $1B and was estimated at $190M ARR, giving it the capital to localize aggressively if it chooses.

Relativity and Everlaw are expanding from document review into broader litigation workflows, and platforms like Litify and Filevine are rebuilding case-management systems as action systems rather than record systems. None of these are Brazil-first mass-litigation products today, but they narrow the gap between a legal ops platform and an AI litigation execution platform, the territory Enter is trying to own.

Enter's stated ambition is to become the operating system for legal departments globally, starting from Brazil's dense litigation environment as a proving ground. The expansion logic runs in three directions: deeper into the enterprise legal stack, broader across geographies, and upstream into pre-litigation workflows.

Enter already covers the case lifecycle from intake through appeal, but the most valuable adjacent product area is the decision layer above workflow. Settlement modeling, reserve management, and litigation-budget forecasting are capabilities Enter has begun building into both its consumer and labor products, aimed at a clear buyer need: GCs are under pressure to show business impact, not just process efficiency. Moving from workflow automation into legal-finance tooling would let Enter tap budget from finance and risk teams, not just legal operations.

Outside-counsel orchestration is another extension. Enter's current model keeps a partner law firm in the loop to audit filings before submission, creating a wedge into the handoff between in-house legal teams and external firms. Products for panel management, playbook enforcement, performance benchmarking, and fee optimization would let Enter address more of the outside-counsel spend that represents a large share of enterprise legal budgets, the same budget pool that Brightflag, acquired by Wolters Kluwer, and Onit have targeted from the legal spend management side.

Enter's current customer base is concentrated in very large enterprises with severe litigation volume. The near-term expansion opportunity within Brazil is to move into the broader in-house legal market, companies with meaningful but not extreme litigation exposure that still benefit from workflow automation, fraud detection, and outcome analytics. The verticals where Enter already has traction, financial services, airlines, insurers, and large technology companies, are attractive because the product improves as it ingests more structured evidence and court outcomes, making multi-product expansion within each customer more plausible over time.

The labor litigation product also opens a path to adjacent buyer personas inside the same enterprise. Labor disputes pull in HR systems and payroll records, which means Enter can sell beyond the legal department into HR, compliance, and risk teams, especially if it can show that litigation intelligence reduces claims volume or improves policy design upstream.

Brazil alone entered 2026 with roughly 75 million pending cases, and the judiciary has been prioritizing technology and anti-abusive-litigation programs to manage that scale. That backdrop gives Enter a dense proving ground, but management has been explicit that the ambition extends beyond Brazil. LATAM is the most logical next region: other civil-law jurisdictions with concentrated enterprise litigation share enough structural similarities with Brazil that the core playbook, high-volume intake, court integration, fraud detection, evidence enrichment, and hearing prep, can be adapted without rebuilding from scratch.

International expansion beyond LATAM is harder because each new jurisdiction requires legal ontology, workflow, and distribution rebuilt for local procedural norms. Enter's partner-law-firm model is a useful mechanism here: pairing software with jurisdiction-specific legal review reduces unauthorized-practice risk and provides local procedural expertise, which is how the company can enter new markets without hiring local legal teams at scale before revenue materializes.

Localization depth: Enter's product is tuned to Brazil's litigation environment, including local court integrations, procedural workflows, fraud patterns, and labor-specific data structures, which means expansion into each new jurisdiction requires rebuilding legal ontology, court connectivity, and workflow logic from the ground up, making international expansion slower and more capital-intensive than a platform-scale narrative suggests.

Platform consolidation: Incumbents like Thomson Reuters, eLaw, and TOTVS can sell AI into existing enterprise relationships, and if GCs move toward fewer vendors spanning the full legal stack, as the broader consolidation trend in legal tech suggests, Enter may need to either integrate tightly with incumbents or expand its product surface faster than its current capital and team can support.

Human-review dependency: Because Enter's outputs are reviewed by partner law firms or licensed attorneys before filing, the model retains labor costs that limit gross margin expansion toward pure-software levels, and if that partner-firm review layer becomes replicable by outside counsel using their own AI stacks from platforms like Harvey, Enter's hybrid architecture could face disintermediation from both ends of the workflow simultaneously.