Technical Program Manager

Harvey, the AI platform for law firms and legal teams, has integrated DeepL to power its document translation capabilities. DeepL will handle over a third of Harvey's total translation volume, supporting more than 100 languages directly within the Harvey platform. Harvey serves more than 200,000 lawyers across 2,400 organisations in 70 countries. The integration enables legal teams to upload documents, select a target language, and receive translations that preserve original structure and formatting without leaving the Harvey platform. DeepL was selected for its ability to maintain complex legal document formatting whilst delivering context-aware translations. The platform offers custom glossaries, extensive file-format support, and enterprise-grade security features including GDPR compliance, SOC 2 Type II, and ISO 27001 certification. DeepL's platform is used by over 200,000 business teams globally, with nearly 50% of the Fortune 500 amongst its users.

Harvey Tenet: why enterprise AI apps must own their models. On August 18, 2026, legal AI company Harvey crossed a line that most enterprise AI software vendors have not reached: it released its first proprietary large language model. Harvey Tenet, post-trained on the open-source Kimi K3 foundation using years of legal reasoning data, marks the transition from Harvey as an API integrator to Harvey as a model owner. The shift carries consequences well beyond one legal tech company. The model launch is part of a broader platform release Harvey calls Harvey II, which also includes persistent Memory across matters, a redesigned Spaces interface, and updated agent workflows. Together, these changes are the clearest signal yet that vertical enterprise AI companies are entering a new strategic phase. The wrapper problem at scale. Harvey built an $11 billion legal software business by combining models from OpenAI, Anthropic, and others into a platform that helps lawyers accelerate document review, drafting, and research. Each query routes through a third-party API call billed per token. At small usage volumes, that economics holds. At the scale Harvey now operates, the math turns. Every new lawyer seat, every additional matter, every background agent task adds to the external model bill. A software company paying a per-token tax on its own revenue has a structural ceiling on gross margin that a model owner does not. Tenet is Harvey's answer. According to the official announcement, Tenet is "frontier-level on prominent legal benchmarks, performing on par with the strongest general models at an open-source cost." That pairing, frontier accuracy on domain benchmarks at open-source inference cost, is the business thesis in a single sentence. Harvey is not claiming Tenet outperforms GPT-5.6 Sol on general reasoning. It is claiming Tenet matches the best general models on legal reasoning, which is the only dimension that matters for Harvey's product, at a fraction of the token price. How Tenet was built. Harvey trained Tenet on Kimi K3, the open-weight frontier model released by Moonshot AI in July 2026. Kimi K3 attracted enterprise interest precisely because it offered competitive performance under a permissive license, making it viable as a base for proprietary fine-tuning without the IP friction of building on closed models. Harvey added its proprietary dataset, built by lawyers who generate and evaluate outputs, to post-train Tenet for legal reasoning end-to-end. That dataset is the actual moat. Any company can download Kimi K3. Only Harvey has years of structured legal reasoning data tagged by practicing attorneys. This is the architecture that makes vertical AI defensible at scale: an open-weight foundation plus proprietary domain data. The foundation manages training cost. The data creates the differentiation that cannot be cheaply replicated. It also removes a dependency risk. Harvey no longer needs OpenAI or Anthropic to keep Tenet running on core legal workflows. Those frontier API relationships can continue for tasks where general capability matters, but the critical path through Harvey's product no longer routes exclusively through a supplier that can change pricing, access terms, or model behavior unilaterally. Harvey II: memory, Spaces, and continuous context. Beyond Tenet, Harvey II solves a problem that has frustrated enterprise AI deployments since the first wave of agentic tools: agents that begin every task without memory of previous work. Memory carries a lawyer's drafting style, citation preferences, matter history, and approved best practices across tasks. Phase one serves individual users. Phase two, rolling out over the following months, extends memory across Spaces and team matters. Phase three allows firm-wide governance of memory boundaries, including what gets stored and what remains scoped to individual matters. Spaces creates a matter-centric shared environment where documents, tasks, permissions, and history stay attached to the work as it moves between agents and lawyers. Rather than a single prompt-response session, Spaces is designed to support continuous multi-session workflows across an entire matter lifecycle. According to Harvey's CPO Anique Drumright, Memory came directly from lawyers describing how much time they wasted re-explaining preferences to an AI that had forgotten everything since the last session. "Instead of repeatedly explaining those preferences or spending time reshaping outputs afterward, Harvey can start closer to the way that lawyer actually works." | Harvey II Feature | What It Does | Enterprise Relevance | | Memory (Phase 1) | Stores individual style, tone, citation preferences | Reduces per-task setup overhead | | Memory (Phase 2) | Extends across Spaces and shared matters | Enables continuity on long-running projects | | Memory (Phase 3) | Firm-wide memory governance and boundary controls | Addresses compliance and confidentiality | | Spaces | Matter-centric environment with persistent history | Replaces scattered document and prompt workflows | | Harvey Tenet | Proprietary legal model at open-source inference cost | Removes per-token external cost on core workflows | What this means for enterprise AI strategy. Harvey's move illustrates a transition that many enterprise AI companies will face in the next 12 to 24 months. The dynamic follows a recognizable pattern. Phase 1 (Wrapper). The company integrates frontier model APIs to ship a vertical product quickly. Margins are acceptable because usage is modest and the product value justifies the token cost. Phase 2 (Scale pressure). As usage grows, the per-token cost becomes a meaningful line in the P&L. The company finds itself paying a supplier tax on every dollar of revenue. Phase 3 (Model ownership). The company fine-tunes an open-weight model on its proprietary data. External API calls shift to tasks where general capability genuinely matters; core workflows route through the internal engine. Harvey is entering Phase 3. The timing is notable: at an $11 billion valuation, not at inception. The capital and data required to reach Phase 3 are only available to a company that has already proven market fit and accumulated substantial domain data. Earlier-stage vertical AI companies should treat the Harvey Tenet launch as a milestone that defines what they are building toward. Harvey's longer-term vision goes further. CEO Winston Weinberg has described Tenet as a potential "building block" that law firms use to train their own models shaped by their own legal work. If that materializes, Harvey moves from a software supplier to a model foundry for the legal industry: individual firm models, each differentiated by proprietary matter data, with Harvey providing the common base and infrastructure layer underneath. That is a very different business than an API wrapper, and it is the direction the enterprise AI software market is heading. The companies that reach that destination will have done so because they accumulated irreplaceable domain data while others were focused solely on the interface layer. For enterprise teams assessing vertical AI vendors, the question is no longer whether a vendor uses frontier models. The question is whether the vendor is building its own data flywheel and model ownership path. Vendors with no answer to that question are structurally exposed to margin compression and supplier dependency as their usage scales. Understanding model strategy at the platform level is one of the decisions that separates AI-native enterprises from those still experimenting. For teams navigating that transition, Enera works with enterprise buyers and operators to build durable AI capabilities that do not rent their core intelligence from a changing market.