Legora's Agentic OS for Legal Work: AI Automation at Scale

Introduction

Stockholm-based Legora, founded in 2023, is building what CEO Max Junestrand describes as "the agentic operating system for legal work." The platform integrates OpenAI models—including GPT-3.5, ChatGPT, and Codex—directly into the daily workflows of legal teams, aiming to shift lawyers from high-volume, low-value document review toward strategic, higher-value work. For IT, security, and engineering leaders, Legora's trajectory offers a concrete case study in deploying agentic AI at enterprise scale: the company itself uses coding agents to compress bug-to-fix cycles to roughly five minutes, while its customers report measurable reductions in external spend and dramatic expansions in analytical coverage.

Technical Architecture: Models, Agents, and the Plugin Layer

Legora's product stack rests on OpenAI as a key model provider, with the founding team citing the release of GPT-3.5 as the inflection point that made their envisioned legal use cases viable. Today, the platform orchestrates multiple model capabilities across core workflows: agentic legal reasoning, large-scale document review, and a Tabular Review workflow that can analyze thousands of contracts across multiple questions in minutes. A Legora plugin for ChatGPT further embeds these capabilities inside the conversational interface many knowledge workers already use daily.

Internally, Legora's engineering team leverages Codex and coding agents to explore up to 20 solution prototypes in parallel, selecting the optimal approach within days rather than committing to a single research path. When incidents arise, agents search millions of log lines and pursue multiple root-cause hypotheses simultaneously, collapsing diagnosis and remediation from hours to approximately five minutes. This internal velocity translates externally: faster defect resolution, earlier customer feedback loops, and accelerated feature delivery.

Operational Impact: From Legal Team to Enterprise Scale

The most instructive deployment may be Legora's own legal department. General Counsel Emma Kadri Bergström's team built a playbook from historical NDAs, enabling the platform to perform a first-pass review that flags terms outside acceptable parameters. Before automation, NDA review consumed roughly 20% of the legal team's capacity; today it accounts for under 1%, a 95%+ reduction. That reclaimed bandwidth proved critical as the company scaled from three offices to 18, completed three financing rounds, and executed five acquisitions in a single year.

Beyond efficiency, the team now handles M&A diligence in-house that would previously have been outsourced or narrowed. Agents surface relevant clauses across entire deal sets, with lawyers interpreting and validating the output. As Kadri Bergström notes, "AI doesn't just make you faster. It also increases quality."

Customer Outcomes: Measurable Expansion of Coverage

For external customers, the value proposition extends beyond speed. Global Head of Legal Engineering Alex Fortescue-Webb highlights due diligence: traditional time and cost constraints force deep review of only the highest-value contracts, leaving risk in the long tail unexamined. Legora's Tabular Review applies detailed scrutiny across the full corpus, surfacing risks that would otherwise remain hidden. One Fortune 100 customer reported:

• Over $20 million saved on external litigation and diligence spend
• 90% faster contract turnaround
• 9.5 hours returned per lawyer per week for higher-value work

These figures suggest that agentic AI can simultaneously reduce cost, accelerate cycle time, and broaden risk coverage—a combination that challenges the traditional iron triangle of legal operations.

Security, Trust, and Adoption Considerations for Enterprises

Legal work is defined by confidentiality, privilege, and regulatory sensitivity. While the source material emphasizes trust as a scarce resource in the profession, it does not disclose specific security certifications (SOC 2, ISO 27001), data residency controls, or model governance frameworks employed by Legora. Engineering and security leaders evaluating similar agentic platforms should demand clarity on:

• Data handling: whether customer documents are used for model training, fine-tuning, or retained beyond the inference request
• Access controls: role-based permissions, audit logging, and integration with existing identity providers
• Model governance: version pinning, evaluation pipelines for hallucination rates on legal reasoning tasks, and fallback mechanisms when agent outputs fall below confidence thresholds
• Supply chain: the extent of dependence on a single model provider (OpenAI) and contingency plans for API deprecation or policy changes

Until such details are public, treat them as open requirements in any procurement process.

What Security and Engineering Teams Should Do Now

If your organization is exploring agentic AI for document-intensive workflows—legal, compliance, procurement, or contracts—take these steps:

1. Map the workflow end-to-end. Identify high-volume, repeatable review tasks (NDAs, vendor agreements, regulatory filings) where a playbook-driven first pass could reclaim significant capacity.
2. Run a controlled pilot. Use a representative document corpus with known ground truth to measure precision, recall, and time-to-decision against current manual baselines.
3. Define acceptance criteria for agent outputs. Establish confidence thresholds, human-in-the-loop checkpoints, and escalation paths for edge cases.
4. Assess vendor security posture. Request penetration test summaries, data processing agreements, and evidence of secure software development lifecycle (SSDLC) practices—including how the vendor secures its own coding agent pipeline.
5. Plan for model portability. Architect prompts, evaluation harnesses, and orchestration logic to be model-agnostic, reducing lock-in risk.

Conclusion

Legora's progress illustrates how agentic AI can restructure knowledge work by expanding the scope of what teams can review, not merely accelerating existing processes. The 95% reduction in NDA review time and the $20M+ savings for a single enterprise customer signal that the technology has moved beyond pilot into production-grade impact. For security and engineering leaders, the imperative is to evaluate these platforms with the same scrutiny applied to any critical infrastructure—demanding transparency on data governance, model risk, and operational resilience. Grivyonx helps organizations embed these controls into AI-enabled pipelines through automated DevSecOps and cloud security services that keep pace with agentic development velocity.