What would a law firm need to run its own AI?

Why this conversation is urgent

In 2023, a New York attorney filed a brief in federal court citing six judicial cases. Not one of them existed. They had all been fabricated by ChatGPT. The court sanctioned the attorney and his firm. Since then, bar associations across multiple jurisdictions have issued ethical guidelines requiring lawyers to verify any content generated by AI.

The problem is that verification without source traceability does not scale.

But there is a less-discussed risk that can be just as paralyzing: dependence on a single provider.

In April 2026, a Latin American fintech lost access to more than 60 accounts on the AI model that powered its operation. The provider closed them automatically over an alleged policy violation. There was no meaningful prior notice, no human support channel, no clear appeal process. The company was down for hours and had to perform an emergency migration to an alternative model just to keep operating. It was later determined to have been a false positive — but the operational damage was already done.

This did not happen to a two-person startup. It happened to a company with dozens of users depending on a critical system. And it can happen to any organization whose entire AI operation is tied to a single provider. A law firm running its document management, contract analysis, or case-law review on a single AI model is in exactly the same position. One day the provider cuts off your access, changes its terms of service, raises prices, or simply goes down — and your operation stops. No data, no answers, no alternative.

That incident exposed something many companies prefer not to think about: if the system is not yours, your operation is not yours either.

These are the underlying problems holding back serious AI adoption in the legal industry.

The real problem: AI is already inside your firm. It is just that nobody is controlling it.

In most law firms today, AI is already in use. The question is how. An associate uses a personal ChatGPT account to summarize a contract. A paralegal pastes sensitive clauses into a free plan to ask the AI for alternative wording. A partner drops a draft litigation strategy into an online tool to "review the tone."

Each of those actions sends confidential client information to third-party servers, under terms of service no one at the firm read, through accounts the firm does not administer or audit. Free plans are particularly risky: many providers explicitly use them to train their models on the data users enter. Privileged client information may be feeding the very model your competitor uses tomorrow.

Some firms have tried to solve this by buying Enterprise plans from providers like OpenAI or Anthropic. It is a step in the right direction, but it has its own problems:

• Enterprise plans run on token quotas.
• When a team is in the middle of a heavy document review or wrapping up an M&A close and the quota runs out, work stops.
• There is no way to keep going until the billing cycle resets access.
• The workflow breaks exactly when it is needed most.

And worst of all: when that happens, what does the lawyer with a deadline do? They go back to their personal account. Back to the free plan. And the data exposure cycle starts all over again.

The underlying problem is not which AI tool your team uses. It is that there is no single, centralized place — controlled by the firm — where all AI use happens under the firm's rules, with the firm's controls, and with the visibility the firm needs.

That is exactly what Novux implements.

Beyond exposure: the three structural problems

The lack of centralization is the immediate problem, but underneath there are three deeper challenges holding back serious AI adoption in the legal industry:

Hallucinations are a real risk

AI-generated content that cites non-existent cases or misrepresents rulings exposes the firm to sanctions, professional negligence claims, and reputational damage. Lawyers need traceability: the ability to verify every assertion against a source document.

Client data on cloud-hosted models can create privilege and confidentiality risk

Sending case materials to cloud AI providers raises questions about waiver of attorney-client privilege. In Chile, Law 19.628 on the Protection of Private Life, along with ongoing updates to personal-data regulations, makes this concern increasingly concrete. Some firms handle sensitive litigation, mergers and acquisitions, or regulatory investigations where reducing third-party exposure is not a preference — it is an operational necessity.

Compliance requirements keep multiplying

Disclosure rules around AI use, data-protection regulations for international client work, and internal governance obligations increasingly demand auditable, controllable AI infrastructure — not opaque handling of information.

These three problems, combined with the reality that your team is already using AI on its own without supervision, share a single solution: a centralized AI system that the firm controls, observes, and validates internally. One place where every piece of AI used for work runs under your rules.

What it would actually take to run your own AI

There is no shortage of legal AI products on the market. Many are sophisticated, well-funded, and easy to adopt. The strategic question is a different one: control. Where the data is processed, who can access it, and how confidently you can demonstrate that to your clients, courts, and regulators.

And there is an equally important practical question: can your team work without interruptions?

If your current solution depends on the token quota of an external provider, the answer is no. With your own system, capacity is defined by you. There are no artificial limits, no cuts in the middle of a project, no dependence on someone else's billing cycles. And if a model provider goes down or cuts off your access, your system switches to another model and your team keeps working. That is Multi AI.

If a law firm decided to explore its own AI, what capabilities would matter? The requirements tend to cluster into five areas:

Centralization of AI use

A single point of access where the entire team — from partners to paralegals — uses AI under firm policy. No more personal accounts, free plans, or information scattered across platforms nobody administers. Everything in one system, under the rules the firm defines.

Data localization

The ability to run AI entirely on infrastructure controlled by the firm — on-premise, in a private cloud, or air-gapped. With the right configuration, this reduces third-party data exposure, limits the risk of models training on your information, and avoids external API calls for inference.

Source-grounded answers with citations

The ability for lawyers to query the firm's briefs, precedents, statutes, and internal memos and receive answers with inline citations and relevance scores. This does not eliminate hallucinations, but it improves traceability for verification workflows.

Group-based access control

The ability to map role permissions to practice groups, restrict administrators from seeing certain conversations, and control access to models, documents, and features by group.

Configurable audit and retention controls

Conversation-retention controls, configurable logging, SSO integration, and restrictions on chat deletion that support the firm's governance and audit requirements.

These are not exclusive to any single provider. They are the criteria firms exploring their own AI tend to evaluate. And they are exactly the kind of system Novux designs and implements.

How Novux builds it for your firm

Novux does not sell you a software license and wish you luck. The Novux team sits down with your firm, understands your processes, your practice areas, your existing infrastructure, and your compliance requirements — and from that, designs, programs, and deploys a Multi AI system tailored to you. This applies equally to a 5-lawyer boutique and a 200-lawyer firm. The architecture scales, the controls adapt, and the implementation matches the size and complexity of each organization.

The result is yours. It runs on your infrastructure. And it is configured according to your rules.

An illustrative example

Imagine a law firm in Santiago hires Novux to implement a Multi AI system with its internal document library. An associate preparing a brief enters a question in the platform. The answer is built from the firm's briefs and cites the specific documents used, with relevance scores for each source. The associate clicks each citation to verify it against the original. The conversation is logged under their user account for search and audit. When the deployment is configured to avoid external connections, the data stays inside the systems controlled by the firm.

For a partner reviewing the associate's work, the conversation log shows which queries were made, which sources were cited, and when. That level of traceability is exactly what ethical guidelines and internal AI governance increasingly demand.

And this is where Multi AI makes the difference: that same associate can use a lightweight model to summarize case law quickly, switch to a heavier model to analyze a complex M&A contract, and then use a model specialized in English to review clauses in a cross-border contract. All from the same interface. Without migrating data. Without losing context. Without depending on a single provider having the best solution for every task. And without the system telling them, mid-review, that the monthly quota is up.

Novux makes sure all of that works. Your team just uses it.

What access control looks like

Part of what Novux configures for each client is a group-based access-control system. Below is an example of how a firm could map its practice groups to AI capabilities. This is an illustrative configuration: Novux designs the actual structure together with each firm based on their needs, risk tolerance, and governance requirements.

Litigation

Full AI capabilities. Access to case law, briefs, and discovery templates. Web search enabled.

Corporate / M&A

Full AI capabilities. Access to deal templates, regulatory filings, and due-diligence checklists. Document extraction enabled to pull structured data from contracts and filings.

Intellectual Property

Full AI capabilities. Access to patent databases and prosecution templates. Code interpreter enabled for analysis scripts on patent-claim data.

Tax

Advanced analysis only. Access to tax regulations, SII case law, and the firm's tax opinions. RAG mode only, with answers grounded in firm documents.

Paralegals / Administrative staff

Basic tasks only. Access to internal procedures and HR policies. No file upload, no web search.

Groups can be synchronized with the firm's identity provider (Okta, Azure AD, Google Workspace) via OAuth, so practice-group membership stays aligned with the internal directory.

What infrastructure is needed

This section is a reference for your IT or engineering team. If you are evaluating at a strategic level, the takeaway is simple: the systems Novux implements can run on existing infrastructure (VMware, Azure, AWS, or bare metal), scale with the firm, and deploy with minimal external dependencies.

For large firms with 200 to 1,000+ lawyers, a production deployment typically requires high availability and data isolation. For smaller firms, the architecture simplifies significantly and can run on a single server. Key design decisions include:

Stateless application nodes

Horizontal scaling lets capacity track demand across the firm.

Local inference

Via Ollama for lightweight models and vLLM for large GPU-optimized models, so prompts stay inside your network when configured that way.

Unified data layer

PostgreSQL handles both application data and vector search, reducing operational complexity.

Session coordination with Redis

Enables multi-node deployments where any instance can transparently handle any request.

Novux owns the design, deployment, and maintenance of this entire architecture. Your IT team participates in the decisions but does not have to build it from scratch.

Things to consider before starting

Implementing internal AI is not trivial. Before committing, every firm should consider:

Infrastructure cost

GPU servers, storage, and networking all cost money. A pilot with a single practice group can run on one GPU server. A firm-wide deployment involves dedicated compute and storage. Novux can help size this before any investment.

Governance design

Who approves AI use cases? How are outputs reviewed? What is the policy for AI-assisted professional work? These questions matter more than the technology. Novux works with each firm to define these rules as part of the implementation process.

Validation and testing

Any AI deployment should go through security review, governance-control design, and integration testing before going to production. This typically takes several weeks.

Ongoing maintenance

Model updates, security patches, user support, and knowledge-base curation are permanent responsibilities. Novux offers ongoing support plans for firms that prefer not to take this on internally.