Welcome to Attorney Intelligence, where we break down the biggest advancements in AI for legal professionals every week.

Law firms today are still finding their footing when it comes to AI. Most are leaning heavily on one or two vendors, partly because that’s all they feel they can handle from a training, procurement, and compliance perspective.

But beneath that vendor layer is a bigger question: what underlying AI models are powering those tools?

If firms build systems that are model-agnostic, they don’t need to worry about which model will outperform the others. When OpenAI, Anthropic, or Google release something new, they can just plug it in and move on.

However, balancing flexibility with security isn’t simple, especially if adding more vendors means multiplying your firm’s attack surface and operational overhead.

In this week’s Attorney Intelligence, we’ll explore:

1. The pros and cons of using multiple AI models and vendors

2. How firms can build AI systems that are model-agnostic

3. Why firms are making targeted bets on AI startups in different legal domains

4. How firms are adapting to a new decision-making process that resembles venture investing

Let’s dive deeper.

Pros and Cons of the Multi-Model Stack

To summarize, there are really three different choices here:

1. Should firms build tools themselves using base models?

2. Should firms buy from multiple vendors?

3. Should vendors themselves use multiple models?

Some law firms are owning the work from beginning to end and building tools internally using foundation models like OpenAI, Anthropic, or Gemini. In those cases, going multi-model can unlock big advantages. Different models have different strengths: Gemini for long-context reasoning, Claude for structured drafting, OpenAI for generalist performance.

However, each model has its own quirks, and performance can suffer if you don’t customize prompts and infrastructure for each one.

On the vendor side, the best AI vendors are already doing this behind the scenes. They’re mixing and matching models depending on what gets the best results. That’s a huge advantage for firms using vendor products, where they get the benefits of a multi-model strategy without the engineering lift.

But for law firms trying to buy from multiple vendors, the picture changes again. More vendors mean more security risk, more integration work, and more overhead to train people across different platforms. Even if the models behind the scenes are strong, too many tools can overwhelm the firm.

Build Model-Agnostic, Stay Competitive

Specifically speaking, for firms building tools internally the real value exists in staying model-agnostic by designing systems that don’t rely on a single model or provider. This way switching from Claude to GPT-4.5 when performance improves is no issue with a modular infrastructure.

It’s not just about future-proofing. It’s also about performance today. A modular approach lets teams route different tasks to the models that do them best. And as models evolve, you can keep swapping in what’s best, without rebuilding your whole stack.

The same logic applies when evaluating vendors. The most forward-thinking vendors are already integrating multiple models into their products. That makes it easier for firms to get the benefits of a multi-model approach, even if they aren’t building anything themselves.

Why Firms Are Making Targeted Bets

Most firms aren’t going all-in on dozens of tools. They’re making a handful of bets across different categories:

• One tool for timekeeping.

• One for legal research.

• One for contract analysis.

• Maybe one for depositions or medical record extraction.

Once the use case gets niche enough (think patent search or e-discovery) there’s room for more specialized tools.

But firms are still cautious. Each new vendor means onboarding, training, integration, and compliance reviews. Most firms just don’t have the bandwidth to manage ten tools at once, let alone drive adoption across the org.

Betting on Vendors Is Betting Like a VC

Here’s the hard part: choosing vendors today is a lot more like venture investing than traditional procurement. You’re not just buying software - you’re betting on a team, a vision, and a roadmap that hasn’t played out yet.

The flashiest product might not win.

The best-funded startup might not last.

So firms are increasingly relying on innovation teams to do the kind of diligence that used to only happen in boardrooms and venture firms.

There’s a new muscle being built here, one that understands how to place smart bets in a market where the best tool is often still being built.

Final Thought

The firms that win with AI won’t be the ones that made the earliest moves - they’ll be the ones that made the smartest bets, and kept their stack flexible enough to evolve.

A multi-model strategy isn’t just technical optimization. It’s a hedge against uncertainty, a play for performance, and a bet that the pace of innovation isn’t slowing down anytime soon.

Legal bytes

• Filevine acquires deposition AI platform Parrot: Parrot has built some impressive tech for depositions, including real-time transcription, video clips, and AI-generated summaries based on topics or page references. The acquisition lets Filevine tackle three of the most expensive and time-consuming areas in litigation: remote depositions, transcript management, and medical record organization.

• UAE becomes first country to use AI in lawmaking process: The United Arab Emirates has established a Regulatory Intelligence Office to implement AI for writing and reviewing laws. Officials are projecting the system will accelerate lawmaking by up to 70% while reducing associated costs by 50%.

• LexisNexis shares GenAI strategy: In a recent Artificial Lawyer interview, Chief AI Officer Min Chen revealed the company’s GenAI strategy. After testing over 30 AI systems—including OpenAI, Anthropic, and Mistral—they landed on a multi-model approach. This enables them to develop autonomous systems that can self-reflect, reason, and execute complex legal tasks transparently. Future iterations will allow professionals to audit and refine the AI’s reasoning directly within their own environments.

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Thanks for reading and I'll see you next week,

Adrian