Build vs Buy: Adding AI to Your Legal Services Business

A strategic guide for legal service organizations considering AI integration

If you’re running a legal services business, you’ve likely noticed the growing demand for AI-powered document processing and analysis. Your clients want faster insights from case documents, and competitors are starting to offer AI-enhanced services. The question isn’t whether to add AI capabilities, it’s how to do it strategically.

1. Start with Your Strategic Objectives

Before diving into the technical details, you need to clarify what success looks like for your business:

• Time to market: How quickly do you need to launch AI-enhanced capabilities to stay competitive with other legal service providers?
• Core competency focus: Your strength lies in efficient, compliant legal services. Do you really want to divert your R&D resources into AI model training and development?
• Long-term differentiation: Will AI document processing and analysis become a key differentiator for your business, or will it quickly become a commoditized “table-stakes” feature that everyone expects?

These strategic questions will guide every tactical decision that follows.

2. The Build vs Buy Trade-offs

Every company faces different constraints and opportunities. Here’s how the key factors typically break down:

Important cost reality: Unlike traditional cloud infrastructure where marginal costs approach zero after setup, AI document processing will always have per-page token costs (typically $0.02-0.05 per page). Depending on your vendor’s markup, the true cost difference between building vs buying may be smaller than you think—especially when factoring in engineering time and ongoing maintenance.

Factor	Build In-House	Partner with AI Vendor
Speed to Market	Longer ramp-up time (hiring talent, data preparation, model training)	Fast integration via vendor platforms and tools
Initial Investment	High upfront costs (specialized talent + infrastructure)	Predictable ongoing fees; typically lower upfront investment
Control & Customization	Complete control over data pipelines and model adjustments	Limited to vendor roadmaps; custom features may require special contracts
Required Expertise	Must recruit ML engineers and data scientists	Leverage the vendor’s specialized AI team
Ongoing Maintenance	You own all updates, bug fixes, and compliance responsibilities	Vendor handles upgrades, scaling, and compliance updates
Data Security	Complete visibility and audit trail (but you bear the full compliance burden)	Vendor must meet your security standards (requires thorough vetting)
Scalability	Scales with your investment; risk of performance bottlenecks	Typically elastic “pay-as-you-grow” scaling
Vendor Dependency	No external dependencies	Platform dependency creates potential switching costs

3. Our Recommended Approach: Start with a Strategic Pilot

Rather than making an all-or-nothing decision, we recommend a phased approach that minimizes risk while maximizing learning:

Phase 1: Launch a Vendor-Powered Pilot

Why this approach works: You can rapidly roll out a proof-of-value to clients, gather real usage metrics, and refine your user experience without heavy upfront investment in AI talent and infrastructure.

How to execute:

• Select 2-3 AI providers that offer secure, compliant document processing
• Integrate their platforms into your workflow on a small scale (start with 100-500 files per week)
• Run side-by-side comparisons: have your current manual process run parallel to the AI solution on the same cases
• Measure key metrics: accuracy, processing speed, cost per file, and most importantly, client satisfaction

Expect an adjustment period: Most AI vendors need a few weeks to customize their systems for your specific document types and quality standards, so factor this into your timeline.

Phase 2: Analyze ROI and Identify Gaps

Use your pilot data to quantify summary usage against your case volume and revenue impact. Look for specific gaps where a custom in-house model might provide meaningful differentiation, such as handling your industry’s specialized terminology or supporting proprietary document workflows.

Phase 3: Make Your Long-term Decision

Continue with your vendor partner if:

• The integration remains cost-effective as you scale
• Vendor roadmaps continue to align with your client needs
• You prefer to focus entirely on your core strength: legal service innovation

Begin gradual in-house development if:

• You identify clear product differentiation opportunities (like specialized processing tailored to your specific legal workflows)
• You can amortize the ML investment across a growing user base
• You have the ability to hire and retain the necessary AI expertise

4. Key Vendor Selection Criteria

If you decide to start with a vendor partnership, here are the essential factors to evaluate:

• Data handling and compliance: Ensure encrypted data pipelines, SOC-2 certification, and data residency options that meet your requirements
• Platform performance: Test processing speed, throughput limits, and reliability under realistic load conditions
• Quality assurance process: Understand their human verification workflows. Most reliable AI vendors still use human reviewers for quality control.
• Customization capabilities: Understand their ability to incorporate your training data or fine-tune models for your specific use cases
• Scalability planning: Ensure they can handle capacity increases with just a few weeks’ notice as your volume grows
• Transparent pricing: Compare per-document vs. subscription models, and understand tiered pricing for volume scaling
• Support and reliability: Review uptime guarantees, response times, and availability of dedicated account management

5. Strategic Business Considerations

Competitive positioning: Record indexing, summaries, and analysis are increasingly becoming table stakes for winning larger enterprise contracts. Even if the technology isn’t your core differentiator, lacking these capabilities can eliminate you from RFP processes with major clients.

Revenue diversification: AI-powered services can create new revenue streams with existing clients, especially when their primary service contracts are locked with competitors but ancillary services remain flexible.

Pro tip: Don’t just evaluate vendors on their demo performance. Run a real pilot with your actual document types and volumes to understand how their solution performs with your specific workflow and compliance requirements.

Real-world insight: Many companies we’ve spoken with have tried building AI document processing in-house first, only to discover the complexity and ongoing maintenance burden was far greater than expected. They eventually became external partners after months of internal development struggles.

The Bottom Line

Start with an external AI partner to validate market demand and refine your product strategy. Once you’ve gathered real usage data and identified specific differentiation opportunities, you’ll be in a much stronger position to decide whether—and when—to bring AI model development in-house.

This phased approach allows you to minimize risk, manage costs effectively, and maintain your focus on what you do best: delivering reliable, compliant legal services. The AI capabilities become an enhancement to your core strength, not a distraction from it.