AI Engineering in Legal Practice

Eleanor Berger enables AI engineering teams and leaders, helping companies grow AI engineering capacity and build effective AI systems. Hadi Khan engineers AI applications for startups & enterprises, specializing in back office automation. Together, they explore why legal AI represents one of the biggest opportunities in AI engineering today.

The $1,000,000,000,000 Problem Hiding in Plain Sight

Legal services represent a trillion dollar global market built on an uncomfortable truth: most legal work involves processing vast amounts of text to find patterns, precedents, and anomalies. Sound familiar?

This is exactly what large language models excel at. Yet while engineering teams rush to build chatbots and content generators, they’re missing the most lucrative application of AI technology sitting right in front of them.

Hadi discovered this firsthand during a family property dispute that required reading thousands of pages of legal documents. “Legal consultation was expensive, doubtful, and gave me no deliverables,” he explains. “No transcripts, no clear plan of action, and if I needed urgent advice, I had to schedule a slot and wait.” The friction was enormous, especially for time-sensitive situations like domestic violence cases where immediate advice could be critical.

His solution was simple: feed legal documents into ChatGPT and get immediate, structured answers. What started as scratching his own itch revealed something profound about the intersection of law and technology.

Eleanor had the same revelation. “I take all the documents my lawyer gives me and feed them into ChatGPT. It explains the gotchas, clarifies complex terms, and helps me understand what I actually need to pay attention to.”

Both experts recognised something fundamental: legal work is programming with words instead of code.

Why Legal AI Isn’t Just Another Vertical

Legal technology sits at the intersection of three powerful trends that make it irresistible for AI engineers:

First, the natural fit is obvious. Legal work processes enormous amounts of text to extract structure, patterns, and meaning. “Legal is a lot of text, and AI models are designed to handle a lot of text,” Hadi notes. “Programming is also a lot of text. There’s an inherent overlap.”

This isn’t superficial pattern matching. Law operates through complex logical structures, precedent analysis, and rule-based reasoning — exactly the capabilities that make engineers effective. The difference is that legal documents express these structures through natural language rather than code syntax.

Second, the market pull is unprecedented. Sales cycles that once took six to nine months for B2B software now complete in weeks. “For small-ticket AI solutions in the thousands of dollars,, it’s literally 30 minutes — just one call,” Hadi reports. “For high-ticket work at $100k or more, it’s down to one month.”

The disruption is so fundamental that traditional sales processes can’t keep pace. Law firms see immediate value because they understand text processing challenges intimately. Unlike other industries that need education about AI’s potential, legal professionals immediately grasp what automated document analysis could mean for their practice.

Third, software engineers have an unfair advantage. While domain experts struggle with manual AI tools, engineers can build automated, evaluated, and reliable systems. “Software engineers are superior at exploiting AI because they can code, automate, and create reusable workflows,” Eleanor observes. “They become 10x, 100x more effective.”

This advantage compounds over time. Manual AI workflows require constant human intervention and produce inconsistent results. Engineered solutions improve through iteration, scale without linear cost increases, and deliver predictable outcomes.

The New Reality: AI Engineers Can Do Anything

The most striking insight from both experts is how AI reshapes professional boundaries. “Most jobs can now be done by software engineers,” Hadi argues. “AI is not going to replace you yet, but a person developing AI will.”

This creates a new professional category: domain-specific AI engineers. Think “Legal AI Engineer” or “Investment Research AI Engineer” — specialists who combine engineering skills with deep understanding of specific industries.

The transformation extends far beyond legal. “Any back-office automation, research, and reports — anything that can be done on the internet using human intellect — that’s what AI engineers can now do using AI agents,” Hadi explains.

Consider the workflow Hadi describes: Research → Scrape/Sort/Classify → Analyse/Make memory → Implement → Review. This pattern applies across knowledge work industries. The ability to process thousands of documents beyond human capabilities while delivering consistent reports creates immediate competitive advantages.

But the shift isn’t just about capability — it’s about value creation. “The relative value I could bring to legacy business versus tech startups was an enormous difference,” Hadi notes. Traditional firms have established revenue streams, proven business models, and clear pain points that AI can address immediately.

What Actually Works: The 90% Rule

Both Eleanor and Hadi emphasise focusing on the most common workflows rather than edge cases. “There are four or five queries that are the most common,” Hadi explains. “If you solve for these, you get 90% accuracy. The edge cases can be handled separately or classified for manual review.”

This approach recognises a fundamental principle: perfect is the enemy of good. Law firms don’t need systems that handle every possible scenario flawlessly. They need reliable automation for their most common work streams, with clear pathways for exceptions.

Eleanor sees this pattern across legal clients: “The cost of handling an exception isn’t very high once you’ve automated the bread-and-butter work. You get an immediate return on investment.”

The winning workflows consistently include:

• Document review and analysis for due diligence
• Contract comparison and anomaly detection
• Legal research and case precedent analysis
• Compliance checking and risk assessment
• Settlement prediction and case outcome modelling
• Simulation, profiling and court preparation

The key insight is building these as reliable, automated systems rather than manual chatbot interactions. “You need engineers to figure these workflows out through iterative development,” Eleanor notes. “You evaluate what works, improve until you reach the point where the task works reliably, then it becomes a routine tool you can always use.”

Beyond Simple Automation: The Memory Agent Advantage

Advanced legal AI goes beyond document processing to include “memory agents” — systems that retain context across sessions and build up institutional knowledge.

Hadi describes judge profiling as one powerful application: “We scrape everything available on a particular judge and create a document showing their persona and judgment patterns from past cases.” This intelligence helps lawyers prepare more targeted arguments and set realistic expectations for case outcomes.

The technical implementation involves sourcing data from legal research platforms, case law databases or through scraping publicly available records , then building a sophisticated classification pipeline with state management to track information in real time and overriding previous memories when new information emerges.

But the real breakthrough is treating legal knowledge as cumulative. Traditional law firm knowledge management relies on human memory and informal networks. AI systems can capture, structure, and recall institutional knowledge at scale, creating compound advantages over time.

Eleanor points to another pattern: “Companies that are already co-piloting with AI tools understand the next logical step is engineering it properly. If they’ve done it manually and seen the potential, they’re much more open to automation.”

This creates a natural progression path. Firms experiment with ChatGPT or similar end-userAI tools for document analysis, recognise the value, then seek engineering expertise to scale and systematise their workflows.

The Technical Reality: Keep It Simple

Both advocate for pragmatic technology choices over cutting-edge complexity. “AI moves fast — new tech makes old tech irrelevant in weeks and months,” Hadi notes. “No need for fine-tuning overhead. Context Engineering and model selection deliver the highest return on investment.”

This philosophy extends to the entire technical stack. Rather than building complex RAG systems that require extensive research and development, focus on proven approaches that deliver immediate value.

For engineers wanting to get technical, Hadi recommends DSPy for structured prompt engineering. But the core skills remain foundational:

• Search Engineering: Retrieving and processing sources effectively
• Product Engineering: Solving complex, open-ended problems
• Evaluation Engineering: Measuring and improving performance iteratively

“Focus too much on RAG and you’re using hacky technology that needs enormous research to get right, most of the time you’re dealing with a classification problem” Hadi warns. “User-facing RAG is evolving into AI-generated reports powered by agentic tool calling.”

The emphasis on simplicity reflects a deeper insight about enterprise adoption. Legal firms need solutions that work reliably today, not experimental technology that might work better tomorrow. Engineers who deliver immediate value through proven approaches build trust and create opportunities for more sophisticated implementations later.

The Compliance Challenge

Legal AI demands higher reliability standards than most applications. Both experts stress the importance of compliance frameworks covering SOC2, HIPAA, GDPR and best practices across major jurisdictions including the US, EU, India, Singapore, and Australia. The ethical considerations are equally important.

For technical implementation, this means robust evaluation systems, clear audit trails, and human oversight for critical decisions. The challenge isn’t just accuracy — it’s building systems that lawyers can trust with sensitive client information and critical legal strategies.

Hadi’s approach involves comprehensive compliance planning from the start rather than retrofitting security measures. “I try my best to be compliant with laws from major countries,” he explains, noting that regulatory requirements often drive technical architecture decisions.

The human element remains crucial. “The last 10% — from signatures to court appearances — still requires human lawyers,” Eleanor notes. “But AI handles the 90% of analysis and structured work.” This division of labour respects both legal requirements and practical limitations while maximising AI’s impact.

Getting Started: The Network Approach

For engineers looking to enter legal AI, both recommend starting with professional connections rather than formal domain expertise. “I find someone in my network who works in the industry and ask them everything,” Hadi explains. “How do they work, what tools do they use, how does their process function? I just eat their brains.”

This approach recognises that domain knowledge isn’t just about understanding legal theory — it’s about understanding workflow pain points. Engineers need to identify the specific tasks that consume enormous amounts of lawyer time without requiring deep legal judgment.

Eleanor agrees: “Engineers are generalists and problem-solvers. We don’t need extensive domain knowledge initially. We can think through problems and build solutions, and validate with domain experts.”

The methodology works across industries. “Talk to the domain experts, get all their tribal knowledge, then scratch your own itch,” Eleanor suggests. “Find document repositories online and build something useful.”

Hadi’s experience illustrates this progression. He started by solving his own legal problem, shared his story in communities, connected with legal tech startups, and gradually built expertise through practical application rather than theoretical study.

The key insight is that legal professionals want solutions to concrete problems, not demonstrations of AI capabilities. Engineers who focus on workflow automation rather than technology showcasing build stronger relationships and create more valuable solutions.

The Competitive Imperative

The window for competitive advantage is closing rapidly. “The market pull is so strong,” Hadi observes. “If you don’t implement AI, your competitor will. This is like having cars for the first time versus bicycles.”

The analogy captures more than just efficiency gains. Law firms that embrace AI today are establishing operational capabilities that will compound over years.

Consider the capacity implications alone. AI-enabled firms can take on more cases, work more efficiently, and deliver consistent results beyond human limitations. They can process thousands of documents in hours rather than weeks, identify patterns across vast case databases, and provide 24/7 intelligence support for time-sensitive legal strategies.

Firms that delay adoption face more than just efficiency disadvantages. They risk losing institutional knowledge as younger lawyers gravitate towards AI-enabled practices, missing opportunities to build competitive intelligence capabilities, and falling behind in client expectations for rapid, data-driven legal analysis.

For AI engineers, this creates an urgent opportunity. Legal firms need embedded AI engineering teams, not one-off consulting projects. They need reliable, evaluated systems that improve iteratively — exactly what effective engineering teams deliver.

The timing advantage is particularly important. “AI engineering is search engineering, product engineering, and evaluation engineering,” Hadi notes. These skills transfer directly from other domains, giving experienced engineers immediate advantages in legal applications.

The Economics of Legal AI

The financial dynamics of legal AI create compelling opportunities for both law firms and engineering teams. Traditional legal work operates on billable hour models that create perverse incentives — more time means more revenue, even if clients prefer faster resolution.

AI flips this equation. Firms that can deliver equivalent outcomes in less time can either increase profitability by maintaining rates while reducing costs, or gain market share by offering competitive pricing with maintained margins.

Hadi’s experience with settlement prediction illustrates the value creation potential. By analysing historical dispute patterns and outcomes, AI systems can help firms set more accurate expectations, negotiate more effectively, and advise clients on case strategy with data-driven confidence.

The consulting model Hadi describes — “done-for-you services” rather than software licenses — captures this value more effectively than traditional SaaS approaches. Instead of selling monthly subscriptions, engineering teams provide results as a service, aligning incentives around outcomes rather than usage.

Eleanor’s work shows similar patterns. Legal-adjacent industries face the same text-processing challenges as law firms but often have larger budgets and clearer ROI metrics for efficiency improvements.

Your Next Move

The legal AI market isn’t just another vertical opportunity — it’s a blueprint for how AI transforms knowledge work across industries. The firms moving first are establishing competitive advantages that will compound over years.

The path forward requires recognising that legal AI is fundamentally about engineering excellence applied to domain-specific challenges. Success comes from building reliable, evaluated systems that improve iteratively, not from creating impressive demos or pursuing cutting-edge research.

For engineering leaders, the question isn’t whether to explore legal AI, but how quickly you can get started. The technology is proven, the market demand is enormous, and the competitive window is still open.

For law firms and legal-adjacent businesses, the stakes are even higher. Your competitors are already embedding AI engineers, automating discovery, and drafting airtight documents while their juniors sleep. Treat AI as core infrastructure: hire or partner with engineers, pick one high-volume workflow, and automate it end-to-end. Measure, iterate, expand. Firms that move first will set price and quality benchmarks for the next decade; laggards will spend years playing catch-up.

Ready to lead the transformation? The methodology is straightforward: Focus on the 90% of routine work rather than edge cases. Build simple, reliable systems using proven technology. Iterate based on user feedback and measurable outcomes. Scale successful workflows across the organisation.

The opportunity extends beyond projects and companies to individual career transformation. As both experts note, the future may include entirely new roles like “Legal AI Engineer” or “Investment Research AI Engineer” — domain specialists who combine engineering skills with industry expertise.

The future belongs to companies that treat AI as core infrastructure, not a side project. Legal AI offers the perfect proving ground to build that capability, with clear value propositions, receptive customers, and immediate applications for proven technology.

Eleanor Berger helps engineering teams build reliable AI systems at scale. Hadi Khan engineers AI solutions for startups & enterprises. Connect with them to explore opportunities in your industry.