AIh – AI to the Human Power

Accountability and Reliability in the Age of AI 

In litigation, you still win on the evidence. Our job is to help legal teams build a record they can explain, defend, and stand behind. We don’t sell an AI platform. We engineer matter-specific, defensible workflows that use AI when it helps and avoid it when it doesn’t. 

This is how we think about AI: 

Evidence-First Approach 

Every AI-enabled step must be documented (protocol-ready), validated (tested for reliability), traceable (including what it relied on and how it worked), and human-owned (a person is accountable for it). 

Tool-Neutral by Design 

We select, configure, and validate the right platform or model for the matter, not a tool we’re trying to sell. And if the right solution doesn’t exist, our human experts will build the solution your matter needs.  

Human Accountability 

AI doesn’t make legal judgments. Our experts do. Every decision that matters has a name behind it.  

Responsible AI, Operationalized 

We apply responsible AI principles, keeping privacy and security, transparency, fairness, and reliability at the forefront. We minimize exposure, protect confidences, identify and mitigate bias, document everything, and validate outputs.   

Why This Matters to Litigators  

AI is now part of how evidence is generated, summarized, and analyzed. When AI touches the record, courts will ask:  

  • Is it reliable?
  • Is it explainable?
  • Can it be challenged?
  • Who can testify about it?

Our workflows prepare you for those questions. 

What Makes Us Different

We start with the matter, not the platform.

We know when NOT to use AI.

We design workflows that survive scrutiny.

We avoid lock-in and vendor bias.

We communicate in plain English.

We deliver clean productions, predictable timelines, and defensible decisions.

AI Workflows You Can Defend 

Connect with our team for help designing defensible AI workflows aligned with the needs of your matter. 

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Our Framework

Understand.

During this phase, we work to step away from any assumptions and guesses about what our customers needs, and let our research findings inform our decision-making. We learn more about our customers, their problems, wants, and needs, and the environment or context in which they will use the solution we offer.

Our Framework

Define.

During the Define phase, we analyze our research findings from the Understand phase and determine what is the most important problem to solve — and why. This step defines the goal. Then we can give a clear problem statement, describing what our customers’ needs are that we are trying to solve, making sure that we heard and defined their problem correctly.

Our Framework

Solve.

This phase is an important part of the discipline in our process. People often settle for the first solution, but the most obvious solution is often not the right one. During the Solve phase, we brainstorm collaboratively with multiple stakeholders to generate many unique solutions. We then analyze our potential solutions and make choices about which are the best to pursue based on learnings in the Understand phase.

Our Framework

Build & Test.

This phase is critical in developing the right solution to our customers’ problem. An organized approach to testing can help avoid rework and create exceptional outcomes. Starting small and testing the solution, we iterate quickly, before deploying solutions across the entire project.

Our Framework

Act.

During this phase, the hard work of prior phases comes to life in our customers’ best solution. The research, collaboration, and testing performed prior to project kick-off ensure optimal results.

Our Framework

Feedback.

At the project completion, we convene all stakeholders to discuss what went well, what could have been better, and how we might improve going forward. We call these meetings “Retrospectives,” and we perform them internally as a project team, and with our external customers. The Retrospective is one of the most powerful, meaningful tools in our framework.

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