Family law is one of the most emotionally charged and administratively complex areas of the justice system, yet it remains one of the least supported by meaningful technology. For Nimrita Dadlani, Founder of Pivot, this gap became painfully real during her own divorce, when she saw how fragile evidence handling is and how overwhelming the process can be for people already in crisis. Pivot was built from that lived experience. It is an auditable, AI-powered evidence intelligence platform designed to help family law teams make sense of chaotic, fragmented case material. Nimrita’s long-term mission is to bring clarity, fairness and humanity to a system that urgently needs it.
What inspired you to build Pivot, and why start with family law as the first vertical?
As a British South Asian founder, it was important to me that Pivot was not simply another AI company but a product that improves fairness for real families going through difficult transitions. The idea emerged during my own divorce, including a period of legal self-representation, which exposed how fragile evidence handling is in the UK. I often felt uninformed and, at times, completely lost. Navigating the system required a coach, a therapist and a lawyer. Realistically, I also needed a PA because the administrative burden was overwhelming. Family law is emotionally demanding, highly evidence heavy and historically underserved by technology. During my own proceedings, I faced thousands of WhatsApp messages and documents that all needed to be manually sifted. When cases involve complexity or high conflict, that workload quickly becomes unsustainable. I was drawn to family law because people entering the system are often traumatised, and the process can compound that trauma. The issue is not the law itself but the way evidence is handled. If we can prove that trustworthy, auditable AI works in family law, we can expand horizontally into other evidence-heavy areas using the same core system.
Share a simple summary of Pivot.
Pivot uses AI to help family law teams make sense of messy, unstructured and often chaotic case evidence. It automatically organises WhatsApps, emails, PDFs, bank statements, screenshots and other materials into one searchable case map and timeline that shows who did what and when. Lawyers can ask questions and receive fully referenced answers with direct links to the original source material. Lawyers currently spend too much time sifting through fragmented evidence. Pivot takes on the administrative heavy lifting so teams can work faster, reduce costs and improve access to justice. Most tools treat cases as flat documents. Pivot creates a three-dimensional map that captures people, events and relationships over time, giving context rather than plain text. The system strengthens as more information is added, attaching new facts to what has already been verified. A human expert is always in the loop. Lawyers decide review stages, approve changes and trace every conclusion back to its origins. Access to justice should not depend on who can afford long hours of manual evidence work, and Pivot aims to level that field.
How did you identify the gap between existing legal AI tools and the real-world pain points of lawyers handling fragmented evidence?
Conversations with firms revealed a clear pattern. They were testing generic workflow automation and contract-first AI tools, but nothing addressed the real work of gathering, collating, analysing and validating fragmented data. Teams were exporting WhatsApps, rebuilding spreadsheets and reconstructing timelines by hand. Pivot seeks to automate this process and deliver verifiable, court-ready answers and reports. Although we began in the UK, these challenges are universal. Sri Lanka, India and many Commonwealth countries still rely heavily on manual evidence handling. Pivot’s model can support legal systems worldwide.
Was there a moment that crystallised the inefficiency?
Yes. We watched a team spend days reconstructing a disputed timeline from chats and bank activity. These were brilliant lawyers operating as human data pipelines. When this is multiplied across thousands of cases, it creates delays, rising costs and uneven access to justice. It became obvious that better evidence infrastructure was essential. Every hour a lawyer spends reconstructing a timeline is an hour a client is paying for clarity that technology could deliver in seconds.
How do you balance precision, empathy and innovation in such a personal domain?
We begin with a simple assumption: clients are usually at their most vulnerable when they reach family lawyers. Precision comes from clear workflows and auditable trails rather than a chatbot improvising answers. Empathy comes from allowing clients to tell their story once and re-using that information across tasks so that emotional wounds are not repeatedly reopened. Innovation means giving lawyers superpowers, not replacing their judgement. Human review and sign-off are built into the product by design. Pivot incorporates trauma-informed principles to ensure clients are not retraumatised. AI should not replace judgement. It should protect it.
What is evidence intelligence and how is Pivot different?
Evidence intelligence uses a graph structure to organise messy case evidence into a clear map of facts, people, events and documents, showing where each fact comes from and who verified it. Pivot begins with the evidence rather than a stack of documents. It follows structured, repeatable steps that align with how courts operate rather than relying on a generic chatbot. Because Pivot is built specifically for family law, disclosure rules and court procedures are embedded from the outset. Evidence intelligence makes truth easier to find.
How does the Evidence Graph work in simple terms and why do provenance and auditability matter?
The Evidence Graph functions like Google Maps, but instead of roads and buildings, it displays connections between facts, messages and events. People, chats, documents, transactions and events are the points, and the links show who did what, when and how everything connects. This enables questions such as: show messages about this property sale in the three months before separation. The system provides an answer with direct links back to the original evidence. Provenance and auditability matter because courts must see where each fact originated, who added it and how it contributed to the final conclusions. When both sides rely on the same verifiable map, disputes can resolve more quickly.
How do you ensure transparency and human-in-the-loop decisions?
Lawyers control every stage of the process. There are no hallucinations because every conclusion is traceable. Each answer and output includes source references and a complete activity log. The workflow contains checkpoints so lawyers can inspect and approve at the appropriate moments. We never ask people to trust the AI blindly. We show them exactly how it reached each conclusion, and lawyers can override results at any time.
How do you handle data privacy and confidentiality?
No client data is used to train external AI models. Family law data is extremely sensitive, so we use strict access controls within the graph that limit how and when AI models are applied. All processing takes place within secure environments that align with legal and professional requirements. We design the product with partner firms and advisors to ensure compliance with bar guidance, data protection laws and client expectations.
What were the key technical challenges and how did you overcome them?
We had to make AI work for law. The core challenge was building a deterministic system where every output is repeatable and explainable, which distinguishes Pivot from chatbot-style unpredictability. Law requires consistency and verifiable results, so we treat the graph as the single source of truth and run AI within strict, step-by-step workflows. Family law evidence is chaotic and arrives in many formats. We built robust systems to ingest, clean, validate and deduplicate this material. By co-designing with partner firms and groups such as the Resolution Innovation Committee, real legal procedures are integrated directly into our workflows.
Family law is the beachhead. How will you scale horizontally?
Once the foundational building blocks of people, events, transactions and orders are fully established and proven in family law, we can apply them to other evidence-heavy domains such as employment disputes, civil litigation, fraud investigations and immigration. The underlying Evidence Graph remains the same. Only the regulatory frameworks change. Family law was the ideal starting point because it is emotionally complex and saturated with unstructured evidence.
What are prospective pilot firms asking for?
Firms consistently ask for clear, verifiable answers with source links, faster bundle preparation and tools that integrate seamlessly with existing workflows. During our pilots, we are focused on measuring time to bundle, accuracy against original evidence and how confident lawyers feel using Pivot. Many describe the experience as having a junior assistant devoted entirely to evidence clarity.
How does this improve fairness, not just efficiency?
At present, outcomes often depend on who can afford more hours. That is not justice. By reaching the key facts more quickly, lawyers and courts can see the full picture sooner rather than relying on who can pay for prolonged manual analysis. Standardised workflows with citations and audit trails create transparency and more consistent decisions across cases.
How do you drive adoption in a risk-averse profession?
Resistance is expected. Legal innovation must be earned. We begin with tightly scoped pilots, clear success criteria and co-development with respected practitioners. Transparency is essential. Firms gain confidence when they see real performance data, hear from peers and observe repeatable, predictable behaviour before scaling.
What role do mentors and advisors play?
Our board provides targeted guidance where deeper expertise is needed, particularly around identifying where innovation genuinely improves legal practice. Our operator and AI advisors support us in positioning Pivot, sharpening our competitive edge and shaping pricing, go-to-market strategy and the product roadmap.
What is the long-term vision?
Our long-term ambition is for Pivot to become the trusted operating system for evidence-based reasoning across industries. We aim to build the industry-defining, verticalized AI for family law, used by barristers, solicitors, coaches, consultants and clients. We are not replacing lawyers. We are giving them extraordinary clarity. From this foundation, we intend to expand horizontally, bringing the same provenance-first approach to adjacent evidence-heavy domains and defining the Evidence Intelligence category.
Online: https://yourpivot.co
