| INDUSTRY
LEGAL |
AI SERVICE
DATA LAKE |
From Data Silos to AI-Ready Lakehouse: Major Law Firm Leverages Databricks to Enable AI and Data Transformation
Learn how Synaptiq enabled AI-powered workflow automation and data-driven operations across a law firm by architecting and deploying a production-grade enterprise data lakehouse on Databricks.
Background:
A legal services and immigration firm operating across multiple practice areas was at a critical inflection point in its business growth. The firm recognized that fragmented data systems, manual workflows, and the absence of a formal data architecture were limiting its ability to scale operations and compete in an increasingly AI-driven market. They engaged Synaptiq to define and execute a comprehensive AI and data transformation strategy spanning platform architecture, governance, AI-enabled workflow automation, and organizational capability building.
Business Challenge
Data was distributed across multiple systems – Salesforce, multiple case management systems, and internal spreadsheets – without a centralized infrastructure or unified catalog to connect them. Analysts relied on manual, spreadsheet-based reporting processes, presenting a clear opportunity for automation and efficiency gains. The firm was building its data and AI capabilities from the ground up, without yet having dedicated data engineering talent or established data product management practices.
AI adoption was at an exploratory stage, with significant runway to define and capture value from AI across the business. The firm had no customized AI products in production and no enterprise LLM governance policy in place. Data had not yet been positioned as a strategic growth driver, representing a significant opportunity Synaptiq moved quickly to address.
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Why Databricks:
Databricks was selected as the foundational platform for the data and AI transformation because of its unified Lakehouse architecture, which addresses both the structured analytics and unstructured document processing needs central to a legal services firm. With Synaptiq’s help, the law firm identified 3 key transformation areas: multi-source data ingestion from CRM and case management systems, exploratory data analysis, AI-powered document classification and extraction from intake workflows, executive BI dashboards, and a custom internal LLM. These use cases required a single platform capable of supporting data engineering pipelines, collaborative notebook-based research, and AI/ML workloads without requiring separate tooling stacks. Databricks on Azure aligned with the firm’s existing Azure infrastructure investment and Landing Zone architecture, enabling secure, governed, production-grade deployment. Databricks’ ability to support both structured gold-layer datasets for self-service analytics and unstructured document pipelines for AI automation made it the right fit for a phased transformation roadmap.
Synaptiq's Role:
Synaptiq served as the firm’s end-to-end AI and data transformation partner - functioning simultaneously as strategic advisor, enterprise architect, product strategist, and implementation lead. The engagement began with Synaptiq’s proprietary AIQ Readiness Assessment, a structured diagnostic that quantified the firm’s maturity across 11 data and AI capabilities and produced a prioritized 18-month transformation roadmap. Synaptiq then led architecture design for the enterprise data Lakehouse on Azure and Databricks, defined data governance policies and procedures, established Azure DevOps and Azure Landing Zones for production-grade deployment, and built the initial data pipelines ingesting data from Salesforce, case management systems, and internal spreadsheets into the Databricks environment. Synaptiq also led product definition and delivery for two AI workflow initiatives - an AI-powered document classifier and extractor for the intake process, and a custom internal LLM for staff writers. Throughout the engagement, Synaptiq embedded product management disciplines, data literacy programs, and organizational design recommendations to ensure the firm could sustain and expand its data and AI capabilities independently over time.
Databricks Solutions Delivered
| Component | Role in Solution | Business Value |
| Databricks Lakehouse Platform | Established as the centralized enterprise data Lakehouse, consolidating structured case management data and unstructured document data from multiple source systems into a unified, governed architecture with bronze, silver, and gold data layers. | Eliminated data silos across Client Services, Marketing, Operations, and other departments; enabled a single source of truth for case and client data accessible to all business teams; replaced fragmented Excel-based workflows with a scalable, production-grade data platform. |
| Delta Lake | Used as the storage layer for structured ingestion from Salesforce, case management systems, and internal spreadsheets, enabling ACID-compliant, versioned data tables across pipeline stages. | Provided reliable, auditable data storage that supports both analytical workloads and AI model training, reducing data quality risk and enabling consistent reporting across business units. |
| Lakeflow Pipelines / DLT | Pipeline framework for building and managing data ingestion and transformation workflows from multiple source systems into the Lakehouse, including snapshot-based and incremental pipeline patterns. | Accelerated data source connectivity across four source systems; reduced manual data preparation effort; established a repeatable pipeline pattern for future source system integrations. |
| Databricks Notebooks | Centralized research and development environment for data engineers and data scientists, explicitly referenced in the engagement as a key infrastructure recommendation. | Enabled collaborative exploratory data analysis (EDA) on case and intake document data; supported AI model development and prototyping for the document classifier use case; reduced time from data exploration to actionable insight. |
| Unity Catalog | Governance layer for the enterprise data Lakehouse, supporting the data catalog, access controls, and data lineage requirements defined in the engagement’s Architecture & Governance workstream. | Enabled the shared data catalog recommended as a top priority; provided fine-grained access controls appropriate for a legal services firm handling sensitive client and immigration case data; supported compliance and data quality governance requirements. |
| Databricks Apps / Collaboration | Environment supporting the Data Catalog App and EDA application, enabling internal stakeholders to interact with and explore data assets. | Democratized access to data insights across business teams; reduced bottlenecks on the data engineering team for routine data exploration and discovery tasks. |
Business Outcomes
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Elevated AI maturity trajectory across 11 capability dimensions over an 18-month roadmap, with highest-priority capabilities (Architecture & Governance, Data Operations, Customizing AI) advancing from Initial to Managed/Defined stages.
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Established the firm’s first production-grade enterprise data Lakehouse on Azure and Databricks, replacing a fragmented landscape of siloed spreadsheets and disconnected SaaS systems.
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Ingested data from multiple source systems (Salesforce, two case management systems, etc.) into the Databricks environment within the first sprints, with pipelines and silver-layer tables on the near-term roadmap.
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Deployed Azure Landing Zones and production Databricks workspaces, providing a secure, scalable infrastructure foundation for all current and future data and AI workloads.
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Initiated development of an AI-powered document classifier and extractor for the intake process, targeting end-to-end automation of a previously manual, high-volume document processing workflow.
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Defined and scoped a custom internal LLM for staff writers, with product requirements and PR/FAQ completed, enabling the firm to move from AI experimentation to structured AI product development.
Databricks Value Created
This engagement represents a net-new Databricks platform footprint at a firm in the early stages of building its data infrastructure prior to the engagement. Synaptiq drove the selection, architecture, and production deployment of Databricks as the enterprise data and AI platform -establishing it as the system of record for all analytics, data science, and AI workloads across the organization.
The engagement created a durable, expanding Databricks consumption opportunity: initial workloads (data ingestion, EDA, notebook-based development) are already in production, with pipeline buildout, silver and gold layer development, AI model training, document intelligence workloads, and self-service analytics querying all on the near-term roadmap. The 18-month transformation roadmap Synaptiq delivered explicitly sequences additional Databricks workload expansion across AI/ML, BI, and data operations - providing Databricks with clear visibility into consumption trajectories and a roadmap for moving the account up the commit ladder.
Synaptiq’s role as the trusted transformation partner also positions Databricks favorably within the leadership team as the strategic platform of choice, strengthening platform stickiness and reducing competitive risk from alternative data platforms.
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