of CEOs expect AI to transform their business
organisations plan to increase GenAI investment
of legal leaders see AI potential, but few have a strategy
Why work with us
AI and Data Experts on board
Finished projects
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
What our clients say
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Clients that trusted us
Law firms and in-house teams don’t just need speed – they need accuracy, consistency, and control. Generic AI tools may help draft faster, but they often lack legal nuance, context, and safeguards, which can compromise quality.
High-quality legal AI is built on domain-specific models, secure infrastructure, and tailored workflows. Instead of one-size-fits-all chatbots, customized solutions ensure outputs align with legal standards and firm practices – allowing teams to automate work without losing precision or control.
Key AI Applications in AI in Legal Industry
AI‑Assisted Legal Research & Knowledge Search
Contract Review, Playbook Automation & Due Diligence
Reviewing Evidence & Understanding Case Data
Document Automation & Operational Workflows
How AI Supports Legal Companies
Faster decision-making and time-to-market
AI accelerates legal review, contract analysis, and research, significantly reducing turnaround times. Shorter legal cycles mean faster deal execution, quicker product launches, and more agile responses to regulatory changes—directly supporting revenue growth and competitive advantage.
Lower costs and improved ROI
By automating high-volume, repetitive work—such as document review, e-discovery, and standard contracts—AI reduces reliance on external counsel and lowers the cost per matter. The result is more predictable legal spend, improved margins, and a stronger return on legal operations.
Stronger risk control and compliance
AI enforces consistency in contract language, identifies risky or non-standard terms, and tracks obligations across large portfolios. This leads to fewer compliance issues, reduced exposure in audits and disputes, and greater confidence in enterprise risk management.
Scalable, data-driven legal operations
With centralized data and AI-powered insights, legal teams gain visibility into workload, performance, and resource allocation. This enables the function to scale with the business—without proportional headcount growth—while clearly demonstrating impact through measurable KPIs aligned with business goals.
Legal Sector Challenges Solved with AI
How can we speed up contract reviews without hiring more lawyers?
Challenge: Contract review is often a major bottleneck for growing organisations. Sales, procurement, and business teams can wait days or even weeks for approvals, while legal teams spend significant time reviewing standard clauses and chasing non-standard terms. As contract volume increases, this slows down deal cycles, delays revenue, and creates pressure to expand legal teams just to keep up.
Solution: AI reviews contracts against your company’s predefined rules and standards, automatically identifying risky or non-compliant clauses and extracting key terms. It delivers a first-pass review in minutes, allowing lawyers to focus only on exceptions and higher-risk issues. This speeds up approvals, reduces manual effort, and enables legal to support higher volumes without increasing headcount.
How can we reduce time spent on legal research?
Challenge: Legal research is time-consuming and often inefficient. Lawyers need to search across multiple systems—case law databases, internal documents, emails, and past advice—where knowledge is fragmented and hard to access. This leads to duplicated work, inconsistent answers, and slower responses to business teams that depend on timely legal input.
Solution: AI-powered research tools unify internal and external knowledge sources and allow lawyers to search using natural language. Relevant cases, documents, and prior advice are surfaced instantly, reducing time spent searching and improving consistency. This enables faster, more reliable responses to the business and better reuse of existing knowledge.
How can we lower the cost of investigations and e-discovery?
Challenge: Disputes, audits, and internal investigations generate massive volumes of data—emails, messages, and documents—that must be reviewed under tight deadlines. Traditional manual review is slow, labour-intensive, and often requires expensive external support, driving up legal costs and diverting internal resources from strategic work.
Solution: AI-driven tools automatically organise and analyse large datasets by removing duplicates, grouping similar documents, and identifying the most relevant content. This significantly reduces the number of documents that need manual review, speeds up investigations, and lowers reliance on external providers—resulting in substantial cost savings and more efficient use of internal teams.
How can we keep up with constant regulatory changes?
Challenge: Regulatory requirements are constantly evolving, and keeping track of changes across jurisdictions is complex. Legal teams must not only monitor updates but also assess their impact and ensure changes are reflected in contracts, policies, and business processes. Without efficient systems, this can lead to inconsistent compliance and increased risk during audits or regulatory reviews.
Solution: AI continuously monitors regulatory updates and key sources, summarises changes, and connects them to relevant documents, policies, and workflows. It can also support automated checks to ensure compliance in contracts and processes. This helps organisations respond faster to change, reduce compliance gaps, and maintain a proactive, well-controlled risk posture.
How can we speed up contract reviews without hiring more lawyers?
Challenge: Contract review is often a major bottleneck for growing organisations. Sales, procurement, and business teams can wait days or even weeks for approvals, while legal teams spend significant time reviewing standard clauses and chasing non-standard terms. As contract volume increases, this slows down deal cycles, delays revenue, and creates pressure to expand legal teams just to keep up.
Solution: AI reviews contracts against your company’s predefined rules and standards, automatically identifying risky or non-compliant clauses and extracting key terms. It delivers a first-pass review in minutes, allowing lawyers to focus only on exceptions and higher-risk issues. This speeds up approvals, reduces manual effort, and enables legal to support higher volumes without increasing headcount.
How can we reduce time spent on legal research?
Challenge: Legal research is time-consuming and often inefficient. Lawyers need to search across multiple systems—case law databases, internal documents, emails, and past advice—where knowledge is fragmented and hard to access. This leads to duplicated work, inconsistent answers, and slower responses to business teams that depend on timely legal input.
Solution: AI-powered research tools unify internal and external knowledge sources and allow lawyers to search using natural language. Relevant cases, documents, and prior advice are surfaced instantly, reducing time spent searching and improving consistency. This enables faster, more reliable responses to the business and better reuse of existing knowledge.
How can we lower the cost of investigations and e-discovery?
Challenge: Disputes, audits, and internal investigations generate massive volumes of data—emails, messages, and documents—that must be reviewed under tight deadlines. Traditional manual review is slow, labour-intensive, and often requires expensive external support, driving up legal costs and diverting internal resources from strategic work.
Solution: AI-driven tools automatically organise and analyse large datasets by removing duplicates, grouping similar documents, and identifying the most relevant content. This significantly reduces the number of documents that need manual review, speeds up investigations, and lowers reliance on external providers—resulting in substantial cost savings and more efficient use of internal teams.
How can we keep up with constant regulatory changes?
Challenge: Regulatory requirements are constantly evolving, and keeping track of changes across jurisdictions is complex. Legal teams must not only monitor updates but also assess their impact and ensure changes are reflected in contracts, policies, and business processes. Without efficient systems, this can lead to inconsistent compliance and increased risk during audits or regulatory reviews.
Solution: AI continuously monitors regulatory updates and key sources, summarises changes, and connects them to relevant documents, policies, and workflows. It can also support automated checks to ensure compliance in contracts and processes. This helps organisations respond faster to change, reduce compliance gaps, and maintain a proactive, well-controlled risk posture.
AI Solutions for Legal Sector
How can we find and use legal knowledge faster?
AI helps legal teams quickly find and understand relevant case law, regulations, and internal documents by using large language models combined with retrieval systems (RAG) that search across indexed legal databases and internal knowledge bases. Instead of starting from scratch, lawyers receive context-aware summaries and relevant sources, allowing them to focus on analysis and strategy.
How can we manage contracts more efficiently at scale?
AI supports the full contract lifecycle by applying NLP models to extract clauses, classify terms, and compare documents against predefined rules and standards. Integrated with contract management systems, it can flag risks, suggest edits, and track obligations post-signature—ensuring consistency and scalability across large contract volumes.
How can we handle large investigations and disputes more efficiently?
AI accelerates document-heavy matters by using machine learning techniques such as clustering, classification, and predictive coding to analyse large datasets. It identifies relevant documents, removes duplicates, and surfaces key patterns, enabling faster early case assessment and reducing the volume of manual review.
How can we make legal operations more data-driven?
AI analyses structured and unstructured data from legal systems—such as matter management, contracts, and workflows—using analytics models and dashboards. It uncovers trends in workload, turnaround times, and external spend, enabling forecasting, performance tracking, and more efficient allocation of legal resources.
What our clients say
Addepto is a rapidly developing company, that observes the latest technological trends and takes full advantage of it. They offer an individual approach to each client and are open to new challenges.
Bobby Newman VP Engineering – J2 Global
Addepto strives to find the most optimized way to secure business growth through advanced technology.
Steven Kinder CTO of Paperplanes Global Ltd
We are recognized as one of the best AI, BI, and Big Data consultants
We helped multiple companies achieve their goals, but - instead of making hollow marketing claims here - we encourage you to check our Clutch scoring.
FAQ
Is AI safe to use with sensitive and privileged legal data?
Yes, provided it is implemented with the right safeguards. Enterprise‑grade legal AI runs in controlled environments with strict access management, encryption, and clear data‑handling rules. This ensures that confidential documents stay within your organisation and are processed in ways that meet professional secrecy, confidentiality, and data‑protection obligations.
How reliable are AI outputs in legal use cases?
AI systems are highly effective when they are trained on relevant data, configured for specific use cases, and used with clear guardrails. In legal contexts, this means combining domain‑tuned models with curated sources and mandatory human review before anything is final. The goal is not blind trust, but faster first drafts and better starting points that lawyers can verify and refine.
What changes do we need to make internally before adopting legal AI?
Successful deployments usually combine technology with process and policy updates. This often includes defining where AI can and cannot be used, updating playbooks and templates so tools have clear rules to follow, and providing training so lawyers know how to review outputs effectively. Clear ownership (e.g. a legal operations or innovation lead) helps keep the programme on track.
How long does it take to see value from legal AI?
Time‑to‑value depends on the starting point, but targeted pilots typically show impact within weeks rather than months. When you begin with a well‑scoped workflow, such as a specific contract type or a defined research task, teams can measure time saved, error reductions, and smoother handoffs very quickly, then scale to additional use cases.
How can we measure whether our AI initiatives are successful?
Clear metrics are essential. Legal teams usually track a mix of operational and business outcomes: reduction in turnaround times, percentage of work automated, changes in external spend, user adoption, and satisfaction from internal stakeholders. Over time, these indicators help refine where AI is most effective and where additional change‑management or process work is needed.
