How to Choose AI Tools for Your Law or Accounting Firm: A Compliance Guide

How to choose AI tools that are actually worth adopting in your Brisbane law or accounting firm, and which ones are a security risk wearing a polished marketing website?

New AI tools aimed at lawyers and accountants seem to launch every month, each promising to save hours of work on document review, drafting, or compliance checks. For a managing partner or practice manager, it’s genuinely hard to know which ones deserve a budget line and which ones will end up as an unused subscription or, worse, a confidentiality incident waiting to happen.

This guide covers how to choose AI tools for a law or accounting firm, what’s changed in Australia’s compliance landscape recently, the main categories of tools worth understanding, and how to roll one out without wasting the investment.

Key Takeaways: How To Choose AI Tools for Law and Accounting Firms

• AI tool selection should start with the specific bottleneck slowing your team down, not with whichever product has the best marketing.
• Every AI tool a firm considers should be checked against five questions: data handling, integration, accountability for errors, vendor stability, and compliance obligations.
• Australian courts have already sanctioned lawyers over AI-generated submissions containing fabricated citations, making output verification a firm-wide responsibility, not just a vendor feature.
• APES 110 added technology-related provisions from 1 January 2025, and the Queensland Law Society became the first Australian law society to issue formal AI guidance for solicitors.
• Most firms get the best return starting with general productivity AI like Microsoft Copilot before layering in specialised legal or accounting-specific tools.
• A four-to-six week pilot with five to ten staff, before firm-wide licensing, is the single most effective way to avoid paying for software nobody opens.
• Tool selection and staff training need to be planned together. A well-chosen AI tool with no training plan behind it underperforms its potential every time.

What Problem Are You Actually Trying to Solve With AI?

The biggest mistake firms make when choosing an AI tool is starting with “what AI tool should we buy” instead of “what’s actually slowing my team down” — the right tool starts with a clear project goal rather than vendor marketing.

Document review taking too long? Client correspondence eating into billable hours? Manual data entry between systems? The right tool should reduce manual effort in that specific bottleneck, because each of these points towards a different category of tool, and a tool chosen for the wrong problem ends up unused within a few months.

Before looking at any product, map your current workflow so you can see where the time is actually going in your firm before you compare tools. The best AI tools are the ones that solve a specific problem for a defined group of users.

Talk to your fee earners and your support staff separately, identifying who will use the tool, because they usually identify different bottlenecks. A partner might point to slow contract review. A paralegal might point to chasing document versions across three systems. Both are legitimate problems, but they call for different tools.

A useful way to surface this internally is to ask each team to nominate their single most repetitive, lowest-value task; for many businesses, the first win comes from identifying repeated low-value work before comparing vendors.

Patterns tend to emerge quickly, and whichever task comes up most often across the most people is usually where AI delivers the fastest, most measurable return, especially when you set clear outcomes before comparing tools.

What Are the Main Categories of AI Tools for Professional Services Firms?

Rather than chasing every new product, it helps to think in categories. AI tools are often grouped into six main types. Here we’ll focus on the categories most relevant to law and accounting firms, where artificial intelligence supports practical workflows and generative AI tools are often the most visible starting point.

General Productivity AI: Where Most Firms Boost Productivity Fastest

This includes Microsoft Copilot and similar tools built into Microsoft 365, as well as Google Workspace options. Many firms should assess existing AI capabilities there before shopping for separate products. This matters because these general productivity tools boost productivity by working inside the everyday apps staff already use for 6–8 hours daily.

Google Docs is one example where Google Workspace-based AI features can assist drafting and collaboration. In practice, ai helps most when it works inside the document and existing workflow rather than through constant app-switching.

It assists with drafting, summarising, and research across everyday tasks, sitting inside your existing Microsoft 365 security boundary. Vetting effort is lower here, since data governance is largely inherited from infrastructure your firm has already secured. Free versions are useful for initial testing, but they usually have tighter limits than paid tiers, which may offer higher usage limits for daily users.

Legal or Accounting-Specific AI Tools

This includes contract review platforms, due diligence tools, and tax research assistants, built for tasks where purpose-built features offer deeper functionality than general tools, and some firms later pair broad platforms with specialized tools for narrow, high-value work.

Vetting effort is higher here, since these vendors are often newer and smaller, with less established track records, so judge fit with your workflows rather than vendor feature lists or advanced features alone.

Content Creation and Image or Video Generation

Outside professional-services use cases specifically, some specialised AI tools are also chosen for content creation, including image generation.

Some now support video creation for training or marketing content, too, and can save creators 5–7 hours weekly. Firms may also pair them with note-taking tools when building training materials or internal knowledge assets.

Data Analysis Tools

Some specialist tools are also built for data analysis, where firms need help with large or complex datasets. This is a category to test carefully with real, messy client data rather than a vendor’s clean demo dataset.

That matters especially because some tools can pull in real-time data while others rely on static exports, which can materially change results in live business scenarios.

Practice Management AI Add-On

This includes AI features built directly into a practice management or document management system your firm already uses. In many cases, practice-management add-ons are often the most practical powerful AI tool because they fit existing workflows, not because they have the longest feature set.

It’s typically a lower-risk way to adopt AI, since add-on features are often easier to roll out because they fit existing workflows, reduce integration friction, and rely on a platform you’ve already vetted; some firms also prefer one tool inside an existing platform rather than separate apps that add more switching.

Vetting effort is moderate, mainly limited to reviewing what the new AI feature changes about existing data flows.

Most firms get the best results starting with general productivity AI, since it touches the widest range of daily tasks with the lowest integration risk, before layering in specialised tools for specific practice areas.

A firm doing a high volume of due diligence or tax research will eventually outgrow general tools for that specific task, but it rarely makes sense to start there.

Scalability also matters if the add-on may later be used across more teams, matters, or workflows, especially when it also needs to support multiple platforms used to run operations.

What Questions Should You Ask When Choosing an AI Tool

Once you know the problem you’re solving, here’s what we’d recommend working through for any AI tool a firm is considering, before signing up — a structured approach to assess whether the underlying AI models are capable enough for the work required.

Where Does Our Data Go, Who Can See It, and What About Data Security?

This is the most important question and the one most often skipped. Vendors should be able to explain their access controls for sensitive firm and client information, not just where the data is hosted.

Ask the vendor directly whether your firm’s data is used to train their underlying AI model, where the data is hosted, what data security controls apply, and whether it’s stored in Australia or offshore, with the vendor’s data handling policies matching your firm’s privacy requirements.

For firms handling privileged or confidential client information, including financial data or medical records, vague or evasive answers here should be a dealbreaker.

It’s also worth confirming the vendor’s approach to data structure before rollout. AI tools generally require clean, centralised data to be effective. A tool fed scattered, duplicated, or poorly organised records will underperform regardless of how capable the underlying model is.

So it’s worth understanding what state your firm’s data needs to be in before a tool can deliver on its promised results.

Does It Integrate With Our Current Systems and What We Already Use?

A tool that doesn’t connect with your practice management system, document management system, or Microsoft 365 environment creates extra manual work, so test integration across your existing systems and ask whether the vendor provides api access where deeper workflow integration is needed.

Tools that sit inside your existing Microsoft 365 environment, like Copilot, generally have an advantage here because they work within infrastructure your firm already has secured and governed.

Reducing switching between other tools also improves adoption and efficiency, but simply having access to multiple models is less useful than using them in the same workspace in a way that preserves context.

They reduce extra copy-paste work for staff when integration is weak and are more likely to fit existing systems and connect with internal systems already in use when stronger integrations reflect your firm’s business rules instead of forcing staff into a generic process.

What Happens if the Tool Gets It Wrong

AI tools make mistakes, sometimes confidently and convincingly. They can still be useful for repetitive tasks, but higher-stakes work needs stricter checking.

Test them on complex problems and project-specific examples — running the same prompt through several tools and comparing the outputs is a quick way to see where a given model is weak — not just basic prompts.

Understand what the vendor’s terms say about liability, and more importantly, build your own internal process for verifying AI output before it reaches a client or the court, especially where stronger reasoning models and data analysis capabilities may matter more for high-stakes analytical work.

No tool removes the need for a qualified person to check the work. During testing, assess output quality, not just whether the model sounds confident.

AI can support the output, but human creativity and professional judgment still determine quality and appropriateness. This is the exact failure pattern behind nearly every AI-related disciplinary matter Australian courts have seen so far: not that AI was used, but that nobody checked its output before it went out the door.

Is the Vendor Still Going to Exist in Two Years

The AI tool market is crowded and consolidating quickly, and frequent releases of new models can improve tools quickly but also make smaller vendors harder to assess over a two-year horizon.

A flashy new legal AI startup might not be around when you need support, or when your firm has built workflows around it. That’s why comparing established vendors and newer AI companies also helps gauge long-term support and reliability — vendor reputation directly affects how dependable the tool is over time.

Established vendors with a track record, or tools built on platforms like Microsoft 365 that aren’t going anywhere, carry less of this risk.

Does It Meet Our Compliance Obligations

For law firms, this means checking the tool against your obligations under the Legal Profession Uniform Law, your professional indemnity insurer’s requirements, and any AI-specific guidance issued by your state law society.

For accounting firms, it means checking against APES 110 and Tax Practitioners Board expectations. For both, it means checking the tool’s data handling against the Privacy Act 1988 and the cyber risk obligations introduced under the Cyber Security Act 2024.

Can We Explain and Reproduce What the Tool Did

This question gets overlooked but matters increasingly, especially when the tool is being used for advice, analysis, or decision making that may later need to be justified. Explainability matters even more when firms are integrating AI into established advisory or operational processes.

If a client, a court, or a regulator later asks how a piece of advice or analysis was produced, can your firm explain which parts were AI-assisted and show that a qualified person reviewed and approved the output?

Tools that operate as an opaque black box, with no audit trail of inputs and outputs, make this much harder to demonstrate after the fact, and they also weaken reproducibility for the tool’s specific use case and functionality.

Opaque outputs are even harder to defend when they rely on machine learning features the firm cannot meaningfully audit.

Where Does Our Data Go, Who Can See It, and What About Data Security?

This is the most important question and the one most often skipped. Vendors should be able to explain their access controls for sensitive firm and client information, not just where the data is hosted.

Ask the vendor directly whether your firm’s data is used to train their underlying AI model, where the data is hosted, what data security controls apply, and whether it’s stored in Australia or offshore, with the vendor’s data handling policies matching your firm’s privacy requirements.

For firms handling privileged or confidential client information, including financial data or medical records, vague or evasive answers here should be a dealbreaker.

It’s also worth confirming the vendor’s approach to data structure before rollout. AI tools generally require clean, centralised data to be effective. A tool fed scattered, duplicated, or poorly organised records will underperform regardless of how capable the underlying model is.

So it’s worth understanding what state your firm’s data needs to be in before a tool can deliver on its promised results.

Does It Integrate With Our Current Systems and What We Already Use?

A tool that doesn’t connect with your practice management system, document management system, or Microsoft 365 environment creates extra manual work, so test integration across your existing systems and ask whether the vendor provides api access where deeper workflow integration is needed.

Tools that sit inside your existing Microsoft 365 environment, like Copilot, generally have an advantage here because they work within infrastructure your firm already has secured and governed.

Reducing switching between other tools also improves adoption and efficiency, but simply having access to multiple models is less useful than using them in the same workspace in a way that preserves context.

They reduce extra copy-paste work for staff when integration is weak and are more likely to fit existing systems and connect with internal systems already in use when stronger integrations reflect your firm’s business rules instead of forcing staff into a generic process.

What Happens if the Tool Gets It Wrong

AI tools make mistakes, sometimes confidently and convincingly. They can still be useful for repetitive tasks, but higher-stakes work needs stricter checking.

Test them on complex problems and project-specific examples — running the same prompt through several tools and comparing the outputs is a quick way to see where a given model is weak — not just basic prompts.

Understand what the vendor’s terms say about liability, and more importantly, build your own internal process for verifying AI output before it reaches a client or the court, especially where stronger reasoning models and data analysis capabilities may matter more for high-stakes analytical work.

No tool removes the need for a qualified person to check the work. During testing, assess output quality, not just whether the model sounds confident.

AI can support the output, but human creativity and professional judgment still determine quality and appropriateness. This is the exact failure pattern behind nearly every AI-related disciplinary matter Australian courts have seen so far: not that AI was used, but that nobody checked its output before it went out the door.

Is the Vendor Still Going to Exist in Two Years

The AI tool market is crowded and consolidating quickly, and frequent releases of new models can improve tools quickly but also make smaller vendors harder to assess over a two-year horizon.

A flashy new legal AI startup might not be around when you need support, or when your firm has built workflows around it. That’s why comparing established vendors and newer AI companies also helps gauge long-term support and reliability — vendor reputation directly affects how dependable the tool is over time.

Established vendors with a track record, or tools built on platforms like Microsoft 365 that aren’t going anywhere, carry less of this risk.

Does It Meet Our Compliance Obligations

For law firms, this means checking the tool against your obligations under the Legal Profession Uniform Law, your professional indemnity insurer’s requirements, and any AI-specific guidance issued by your state law society.

For accounting firms, it means checking against APES 110 and Tax Practitioners Board expectations. For both, it means checking the tool’s data handling against the Privacy Act 1988 and the cyber risk obligations introduced under the Cyber Security Act 2024.

Can We Explain and Reproduce What the Tool Did

This question gets overlooked but matters increasingly, especially when the tool is being used for advice, analysis, or decision making that may later need to be justified. Explainability matters even more when firms are integrating AI into established advisory or operational processes.

If a client, a court, or a regulator later asks how a piece of advice or analysis was produced, can your firm explain which parts were AI-assisted and show that a qualified person reviewed and approved the output?

Tools that operate as an opaque black box, with no audit trail of inputs and outputs, make this much harder to demonstrate after the fact, and they also weaken reproducibility for the tool’s specific use case and functionality.

Opaque outputs are even harder to defend when they rely on machine learning features the firm cannot meaningfully audit.

What Should You Check Before Signing an AI Tool Contract?

Most AI tool contracts are written by the vendor, for the vendor, and most firms sign them after a single demo and a quick call with a sales rep.

Before rolling out any AI tool across a Brisbane law or accounting firm, there are seven specific things worth confirming in writing rather than relying on a verbal assurance. Firms should also look at the total cost, not just the subscription cost quoted in the demo, including subscription price and broader ownership expenses.

None of these checks require a lawyer to review every line of the contract, but they do require someone in the firm to ask the right question and get a documented answer before money changes hands. Making an informed decision means documenting pricing, support, and ownership costs in writing before signing.

Data Hosting and Model Training Terms

Confirm in writing whether the vendor uses your firm’s data, including client documents and correspondence, to train or fine-tune its underlying AI model, and where that data is physically stored.

Many standard SaaS agreements default to broad data usage rights unless a firm explicitly negotiates an exclusion clause, so a vague answer like “we take privacy seriously” is not the same as a defined data processing clause confirming Australian hosting and no model training use.

System Integration and Access Requirements

Check what level of access the tool requires to connect with your practice management system, document management system, or Microsoft 365 environment, and whether that access is read-only or allows the tool to write changes back.

A tool requesting broad administrative access across your systems carries a very different risk profile to one limited to specific, defined data, and this should be reflected in the permissions you grant during setup, not assumed from the marketing material.

Do not accept promised integrations on vendor claims alone; ask to see exactly what permissions and write-back actions are supported.

Liability for Incorrect AI Outputs

Most vendor contracts include a limitation of liability clause that caps the vendor’s responsibility for incorrect or harmful outputs, often at the value of the subscription fee.

This is standard across the industry. So the more useful exercise isn’t trying to negotiate that clause away. It’s confirming that your firm’s professional indemnity cover is built around the assumption that the vendor will not be liable if something goes wrong. Your internal verification process needs to be built around that same assumption.

Vendor Stability and Contract Exit Terms

Look at the termination clause and data export provisions before signing, not after a problem arises. Confirm how much notice is required to exit the contract, whether your firm’s data can be exported in a usable format if you switch tools or the vendor is acquired, and whether early termination penalties apply.

A vendor reluctant to commit to clear exit terms in writing is telling you something about how confident they are in their own retention.

Compliance and Professional Body Alignment

For law firms, this means checking the tool’s data handling against the Legal Profession Uniform Law, your professional indemnity insurer’s requirements, and your state law society’s AI guidance.

For accounting firms, it means checking against APES 110 and Tax Practitioners Board expectations.

Ask the vendor directly whether other Australian law firms or accounting practices currently use the product, since a vendor with established Australian clients in your industry has usually already worked through some of these questions.

Privacy Policy and Client Disclosure Updates

Confirm whether adopting the tool will require updates to your firm’s privacy policy, particularly given the requirement under recent Privacy Act amendments to disclose when decisions are made using substantially automated processes.

This is also the point to decide whether your firm will proactively tell clients when AI has materially assisted with their matter, rather than leaving that decision to be made informally case by case.

Internal Accountability for Verifying Output

A contract review is incomplete without deciding, in writing, who within the firm is responsible for checking AI-generated output before it reaches a client or the court.

This doesn’t need to sit in the vendor contract itself, but it should be documented as part of the firm’s internal AI usage policy at the same time the tool is approved, so accountability is clear from day one rather than assumed after something goes wrong.

None of this needs to slow the buying process down by months. Most of it can be resolved with two or three direct questions to the vendor and a short internal discussion between partners and IT.

What matters is that the answers are documented before the contract is signed, not reconstructed afterward when a regulator or insurer asks how the decision was made.

How Long Should You Pilot an AI Tool Before Rolling It Out Firm-Wide?

Even after a tool clears the questions above, plan a pilot test before full adoption and roll it out to a small group first, ideally five to ten staff across different roles, for four to six weeks, though some firms may extend testing for a few weeks more if usage is still inconsistent.

For larger or higher-risk rollouts, use a 30, 60, 90 day pilot framework.

Define success measures before the pilot starts, and make sure they tie to business outcomes. A pilot should also test whether the tool can genuinely boost productivity in day-to-day work, not just in demos.

Track three things during the pilot. First, actual usage frequency — not just licence activation. Second, staff feedback on whether it’s helping them save time. Third, at least one or two concrete before-and-after metrics on a specific task, so you can compare time saved and output quality before and after the trial.

A tool that looked impressive in a demo but barely gets opened during the pilot has told you everything you need to know. Firm-wide licensing decisions made without a pilot are one of the most common ways firms end up with expensive software nobody opens.

Why Does Staff Training Matter as Much as the Tool Itself?

Even the best-chosen AI tool delivers little value if staff aren’t trained on how to use it within your firm’s specific workflows and compliance requirements. This matters especially when teams are expected to use multiple AI tools without losing context or consistency.

Ease of adoption also depends partly on usability, interface quality, and the learning curve. Some teams also need training on working across multiple AIs or platform features without losing context across matters.

The tool selection process and the training rollout need to be planned together, not treated as separate projects months apart, because available vendor or internal support affects whether adoption sticks and helps teams maintain focus.

This includes training staff on what not to put into an AI tool, since the most common confidentiality incidents involve well-meaning staff pasting sensitive client information into a tool that was never vetted for that purpose.

Training should also cover when free versions are too limited for professional use and when a paid tier is required.

Common Mistakes Firms Make When Choosing an AI Tool

A few patterns show up repeatedly when firms get this wrong.

Judging AI Models on a Demo Instead of a Trial

Many AI tools offer free trials or a free plan that makes hands-on testing far more useful than a polished demo. Vendor demonstrations are built to impress, using clean, simple example data — real client files are messier, and that’s where AI models either hold up or fall apart.

Read reviews to gather valuable insights into strengths and weaknesses before choosing a paid version. Don’t assume the most expensive plan is the best AI tool if it doesn’t fit your workflow.

Skipping the Data Residency Question Because the Salesperson Seemed Trustworthy

A confident answer isn’t the same as a documented one. Get it in writing.

Licensing Firm-Wide Before Piloting

This is the single most expensive mistake on this list.

Treating the Tool Decision and the Training Decision as Separate Projects

A tool selected without a training plan attached to it almost always underperforms its potential, regardless of how good the underlying product is.

Assuming a Tool’s Compliance Is “Someone Else’s Problem” Once It’s Purchased

Vendor terms typically push liability for verifying outputs back onto the firm, which means ongoing internal process matters more than the initial vetting.

Buying several overlapping AI companies’ products can leave staff switching between multiple tools with little extra value, without giving the firm any real competitive edge over practices that chose one tool and used it properly.

Why Does Choosing the Right AI Tool Matter More for Law and Accounting Firms?

Choosing the wrong project management app is annoying. Choosing the wrong AI tool in a law or accounting firm can create a professional indemnity claim, a regulatory complaint, or a breach of client confidentiality.

The stakes are genuinely different from a typical software purchase. Tools for your business should be judged against specific project needs rather than generic software criteria.

The challenge is not just AI itself, but choosing among fast-moving digital technology with very different risk profiles.

The risk isn’t theoretical. Australian courts have already dealt with multiple instances of lawyers submitting AI-generated material containing fabricated case citations and invented quotes.

One example is a 2025 matter in the Supreme Court of Victoria, where AI-generated submissions in a serious criminal matter contained non-existent judgments and fabricated parliamentary quotes, causing a delay in proceedings.

A Victorian solicitor was separately sanctioned by the Victorian Legal Services Board over fabricated citations, in what regulators described as the first disciplinary action of its kind in Australia. Several more matters involving fabricated AI citations have since been referred to state legal regulators around the country.

The accounting side carries a quieter but equally real version of the same risk.

Industry research has found that while a large majority of Australian accounting firms are already using some form of AI, only a minority have a formal AI governance policy in place. That leaves a meaningful gap between how AI is actually being used day to day and how firms can demonstrate they’re meeting their professional obligations if a regulator or client asks.

Firms also need to understand how these tools behave inside complex systems before trusting them with client work.

None of this means firms should avoid AI. It means choosing the right AI tools requires evaluating specific project needs, not judging a fifteen-minute demo. The right choice should make day-to-day work life easier, not add more friction or oversight burden than it removes. That’s exactly what the next section is built around.

What Has Changed in Australia’s AI Compliance Rules Recently?

The compliance environment around AI in professional services has moved quickly, and several changes are directly relevant to how firms should evaluate tools right now.

For accountants, the Accounting Professional and Ethical Standards Board added principle-based technology provisions to the APES 110 Code of Ethics, effective from 1 January 2025, specifically to address the ethical use of AI tools by members of CPA Australia, Chartered Accountants Australia and New Zealand, and the Institute of Public Accountants.

The Tax Practitioners Board has since gone further, issuing draft guidance specifically addressing how AI use interacts with the Code of Professional Conduct under the Tax Agent Services Act, making clear that practitioners remain personally accountable for the accuracy of AI-assisted advice regardless of which tool produced it.

For lawyers, the Queensland Law Society became the first law society in Australia to issue formal AI guidance for solicitors.

Other state bodies, courts, and the Law Council of Australia have since published their own guidance or practice notes, and several courts now expect disclosure when AI has materially assisted with submissions.

For both professions, the Privacy and Other Legislation Amendment Act 2024 introduced the most significant changes to the Privacy Act 1988 in over a decade. Two changes stand out: a new statutory tort allowing individuals to sue directly over serious invasions of privacy, and a tiered penalty regime with maximum fines reaching into the tens of millions of dollars for corporate breaches.

A provision specifically relevant to AI tools requires entities to disclose in their privacy policies when decisions are made using substantially automated processes, with firms required to have this in place by December 2026, which is worth getting ahead of now rather than scrambling for later.

Separately, the Cyber Security Act 2024 introduced Australia’s first standalone cyber security law, including mandatory reporting obligations for ransomware and cyber extortion payments for businesses above a turnover threshold.

While many smaller firms sit below that specific reporting threshold, the Act signals a broader regulatory direction toward formal cyber risk management that insurers, regulators, and increasingly clients now expect firms to demonstrate — AI vendor due diligence included.

The practical takeaway is that “we use a reputable AI tool” is no longer a sufficient compliance answer on its own. Firms are increasingly expected to show their evaluation process, their internal verification steps, and their data handling decisions.

How Does XCD IT Help Brisbane Law and Accounting Firms Choose AI Tools?

XCD IT helps law firms and accounting practices across Brisbane and South-East Queensland in a few specific ways. We compare many AI tools, including support across ChatGPT- and Claude-style workflows when different teams need different strengths.

We help choose the right option for each department or use case, rather than assuming one platform fits all. We tie each option back to clear security requirements and compliance needs. And we deliver the staff training that makes adoption actually stick. That can include support for a finance team using AI to reduce manual reporting or reconciliation work.

We also help firms review tools they’ve already adopted. This means identifying gaps in data handling, integration, fit with the broader tech stack, or compliance that may not have been obvious at the time of purchase. It can also include coordinating multiple AI tools, or evaluating Claude/Gemini-style model choices within the broader tech stack.

Where needed, that can mean advising on specialist options such as Claude Pro, Claude Code, Custom GPTs, or deep research features.

Frequently Asked Questions: AI Tools for Law and Accounting Firms