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Intapp announces enhancements to Time, Walls and Assist products

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1 year ago

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Professional services solutions provider Intapp, during its Intapp Amplify event in New York City on Feb. 26, announced improvements and upgrades to its solutions for time and billing, cybersecurity and AI assistant. 

Intapp Time

Intapp Time, the company’s time tracking and billing solution, now sports an improved interface in a new, modern web experience, said Beth Cuzzone, vice president of growth marketing during her presentation. 

It also now sports an AI-driven activity log that automatically captures and lists all the user’s daily work activities, allowing them to easily and quickly complete timesheets without things like forgetting about weekends or teleconferences between meetings, as the software captures it all for them. Cuzzone also pointed out a new feature called Quick Add, which allows people to voice dictate what they are doing, and Intapp’s AI can turn that voice into text, and that text into a draft timesheet that is checked for compliance with client billing requirements. 

“Here, you can see the draft time entry includes the word ‘reviewed’ in the narrative. The client does not allow this language in the narrative and will likely reject the invoice. The AI highlights the word or phrase with a warning message, indicating an issue, and even suggests alternatives that comply with the client’s billing requirements,” she said. 

The AI offers a list of acceptable suggestions in cases like this, and if the partner wants more information they can review the guideline that triggered the warning in the first place. Cuzzone noted that timing errors may not seem significant at first, but even the smallest amount of bill and time leakage can have cascading effects on a firm’s revenue. Intapp time, she said, is intended to mitigate this challenge.  

“Intapp’s applied AI, firms can realize millions of dollars they otherwise would have lost. This also supports strategic growth and impacts profits for partners—all by leveraging the data you already have and without requiring professionals to do anything differently,” she said. 

The new Time experience will be released this summer. People can either keep using the existing desktop app or use the new web experience. They also plan to make it available on their mobile app eventually as well. 

Intapp Walls

Meanwhile, Intapp Walls, the company’s data privacy solution, was also enhanced with AI in cooperation with Microsoft, according to Richard Bowes, senior compliance growth director with Intapp who, previously, spent eight years at Microsoft. He said that Walls is designed for CIOs who want to bring the capabilities of AI to end users, but also need to protect against threats and inappropriate internal access, noting that it can be easy to unintentionally overshare. Walls is meant to put up, well, walls that ensure Copilot and AI only reveal the right information to the right people.

“Walls operates at an engagement level for project based industries. It knows the deals and engagement that content belongs to. That understanding of the engagement metadata and what content is associated with. It is what we call engagement context, it protects your most important confidential business data. Engagement context enables wars to manage and enforce access permissions to ensure that neither humans nor digital actors such as CO pilots or large language models can inappropriately access or share confidential information in your deals, matters or engagements,” he said during his presentation. 

One of the biggest changes to the solution has been the addition of numerous new connectors, particularly for Microsoft products, particularly OneDrive, which he said makes things especially easy for a user to unintentionally move sensitive content from a secure server to an insecure laptop. So now they are using connectors to help firms deploy a single centralized system to identify and protect sensitive engagement information across the whole Microsoft 365 ecosystem. Beyond dozens of connectors, he also touted an API to extent Walls to any system that contains sensitive information at all. 

“If it contains sensitive information and It’s plugged in, Walls can secure it,” he said. 

Walls has also been enhanced with new monitoring instruments to assess and track oversharing risks by repository, client engagement or geographic location. This means that professionals can identify and proactively address the highest risk areas of their data, as well as receive “a little nudge” to secure areas that are less protected. 

“You don’t have to go to sleep wondering if your clients’ secrets are safe. Walls will show you,” he said. 

Intapp Assist

Finally, Melanie Fisher, Intapp’s senior product manager, went over improvements to Intapp Assist, the company’s generative AI assistant. She said that the Smart Tags feature has been significantly improved since it was first previewed last year. Smart Tags, she said, scan the cloud and automatically identify companies and contacts mentioned, and link the information to the relevant records—making it accessible across the firm. “It’s like having an assistant who reads all your notes in real-time and adds an @ mention to every relevant company or contact. It’s seamless and simple. Assist can instantly bring critical intelligence to every member of the firm who should have access to it,” she said. 

Intapp Assist now also features a new Prompt Studio. While Assist is very powerful, she conceded that every company is unique and has needs that cannot be addressed by a one size fits all approach. This is why they released the Prompt Studio, which allows people to bolster Assist’s capabilities with custom prompts specific to the user. 

She brought up a hypothetical example of someone named Kate, a partner at a multi-strategy investment firm. Kate is focused on making investments for the firm’s private credit strategy. She asks if Intapp Assist can find credit-related information on a company. Her supervisor, Mark, goes into Prompt Studio, where he sees that there are built-in tips for writing effective prompts. He can use the copy from an existing prompt or create a new one. He fills in basic information and selects the type of task he wants to tailor (in this case, summary.) He assigns the AI a role familiar with a private credit partner, then chooses the data his instructions will apply to. Next, Mark describes the AI’s task, giving it specific instructions on the types of information he is interested in, such as EBITDA, free cash flow, or debt service ratios. Once configured, he is ready to test. He filters the dataset through a realistic example and clicks Generate to see the results.

“Just like that, the experience has been tailored exactly to what Kate needs to run her private credit business. That was so easy!” she said. 

Fisher also noted the solution’s new language capacities. She raised an example of a hypothetical worker named Caleb who works in the UK and whose team is pursuing a deal with a Japanese conglomerate. While reviewing deal information, he discovers that his colleagues took notes in Japanese, and no one there understands them. Given time zone differences, she said, it will be difficult to get everyone on a call to resolve this quickly. However, in this case the firm already configured a prompt to translate automatically.

“Firms aspire to grow along many dimensions, including geographic expansion—whether organically or inorganically. While English is the most commonly used language, as businesses cross borders, the need for multilingual collaboration naturally increases,” said Fisher. 

Prompt Studio, Assist can translate over 100 languages into English.

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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3 days ago

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August 15, 2026

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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1 week ago

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August 9, 2026

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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2 weeks ago

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August 5, 2026

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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