Summary

• Kantata has introduced Expertise Insights, built on Amazon Quick, to generate business intelligence from professional services information through natural-language interaction.
• Four standard dashboards cover finance, delivery, time and resourcing, while Ask Insights supports additional analysis and reusable reporting views.
• The product is available and already used by customers, according to Kantata, but evidence of productivity improvements remains limited to company-reported experience.

Professional services software provider Kantata has introduced Expertise Insights, a business intelligence product designed to let employees investigate project performance, resource allocation and financial results through natural-language questions.

Launched on 20 October during Technology & Services World in Las Vegas, the product combines Amazon Quick with a semantic layer that interprets the operational terminology and data relationships used by individual professional services organisations.

The system is integrated into Kantata’s Expertise Engine and provides financial and operational reporting without requiring users to write database queries or construct dashboards manually.

Kantata says the product is commercially available and already being used by customers. Its release extends the company’s earlier work on agentic business intelligence, with the October launch establishing a named product and additional detail about its capabilities.

Business terminology shapes the analytics

Professional services organisations rely on information about projects, employee assignments, utilisation, customer contracts and financial performance to manage their operations.

Those records can sit across professional services automation software, accounting applications, customer management systems and other operational tools.

Although the information needed for a decision may already exist, producing an accurate analysis often requires knowledge of how the records relate to one another.

A manager investigating declining project margins, for example, may need to examine contracted revenue, staff costs, chargeable time, project scope and the timing of work undertaken.

Different organisations can define these measures differently. Chargeable utilisation and billable utilisation may have distinct meanings within a firm, while commercial rates and methods of allocating costs can vary between business units.

Kantata’s semantic layer maps technical fields onto terminology used within the customer’s business. When someone asks a question in ordinary language, the system uses those definitions to determine which information and calculations are relevant.

This approach is intended to reduce the need for specialist knowledge of database structures. Employees can request a financial or operational analysis without first identifying every table and field required to produce it.

The quality of the result nevertheless depends on the records and definitions available. An apparently straightforward question about profitability may require assumptions about how costs are allocated or whether outstanding work should be included.

Organisations will therefore need to establish that the semantic model reflects their approved reporting definitions and remains accurate when those definitions change.

Four dashboards provide standard reporting

Expertise Insights includes four dashboards covering finance, delivery, time and resourcing.

The finance dashboard presents information about margins, revenue per employee and potential cost leakage. Delivery reporting covers project health, including schedules, scope, budgets and client relationships.

The time dashboard examines utilisation and timesheet compliance, while resourcing provides information about skills, capacity and demand.

These categories are connected in the operation of a services business. Project profitability can change when additional work requires more staff time, while resourcing shortages may affect delivery schedules and customer commitments.

Presenting the information together could help managers investigate those relationships without manually combining several reports.

Users requiring analysis beyond the standard dashboards can use Ask Insights, which generates visualisations, calculations and summaries in response to natural-language requests.

Kantata also says users can assemble presentation material, including board reporting, from the information returned by the system.

Bookmarks and Pinboards allow employees to preserve filtered views and individual analyses without creating additional versions of the underlying dashboards.

That arrangement may help organisations maintain consistent reporting, although saved analyses still need to reflect changes in source information, business rules and access permissions.

Permissions are checked before analysis

Making analytics easier to use also changes how employees encounter commercially sensitive information.

Professional services systems can contain client agreements, confidential project details, employee records and financial information that should not be accessible to every user.

Kantata says Expertise Insights applies role-based business permissions before executing an analytics request. Its technical documentation describes this as a deterministic authorisation process intended to restrict the information available to each user.

Such controls matter because a natural-language interface allows employees to request analyses that might previously have required assistance from a specialist reporting team.

A project manager may need information about assignments and budgets within their own portfolio, while a senior executive could require an aggregated view across several business units.

Access controls need to apply to the records used in producing an answer, not simply the dashboard in which it appears.

They must also account for the possibility that an aggregate calculation reveals commercially sensitive information where only a small number of projects or employees contribute to the result.

Customers will need to test whether the software applies their existing permissions consistently across standard dashboards, generated reports and information retrieved from connected systems.

Kantata’s architectural description identifies the intended controls, but the effectiveness of a particular deployment will depend on its configuration and the customer’s own governance requirements.

Customer experience provides early evidence

Kantata cites professional services customer Lumanity as an early user of Expertise Insights.

Andy Newbill, the company’s senior manager of professional services automation, describes being able to construct reports and dashboards more easily using natural-language instructions.

In one example, Newbill said he developed a complex report involving calculated fields in approximately an hour, compared with several hours using the previous approach.

That experience provides an account of the product operating in a real business, although it does not establish average time savings or financial returns across the wider customer base.

Faster report creation can reduce administrative work, but employees still need confidence that the resulting calculations are correct and suitable for the decisions being considered.

Where analysis affects customer pricing, staffing or project profitability, organisations may retain review procedures even if the initial report can be produced without a specialist analyst.

The appropriate level of review will depend on the quality of the underlying records, the complexity of the calculation and the potential consequences of acting on an incorrect result.

Integration becomes a commercial differentiator

Kantata is entering a business intelligence market in which established software providers already offer natural-language queries, automated reporting and AI-assisted analysis.

Its proposed distinction lies in combining these capabilities with professional services data and the operational context maintained within its existing platform.

Building Expertise Insights on Amazon Quick provides an analytics foundation, while Kantata supplies the semantic layer and the connection to its Expertise Engine.

The arrangement may reduce the need for customers to construct a separate reporting environment, although practical integration requirements will vary according to the systems and datasets each organisation uses.

Kantata had announced generative business intelligence functionality as part of the Expertise Engine in June 2026, alongside broader agent capabilities. The October release represents a further product development within that programme rather than its first announcement of AI analytics.

Its commercial performance will depend on the reliability of natural-language analysis, the consistency of permission enforcement and the ability of customers to obtain useful results without extensive configuration or manual correction.

The company has not published a comprehensive independent assessment of productivity improvements associated with the launch. Wider customer adoption and comparative operational evidence will establish whether easier access to reporting produces measurable improvements in professional services management.