The Total Economic Impact™ Of ChapsVision Sinequa AI-Powered Search  

Labor Cost Avoidance From AI-Enabled Automated Processes

Evidence and data. The largest benefit the interviewees’ organizations experienced by investing in Sinequa was the reduced time and resource effort required to complete large-scale documentation reviews. Sinequa enabled this by ingesting and indexing large volumes of documentation at scale, so reviewers could locate and validate evidence without manually handling each file. Capabilities such as document conversion across many formats and AI-driven extraction turned previously hard-to-search content into searchable text that could be reviewed consistently and quickly. Interviewees also shared that external regulatory or oversight requests typically triggered these review activities, requiring their organizations to submit consolidated, auditable information spanning multiple business units and product lines and creating large volumes of documents that needed review and validation.

Interviewees explained that because they needed to complete these requests within short submission windows, their organizations historically relied on external contractors to scale capacity quickly and meet deadlines. Interviewees indicated typical submission cadences of three to six months.

• The principal technical architect for AI systems at a manufacturing organization explained, “We did … bulks of 50,000 parts at a time … and we’ve hired a lot of contractors in the past.”

• The same interviewee mentioned that by using Sinequa, they were able to complete these activities at a rate “that would have taken a contractor … months.”

• The principal technical architect for AI systems at a manufacturing organization also noted that review volumes scaled quickly when requests spanned multiple product lines: “Those 25,000 prints could just be one product that we sell. We have hundreds of products.”

• Interviewees emphasized that once key sources were indexed and review workflows were repeatable, their organizations reduced the need to restart full manual reviews by maintaining ongoing coverage and reviewing only what changed.

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• The composite receives recurring external requests that require large-scale documentation review and validation spanning multiple business units and product lines, covering 750,000 documents.

• Before investing in Sinequa, the composite relies on manual contractor review with a baseline throughput of 40 documents per day.

• As more content is indexed through Sinequa, internal users review an increasing share of documents through the platform, from 15% in Year 1 to 100% in Year 3, which replaces manual reviews by external contractors.

• Contractor labor is modeled at $800 per day, consistent with an 8-hour workday at $100 per hour.

Risks. The impact of this benefit could be lower because:

• The rate of expansion of indexed sources and data coverage can affect adoption and limit the proportion of work that can be completed through Sinequa-enabled workflows.

• The cost of external contractors may be higher than assumed.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $16.5 million.

Search Productivity Gains

Evidence and data. Interviewees explained that prior to their organizations’ Sinequa investment, technical knowledge workers spent a substantial amount of time searching across fragmented repositories and systems to locate the right information to complete daily tasks and make decisions. Sinequa improved search productivity by using hybrid search, which combines traditional keyword search with neural search to surface relevant results even when users do not know the exact terms. This helped users retrieve the right information faster across multiple systems rather than searching each repository separately. In several organizations, users had to search multiple tools, rely on data hygiene practices such as meta data categorization in the form of IDs or local context, or depend on a “who to ask” approach, which created inefficiencies and inconsistencies in outcomes.

Interviewees said that by investing in Sinequa, their organizations improved the ability of users to find relevant information faster using a single knowledge engine that spanned multiple sources, which reduced time spent searching, consolidating, and validating information.

The senior VP and head of R&D at a life sciences organization described how the platform supported rapid preparation for decisions by making key information accessible in a reusable way. They said, “You can walk into any conversation — just click the link and it has all the information available.”

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• Prior to investing in Sinequa, the composite has 4,000 in-scope technical knowledge workers by Year 3, who frequently search across multiple systems as part of their daily work.

• Before Sinequa, the composite assumes these users spend an average of 13% of their day on search and knowledge discovery activities, equivalent to 21 hours per month.

• With Sinequa, the composite achieves a 10% improvement in productivity for these activities in Year 1 (e.g., reduction in time spent searching and validating information), which improves to 20% by Year 3 as more content is indexed into the platform.

• The average fully burdened hourly rate for an in-scope knowledge worker is $64.

• Forrester applies an 80% productivity capture rate to these efficiencies because not all time saved goes back into productive effort.

Risks. The impact of this benefit could be lower because:

• The portion of the workforce that is in scope and actively uses Sinequa for knowledge discovery may be lower. Lower adoption or inconsistent use will reduce time savings and limit measurable impact.

• The realized percentage improvement in search productivity, which depends on indexed source coverage and the relevance of the indexed content to end users, may be lower.

Results. To account for these risks, Forrester adjusted this benefit downward by 15%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $10.8 million.

Production Facility Compliance Case Management Efficiencies

Evidence and data. Interviewees explained that prior to their organizations’ Sinequa investments, operational management teams spent a considerable amount of time preparing for and executing recurring operational compliance reviews for their production sites because the required evidence and procedures were distributed across multiple repositories and formats. Sinequa supported these recurring compliance activities by enforcing secure, permission-based access while helping teams retrieve the right policies, procedures, and evidence quickly. In practice, this reduced the manual effort of assembling documentation because teams could search across approved sources while maintaining the same access controls as the original systems. They said that these activities typically required teams to locate the right rules, procedures, and supporting documentation; validate that the content is current; and present evidence for internal or external stakeholders. Interviewees noted that Sinequa improved their ability to retrieve and validate documentation faster, which reduced the administrative burden associated with compliance preparation and execution.

Interviewees explained that this benefit reflected recurring, operational compliance work that is part of business as usual (i.e., repeated preparation and evidence gathering across operational units), rather than a single large-scale regulatory submission that triggers bulk review work performed under short deadlines with external contractors.

The senior digital expert of cognitive search at a chemicals organization described how these compliance workflows related to required tasks to ensure their organization had the “license to operate”: “We have the topic around improvements of quality, compliance, and the license to operate.” They also explained that these compliance activities occurred at least twice throughout the year and involved multiple preparation hours and reviews.

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• Prior to investing in Sinequa, the composite conducts recurring operational compliance reviews across multiple operational sites/locations, modeled as 10 sites in Year 1, up to 40 sites in Year 3.

• The composite assumes two operational compliance reviews per site per year, where teams must find, validate, and present required documentation.

• The composite estimates total annual time savings of 2,400 hours in Year 1, up to 9,600 hours in Year 3, depending on the number of sites and the reduction in time spent searching and validating compliance documentation.

• The average fully burdened hourly rate for a production plant management FTE is $79.²

• Forrester applies an 80% productivity capture rate because not all time saved goes back into productive effort.

Risks. The impact of this benefit could be lower for an organization because:

• The amount of documentation required per event and how consistently content sources are indexed and maintained over time may be higher.

• The level of adoption by operational teams and the extent to which Sinequa becomes embedded into standard workflows for compliance preparation may be lower.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $824,000.

Customer Support Duplication Search Efficiencies

Evidence and data. Interviewees stated that prior to their organizations’ Sinequa investment, customer support users (i.e., teams responsible for responding to customer technical issues) spent a significant amount of time searching across multiple repositories to determine whether a customer issue had been previously addressed and to locate the right supporting documentation. Sinequa enabled efficiencies by making historical cases and documentation searchable in one place regardless of language and using passage/snippet-level retrieval so agents could jump directly to the most relevant part of a document. This helped customer support users confirm whether an issue had already been handled and reuse prior resolutions more quickly. In this context, interviewees emphasized that a distinct user population within one organization experiences these benefits (e.g., customer support and customer-facing technical teams), rather than the broader internal technical knowledge worker base that drives the search productivity gains benefit.

• The business architect of technical support at an aerospace and defense organization described how support teams previously had to search across separate databases and sources but now had a more consolidated experience: “Now they have all-in-one search. … Maybe before they were taking 1 hour to search in all software and databases of information, while today it’s 10 to 15 minutes.”

• The same interviewee explained that a primary use case is reusing prior answers, saying, “The use case… is to find … similar questions we may have answered already in the past.”

• Interviewees also noted that enabling faster discovery of prior resolutions could reduce repeated investigations and duplicated work across support teams, even when the organization could not always measure avoided customer tickets directly (e.g., when customers self-serve without logging a case).

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• The use case and relevant content available for customer support and customer-facing technical teams is indexed and available from Year 2.

• Prior to investing in Sinequa, the composite processes 32,000 to 35,000 customer support tickets in Year 2 and Year 3, respectively.

• It is assumed that 15% of tickets in Year 2, up to 18% in Year 3, represent duplicative or repetitive search efforts (e.g., issues that have prior precedent and require time to rediscover prior guidance).

• Before Sinequa, the composite assumes customer support users spend 1 hour searching for relevant prior cases and documentation to resolve a ticket; with Sinequa, this decreases to 0.15 hours per applicable ticket.

• The average fully burdened hourly rate for a customer support FTE is $41.

• Forrester applies an 80% productivity capture rate to these efficiencies because not all time saved goes back into productive effort.

Risks. The impact of this benefit could be lower because:

• The degree to which customers and agents use the platform consistently as the first step in issue triage (e.g., adoption and workflow embedment) may be lower.

• The baseline complexity of the knowledge environment (e.g., the number of repositories and fragmentation), which influences how much time is saved by consolidating search, may be lower.

Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $193,000.

Application Development Labor Cost Avoidance

Evidence and data. Interviewees explained that prior to their organizations’ Sinequa investment, application and IT teams expended ongoing effort and incurred costs to build, maintain, and update custom integrations and UIs to make enterprise data searchable and usable. Sinequa reduced application development effort by providing out-of-the-box connectors and a single indexing layer that could be reused across multiple applications. This meant teams could build new use cases on the same underlying data foundation rather than building integrations and search layers from scratch each time. In many cases, teams maintained separate search layers or bespoke applications for different functions because data sources were fragmented and could not be reused easily. Interviewees said that by investing in Sinequa, their organizations reduced application development effort by leveraging a shared platform with reusable connectors and a single indexing layer, which limited the need to rebuild integrations and search functionality for each new use case.

• The senior digital expert of cognitive search at a chemicals organization explained, “Once the content is indexed, we can reuse it again and again across applications.”

• The same interviewee noted that without a shared platform, each new use case would have required separate development effort, saying, “Otherwise, every department would implement its own solution.”

• All interviewees emphasized that this benefit was primarily realized by centralized IT and application development teams rather than end users, and it accrued gradually as more use cases were delivered on top of the same platform foundation.

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• Prior to investing in Sinequa, the composite develops and maintains multiple search-enabled applications, each requiring dedicated integration and development effort. The composite develops five new applications per year with development labor requirements of two FTEs per application.

• With Sinequa, the composite avoids incremental development effort by reusing existing connectors and a single indexing layer across new applications.

• The composite experiences a shorter application development cycle, saving 80 hours per application created.

• The average fully burdened hourly rate for an IT FTE is $85.

Risks. The impact of this benefit could be lower because:

• There are differences in the delivery model and labor rates (e.g., internal versus partner-led, onshore versus offshore) affecting the dollar value of time saved.

• Higher skill/complexity requirements to configure, maintain, and update integrations and applications may reduce the hours avoided per application.

Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $161,000.

Knowledge Consolidation

Evidence and data. Interviewees explained that because enterprise knowledge was previously fragmented across multiple tools, repositories, and department‑specific systems, creating relevant applications to locate and reuse information consistently across their organizations was difficult. In many cases, teams adopted their own search tools or maintained separate applications to access the content they needed, resulting in duplicated effort and inconsistent access to historical knowledge. Sinequa enabled consolidation by creating a centralized enterprise index that made content discoverable across previously separate tools and repositories. This helped organizations reduce fragmentation because different teams could retrieve knowledge through one search layer, while still respecting existing permissions.

Interviewees said that by investing in Sinequa, their organizations consolidated access to enterprise knowledge through a single platform, which reduced fragmentation and improved reuse of existing information assets.

• The senior VP and head of R&D at the life sciences organization described the preinvestment environment as fragmented: “Previously, everybody was using their own tools, document repositories, and other systems. It was a very fragmented space.”

• The senior digital expert of cognitive search at a chemical organization explained how Sinequa changed this dynamic at their company, saying, “Once the content is indexed, we can reuse it again and again across applications.”

• Interviewees emphasized that this consolidation did not require migrating or replacing existing systems but instead provided a unified layer for discovering content regardless of where it resided, which made historical and current knowledge accessible to a broader set of users.

Modeling and assumptions. Based on the interviews and model structure, Forrester assumes the following about the composite organization:

• Prior to investing in Sinequa, the composite maintains multiple overlapping search tools or access mechanisms across departments to locate enterprise knowledge.

• With Sinequa, the composite consolidates access to enterprise content through a single, centralized search and retrieval layer, reducing reliance on parallel tools.

• The composite assumes that consolidation enables the retirement or reduced usage of a small number of legacy tools or custom applications over time, while preserving access to underlying historical data.

• The composite applies conservative savings assumptions to reflect that consolidation is gradual and occurs as confidence in the platform increases.

Risks. The impact of this benefit could be lower because:

• The number and type of legacy tools in place prior to the Sinequa investment may be lower.

• An organization may be less able to standardize on a single enterprise knowledge platform versus allowing continued use of parallel solutions.

• Change management considerations, including user adoption and governance decisions around tool consolidation, may vary.

Results. To account for these risks, Forrester adjusted this benefit downward by 20%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of $1.3 million.

Unquantified Benefits

Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:

• Improved employee confidence and satisfaction. With Sinequa, interviewees noted that their organizations experienced improved employee confidence and satisfaction. Sinequa’s ability to return more relevant answers through neural search and hybrid retrieval supported this improvement, which reduced the frustration of “searching but not finding.” As users consistently located what they needed, confidence increased because the platform feels dependable for daily work. Interviewees explained that by providing more reliable, relevant, and intuitive access to enterprise knowledge, Sinequa reduced frustration associated with searching across fragmented tools and increased users’ confidence in their ability to perform their roles effectively. Interviewees also noted that improvements in confidence and satisfaction were visible through user feedback and adoption patterns, which helped reinforce trust in the platform and encouraged continued use. This, in turn, contributed to increased tenure, higher productivity, and increased engagement. The business architect for technical support in the aerospace and defense industry explained that improved access to information changed how experts spent their time and allows their experts to focus on higher-value tasks instead of unproductive searching.

• Improved organizational culture related to use of data. Interviewees noted that their organizations experienced an organizational mindset shift in data quality, access, and risk. Sinequa’s ability to index across multiple systems while enforcing security enabled this shift, which made information easier to find and inconsistencies more visible. As a result, organizations could identify content that was not stored in the right place or not governed properly and address those issues rather than letting them remain hidden.

  • The principal technical architect of AI systems for a manufacturing organization explained that the platform did not introduce new risks but did expose existing ones. They further described how indexing multiple systems revealed those documents stored outside their respective locations, prompting the need to address governance and stewardship gaps rather than ignore them.
  • The senior digital expert of cognitive search for a chemicals organization also described an improvement in their organization’s culture around data hygiene and management and that teams were now placing greater emphasis on where information is stored, how it is documented, and how it can be reused — highlighting that good data is recognized as a prerequisite for good results rather than an afterthought.

Flexibility

The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement Sinequa AI-Powered Search and later realize additional uses and business opportunities, including:

• Foundation for agentic workflows. Interviewees noted that once enterprise content was indexed, governed, and made accessible through semantic and neural search, their organizations could begin to explore more advanced, agent‑based workflows that built on this foundation. Rather than deploying generic AI tools, interviewees emphasized the importance of grounding future agents and assistants in curated, domain‑specific enterprise knowledge to ensure relevance, accuracy, and trust. This flexibility is supported by Sinequa’s AI assistant and agent-enablement capabilities, which allow organizations to build more advanced workflows on top of indexed enterprise knowledge. Over time, this can enable domain-specific assistants or agents to retrieve and use trusted internal information rather than relying on generic AI responses.

• Foundation for multiple, scalable use cases throughout the organization. Interviewees noted that once previously inaccessible or unstructured content was indexed and governed, the platform could be reused to support a growing range of use cases across different functions and user groups. Rather than being tied to a single workflow or department, Sinequa provides a shared foundation that enabled teams to apply the same knowledge layer to new problems as needs evolved. Sinequa’s extensibility via plug-ins and APIs drove this flexibility, which allowed new use cases to be added without rebuilding the platform foundation. As more sources are indexed, teams could reuse the same knowledge layer for new applications across functions, allowing value to compound over time.

  • Interviewees described that Sinequa’s reuse model allowed value to compound over time since each additional dataset or use case built on the same platform rather than starting from scratch.
  • Interviewees also noted that this flexibility supported adoption across a broad range of roles, from technical specialists and analysts to business users and support teams. While many organizations began with a small number of high‑priority use cases, they viewed the platform as a long‑term capability that they could extend incrementally, enabling new applications and workflows as organizational priorities changed and confidence in the platform grew.

Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Total Economic Impact Approach).