{"componentChunkName":"component---src-templates-client-work-js","path":"/case-studies/large-research-and-technology-company/","result":{"data":{"nodeClientWork":{"site_title":"Bounteous","drupal_internal__nid":97916,"title":"Large Research and Technology Company","field_client_work_type":"case-study","field_page_summary":{"processed":"

A large research and technology company partnered with Bounteous to transform quality control from a manual bottleneck into an AI-powered competitive advantage. Together, the teams developed A-EYE, an agentic AI quality-control solution that reviews analytics reports with the reasoning and consistency of the most experienced QC analysts. By combining hierarchical business rules, AI-powered visual and data validation, contextual market intelligence, and human oversight, A-EYE enables faster, more consistent report reviews while preserving analyst expertise and improving client delivery.

"},"field_tagline":{"processed":"Intelligent QC Automation at Scale"},"field_dlp_footer_expanded":false,"field_hide_contact_form":false,"field_hide_footer":null,"field_hide_navigation":null,"field_hide_search":false,"relationships":{"field_components":[{"__typename":"paragraph__video_hero","id":"f09035e5-005a-542b-84e7-e60ca3893b33","field_image_focal_point_x":null,"field_image_focal_point_y":50,"field_detail_show_eyebrow":false,"field_align_text":"center","field_banner_layout":"overlay","field_custom_icon_size":70,"field_theme":null,"field_headline_layout":null,"field_hero_logo_display":"logo_bounteous","field_hero_overlay_opacity":50,"field_hero_overlay_size":"full","field_icon_size":"large","field_logo_vertical_align":-5,"field_logo":null,"field_headline":null,"field_subtitle":null,"relationships":{"field_logo":null,"field_video":null,"field_background_image":{"localFile":{"childImageSharp":{"fixed":{"width":1920,"height":773,"src":"/public/static/5bc274d3f8361894f6c57d5172eda32e/1a900/scaled_quality_control_with_agentic_ai_hero.jpg","srcSet":"/public/static/5bc274d3f8361894f6c57d5172eda32e/1a900/scaled_quality_control_with_agentic_ai_hero.jpg 1x,\n/public/static/5bc274d3f8361894f6c57d5172eda32e/df5b8/scaled_quality_control_with_agentic_ai_hero.jpg 1.5x","srcWebp":null,"srcSetWebp":null}}}},"field_overlay_color":{"field_color":{"color":"#2e1869"}}}},{"__typename":"paragraph__2_column_component_group","field_breakpoint":"large","field_cg_background_color":"white_bg","field_cg_spacing_bottom":"medium","field_cg_spacing_top":"medium","field_column_direction":"leftToRight","field_column_spacing":"xlarge","field_column_vertical_align":"top","field_two_column_layout":"layout-50-50","field_component_width":"contained","field_overlap_top":null,"relationships":{"field_columns_2_column":[{"__typename":"paragraph__component_group","field_cg_headline":null,"field_cg_subheadline":null,"field_cg_link":null,"field_cg_cta":null,"field_cg_background_color":null,"field_cg_gradient":false,"field_cg_wave":false,"field_overlap_top":null,"field_overlap_top_width":null,"field_cg_spacing_top":null,"field_cg_spacing_bottom":null,"field_component_width":null,"field_id_attribute":null,"field_breadcrumb_links":[],"field_breadcrumb_links_position":null,"relationships":{"field_cg_components":[{"__typename":"paragraph__page_summary","id":"114c9101-cce8-550a-a1df-293418c0bc6f","field_cg_spacing_bottom":null,"field_cg_spacing_top":null,"field_hide_logo":false,"field_title":null,"field_summary_copy":null,"field_cta":null,"relationships":{"field_logo":null}}]}},{"__typename":"paragraph__component_group","field_cg_headline":null,"field_cg_subheadline":null,"field_cg_link":null,"field_cg_cta":null,"field_cg_background_color":null,"field_cg_gradient":false,"field_cg_wave":false,"field_overlap_top":null,"field_overlap_top_width":null,"field_cg_spacing_top":null,"field_cg_spacing_bottom":null,"field_component_width":null,"field_id_attribute":null,"field_breadcrumb_links":[],"field_breadcrumb_links_position":null,"relationships":{"field_cg_components":[{"__typename":"paragraph__page_tags_accordion","id":"bf6a2560-ccff-5353-b33e-082cfa628175","field_cg_spacing_bottom":null,"field_cg_spacing_top":null,"relationships":{"field_accordion_override":null}}]}}]}},{"__typename":"paragraph__component_group","field_cg_headline":"By The Numbers","field_cg_subheadline":null,"field_cg_link":null,"field_cg_cta":null,"field_cg_background_color":"white","field_cg_gradient":false,"field_cg_wave":false,"field_overlap_top":null,"field_overlap_top_width":null,"field_cg_spacing_top":"medium","field_cg_spacing_bottom":"large","field_component_width":null,"field_id_attribute":"case-study-client-testimonial","field_breadcrumb_links":[],"field_breadcrumb_links_position":null,"relationships":{"field_cg_components":[{"__typename":"paragraph__stats","id":"83966bfd-05c6-583f-a22e-dff9b223a7ba","relationships":{"field_stats":[{"field_stat":{"processed":"324"},"field_subtitle":{"processed":"reports"}},{"field_stat":{"processed":"18,000+"},"field_subtitle":{"processed":"issues identified"}},{"field_stat":{"processed":"$0.28"},"field_subtitle":{"processed":"cost per report"}}]}}]}},{"__typename":"paragraph__2_column_component_group","field_breakpoint":"medium","field_cg_background_color":"white_bg","field_cg_spacing_bottom":"medium","field_cg_spacing_top":"large","field_column_direction":"leftToRight","field_column_spacing":null,"field_column_vertical_align":"stretch_right","field_two_column_layout":"layout-50-50","field_component_width":"contained","field_overlap_top":null,"relationships":{"field_columns_2_column":[{"__typename":"paragraph__component_group","field_cg_headline":null,"field_cg_subheadline":null,"field_cg_link":null,"field_cg_cta":null,"field_cg_background_color":null,"field_cg_gradient":false,"field_cg_wave":false,"field_overlap_top":null,"field_overlap_top_width":null,"field_cg_spacing_top":"large","field_cg_spacing_bottom":"large","field_component_width":null,"field_id_attribute":null,"field_breadcrumb_links":[],"field_breadcrumb_links_position":null,"relationships":{"field_cg_components":[{"__typename":"paragraph__rich_text","id":"15b33ef6-0f67-5478-ab5e-098a9e5ac8a2","field_rich_text_body":{"processed":"

The Challenge

The research and technology company delivers thousands of customized analytics reports each week, helping consumer packaged goods manufacturers and retailers make critical business decisions. Before each report reaches a client, analysts must validate data accuracy, presentation quality, and business context to ensure every insight is both correct and actionable.

As reporting volumes increased, the quality-control process became increasingly difficult to scale. Experienced analysts relied heavily on institutional knowledge developed over years of reviewing client-specific reports. Much of that expertise existed only in individual team members' judgment, making reviews time-consuming, inconsistent, and difficult to replicate.

Every report required three distinct layers of validation. These included:

The manual process created several operational challenges. Senior analysts became a review bottleneck, delivery timelines placed additional pressure on weekend production cycles, and valuable institutional knowledge remained difficult to capture and share. Instead of advising clients, analysts often spent significant time researching why metrics had changed before they could complete report validation.

The team needed to automate repetitive quality-control tasks while preserving the expertise, judgment, and transparency that its users expected.

The Solution

Bounteous partnered with the research and technology company to design and implement A-EYE, an agentic AI quality-control platform that evaluates reports using the same reasoning process as experienced analysts.

Rather than simply applying static validation rules, A-EYE combines multiple AI capabilities into a coordinated quality-control workflow. A hierarchical rules engine cascades validation logic across global, client, document, and slide-level requirements, ensuring every report is evaluated according to both enterprise standards and customer-specific expectations.

The platform performs three complementary forms of validation.

For data quality, A-EYE detects anomalies using historical trends, seasonality, year-over-year comparisons, and configurable statistical thresholds. This enables analysts to identify unexpected values before reports are delivered to clients.

For visual quality, AI-powered computer vision evaluates rendered reports to detect truncated labels, alignment issues, formatting inconsistencies, and brand-compliance concerns that traditional data validation cannot identify.

To provide business context, the platform automatically researches market events, competitor activity, weather patterns, and other external factors that may explain significant metric changes. Rather than simply identifying anomalies, A-EYE helps analysts understand why performance shifted.

The solution was deployed within the company's hosted environment as an embedded, headless application supporting PDF, PowerPoint, Excel, and CSV workflows. Analysts authenticate through single sign-on, upload reports, configure review options, and receive detailed findings through an intuitive interface that supports overrides and human approval.

Importantly, A-EYE follows a human-in-the-loop model. AI surfaces recommendations and supporting evidence, while analysts remain responsible for reviewing findings, approving decisions, and continuously expanding the organization's institutional knowledge through configurable rule libraries and client-specific event calendars.

The Results

A-EYE transformed quality control from a manual review process into an intelligent, scalable capability that improves consistency while allowing analysts to focus on higher-value client work.

The solution is now live in production across multiple client engagements and continues to expand across additional report types.

During production use, A-EYE has reviewed 324 reports comprising more than 2,500 pages and slides, identifying over 18,000 quality issues at an average processing cost of just $0.28 per report.

The platform has also highlighted the importance of automated quality validation. Analysis found that 87 percent of reports contained more than one issue, while 73 percent included multiple critical issues that could have affected client deliverables if left undetected.

Beyond operational efficiency, A-EYE captures and scales the expertise of the company's most experienced analysts, creating a repeatable quality-control process that reduces review bottlenecks, improves consistency, accelerates report delivery, and enables analysts to spend more time delivering strategic insights instead of manually validating reports.

"}}]}},{"__typename":"paragraph__image","id":"2bbf928b-5249-5272-afdc-847edb57cc5c","field_image_focal_point_x":50,"field_image_focal_point_y":75,"relationships":{"field_image":{"localFile":{"childImageSharp":{"fluid":{"aspectRatio":1,"src":"/public/static/f8f19ce3e03e204e1e0dd0c8f65fc3d9/27c7f/scaled_quality_control_with_agentic_ai_image_2.jpg","srcSet":"/public/static/f8f19ce3e03e204e1e0dd0c8f65fc3d9/e2788/scaled_quality_control_with_agentic_ai_image_2.jpg 360w,\n/public/static/f8f19ce3e03e204e1e0dd0c8f65fc3d9/7d509/scaled_quality_control_with_agentic_ai_image_2.jpg 720w,\n/public/static/f8f19ce3e03e204e1e0dd0c8f65fc3d9/27c7f/scaled_quality_control_with_agentic_ai_image_2.jpg 1268w","srcWebp":null,"srcSetWebp":null,"sizes":"(max-width: 1268px) 100vw, 1268px"}}}}}}]}},{"__typename":"paragraph__component_group","field_cg_headline":null,"field_cg_subheadline":null,"field_cg_link":null,"field_cg_cta":null,"field_cg_background_color":"white_bg","field_cg_gradient":false,"field_cg_wave":false,"field_overlap_top":null,"field_overlap_top_width":null,"field_cg_spacing_top":"medium","field_cg_spacing_bottom":"large","field_component_width":null,"field_id_attribute":"case-study-client-testimonial","field_breadcrumb_links":[],"field_breadcrumb_links_position":null,"relationships":{"field_cg_components":[{"__typename":"paragraph__decoupled_quote_card_no_headshot","id":"574d5582-9af4-5d32-ab85-770c4e2e748d","field_dlp_qci_attribution_logo":null,"field_dlp_qci_attribution_name":"A-EYE Power User","field_dlp_qci_attribution_title":"Large Research and Technology Company","field_dlp_qci_copy_plain":"I like how the bot corrects grammar and all the little things that we don't have the time to check and fix.","field_photo":null,"field_dqcnh_link":null,"relationships":{"field_photo":null,"field_dlp_qci_attribution_logo":null}}]}},{"__typename":"paragraph__featured_insights","id":"afbeec53-860d-5374-9c9d-dc4ee6ea57f2","field_cg_background_color":"midnight_gradient","field_cg_spacing_bottom":null,"field_cg_spacing_top":null,"field_header":"Case Studies","field_insights_type":"case-study","field_max_posts":3,"field_subtitle":{"processed":"Related Client Work"},"field_cta":null,"relationships":{"field_insights_term":{"drupal_internal__tid":2406,"name":"Media, Entertainment, & Technology"},"field_industry":null,"field_featured_insight":[]}}],"field_logo_full_color":null,"field_logo_monochrome":null,"field_pdf":{"relationships":{"field_media_file":{"uri":{"url":"/sites/default/files/2026-08/casestudy_scaled_quality_control_with_agentic_ai_v1.pdf"}}}},"field_industry":[{"name":"Media, Entertainment, & Technology","relationships":{"page":{"path":{"alias":"/industries/media-entertainment-tech"}}}}],"field_partners":[{"name":"Anthropic","relationships":{"page":{"__typename":"node__partner","path":{"alias":"/partners/anthropic"}}}},{"name":"Microsoft","relationships":{"page":{"__typename":"node__partner","path":{"alias":"/partners/microsoft"}}}}],"field_services":[{"name":"Artificial Intelligence & Machine Learning","relationships":{"page":{"path":{"alias":"/artificial-intelligence-service-detail"}}}},{"name":"Generative AI","relationships":{"page":null}},{"name":"Business Process Automation & Transformation","relationships":{"page":{"path":{"alias":"/enterprise-digital-transformation-service-detail"}}}},{"name":"Quality Assurance Testing & Automation","relationships":{"page":{"path":{"alias":"/digital-product-platform-engineering-service-detail"}}}},{"name":"Application Modernization","relationships":{"page":{"path":{"alias":"/digital-product-platform-engineering-service-detail"}}}},{"name":"Technology Strategy & Advisory","relationships":{"page":{"path":{"alias":"/enterprise-digital-transformation-service-detail"}}}}]}}},"pageContext":{"nid":97916,"title":"Large Research and Technology Company","metatag":[{"attributes":{"name":"title","content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | Bounteous","rel":null,"href":null,"property":null}},{"attributes":{"name":"description","content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":null}},{"attributes":{"name":null,"content":null,"rel":"canonical","href":"https://www.bounteous.com/case-studies/large-research-and-technology-company","property":null}},{"attributes":{"name":null,"content":null,"rel":"image_src","href":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","property":null}},{"attributes":{"name":null,"content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | Bounteous","rel":null,"href":null,"property":"og:title"}},{"attributes":{"name":null,"content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":"og:description"}},{"attributes":{"name":null,"content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":"og:image"}},{"attributes":{"name":null,"content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":"og:image:url"}},{"attributes":{"name":"twitter:card","content":"summary_large_image","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:description","content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:site","content":"@bounteous","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:title","content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | [ Bounteoussite:name]","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:image","content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":null}}],"node":{"drupal_internal__nid":97916,"template":"ClientWork","title":"Large Research and Technology Company","path":{"alias":"/case-studies/large-research-and-technology-company/"},"metatag":[{"attributes":{"name":"title","content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | Bounteous","rel":null,"href":null,"property":null}},{"attributes":{"name":"description","content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":null}},{"attributes":{"name":null,"content":null,"rel":"canonical","href":"https://www.bounteous.com/case-studies/large-research-and-technology-company","property":null}},{"attributes":{"name":null,"content":null,"rel":"image_src","href":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","property":null}},{"attributes":{"name":null,"content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | Bounteous","rel":null,"href":null,"property":"og:title"}},{"attributes":{"name":null,"content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":"og:description"}},{"attributes":{"name":null,"content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":"og:image"}},{"attributes":{"name":null,"content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":"og:image:url"}},{"attributes":{"name":"twitter:card","content":"summary_large_image","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:description","content":"Learn how Bounteous helped a large research and technology company automate analytics report quality control with agentic AI, improving accuracy, scalability, and analyst productivity.","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:site","content":"@bounteous","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:title","content":"Bounteous Builds an Agentic Quality Control Product to Scale Reporting | [ Bounteoussite:name]","rel":null,"href":null,"property":null}},{"attributes":{"name":"twitter:image","content":"https://www.bounteous.com/sites/default/files/2026-08/scaled_quality_control_with_agentic_ai_seometadata.jpg","rel":null,"href":null,"property":null}}]},"baseUrl":"https://www.bounteous.com"}},"staticQueryHashes":["1976819378","2125679389","2624023844","2928555954","860662228","972400240"]}