{"id":10114,"date":"2026-07-10T19:36:55","date_gmt":"2026-07-10T19:36:55","guid":{"rendered":"https:\/\/mtpinternational.mx\/generative-ai-testing\/"},"modified":"2026-10-13T19:11:47","modified_gmt":"2026-10-13T19:11:47","slug":"generative-ai-testing","status":"publish","type":"page","link":"https:\/\/mtpinternational.mx\/en\/generative-ai-testing\/","title":{"rendered":"Generative AI Testing"},"content":{"rendered":"

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For CTOs, CDOs, and Product Directors<\/p>\n

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Generative AI Testing: Structured Validation for Reliable AI in Production<\/h1>\n

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We design and implement the validation framework for your Generative AI systems, from stack selection to continuous monitoring in production, with auditable traceability for compliance.<\/p>\n

[\/et_pb_text][\/et_pb_column_inner][\/et_pb_row_inner][et_pb_row_inner column_structure=”1_2,1_2″ use_custom_gutter=”on” gutter_width=”2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_button button_text=”Request AI Diagnostic” _builder_version=”4.27.7″ _module_preset=”default” custom_button=”on” button_text_size=”15px” button_text_color=”#FFFFFF” button_bg_color=”#F65F34″ button_border_width=”0px” button_border_radius=”100px” button_font=”Inter|700|||||||” background_layout=”dark” custom_padding=”15px|20px|15px|20px|true|true” locked=”off” global_colors_info=”{%22gcid-1229dcee-ea6a-4c3d-bd03-a8875c587516%22:%91%22button_text_color%22,%22button_text_color%22,%22button_text_color%22,%22button_text_color%22%93,%22gcid-primary-color%22:%91%22button_text_color%22,%22button_text_color%22%93}”][\/et_pb_button][\/et_pb_column_inner][et_pb_column_inner type=”1_2″ saved_specialty_column_type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_column_inner][\/et_pb_row_inner][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_image src=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/05\/magnific__photo-a-group-of-6-people-average-age-25-working-i__95867-scaled.png” title_text=”magnific__photo-a-group-of-6-people-average-age-25-working-i__95867″ align=”center” disabled_on=”on|off|off” admin_label=”Image” _builder_version=”4.27.6″ _module_preset=”default” custom_margin=”|-6vw|||false|false” custom_padding=”||||false|false” border_radii=”on|15px|15px|15px|15px” locked=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.8″ _module_preset=”_initial” text_font=”Inter|700|||||||” text_text_color=”#333333″ text_font_size=”36px” text_line_height=”1.1em” text_orientation=”center” custom_margin=”||0px||false|false” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Trusted by organizations that validate their Generative AI systems in production<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][dsm_image_carousel gallery_ids=”9763,9764,9765,9766,9767,9768,9769,10070,9771,9772,9773,9774,9775,9776,9777,9778,9779,9780,9781,9782,9783,9784,9785,9786,9787,9788,9789,9790,9791,9792,9793″ lazyload=”on” centered_slides=”on” slide_to_show=”6″ slide_to_scroll=”3″ speed=”1400″ autoplay_speed=”1110″ pause_on_hover=”on” space_between=”50″ arrows=”off” slide_to_show_tablet=”6″ slide_to_show_phone=”3″ slide_to_show_last_edited=”on|phone” _builder_version=”4.27.6″ _module_preset=”default” custom_padding=”30px||30px||true|false” locked=”off” global_colors_info=”{}”][\/dsm_image_carousel][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Features” _builder_version=”4.27.6″ _module_preset=”default” background_enable_color=”off” background_enable_image=”off” background_position=”bottom_right” locked=”off” collapsed=”on” global_colors_info=”{}”][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” _builder_version=”4.27.7″ _module_preset=”default” custom_css_main_element=”align-items:center;” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Inter|800||on|||||” text_text_color=”#F65F34″ header_2_text_align=”left” header_2_font_size=”31px” custom_margin=”||0px||false|false” global_colors_info=”{}”]<\/p>\n

Conventional QA is not designed for non-deterministic systems<\/p>\n

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Three Scenarios Where AI in Production Poses an Operational Risk<\/h2>\n

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In the organizations we advise, we observe three distinct patterns. Each has its own symptoms, but they share the same consequence: when AI is not validated with the correct rigor, the business absorbs the risk, not IT. <\/p>\n

[\/et_pb_text][et_pb_blurb title=”AI Without a Validation Framework” use_icon=”on” font_icon=”||fa||900″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” icon_placement=”left” image_icon_width=”30px” _builder_version=”4.27.7″ _module_preset=”_initial” header_font=”|600|||||||” header_text_color=”#0a0a0a” header_font_size=”16px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_font_size=”15px” body_letter_spacing=”0em” body_line_height=”1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_radii_image=”on|50px|50px|50px|50px” border_width_all_image=”2px” border_color_all_image=”#1E9BB0″ locked=”off” global_colors_info=”{}”]<\/p>\n

The team implements the model, performs basic functional tests, and releases it. Without defined acceptance criteria for hallucinations, bias, or semantic consistency, the system enters production with unmeasured and undocumented risk. <\/p>\n

[\/et_pb_blurb][et_pb_blurb title=”Conventional Testing Applied to AI” use_icon=”on” font_icon=”||divi||400″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” icon_placement=”left” image_icon_width=”30px” _builder_version=”4.27.6″ _module_preset=”_initial” header_font=”|600|||||||” header_text_color=”#0a0a0a” header_font_size=”16px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_font_size=”15px” body_letter_spacing=”0em” body_line_height=”1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_radii_image=”on|50px|50px|50px|50px” border_width_all_image=”2px” border_color_all_image=”#1E9BB0″ locked=”off” global_colors_info=”{}”]<\/p>\n

Traditional QA (string comparison, deterministic test cases) is applied to a system that, by definition, does not produce identical outputs. Tests pass, but hallucinations and systematic bias go undetected. The suite provides false confidence. <\/p>\n

[\/et_pb_blurb][et_pb_blurb title=”AI in Production Without Governance” use_icon=”on” font_icon=”||divi||400″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” icon_placement=”left” image_icon_width=”30px” _builder_version=”4.27.6″ _module_preset=”_initial” header_font=”|600|||||||” header_text_color=”#0a0a0a” header_font_size=”16px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_font_size=”15px” body_letter_spacing=”0em” body_line_height=”1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_radii_image=”on|50px|50px|50px|50px” border_width_all_image=”2px” border_color_all_image=”#1E9BB0″ locked=”off” global_colors_info=”{}”]<\/p>\n

Multiple models and versions in production without decision traceability, without drift monitoring, and without clear governance. When an incident occurs, it’s impossible to reconstruct what the system did and why, a serious problem for auditors and regulators. <\/p>\n

[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/07\/MTP_app_chatbot_2.webp” title_text=”MTP_app_chatbot_2″ align=”center” _builder_version=”4.27.7″ _module_preset=”default” module_alignment=”center” max_height=”650px” border_radii=”on|29px|29px|29px|29px” border_width_all=”8px” border_color_all=”#000000″ global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_line_height=”1.4em” text_orientation=”justified” global_colors_info=”{}”]<\/p>\n

<\/strong>Whatever your scenario, the root cause is the same: AI validation is not designed with the framework, acceptance criteria, and governance that non-deterministic systems require. That’s what we implement. <\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Call to Action” _builder_version=”4.27.6″ _module_preset=”default” background_image=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/05\/QA-Testing-MTP3-1.png” background_size=”custom” background_image_width=”25%” background_position=”bottom_left” locked=”off” collapsed=”on” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Inter|800||on|||||” text_text_color=”#F65F34″ text_letter_spacing=”6px” header_2_text_align=”left” header_2_font_size=”32px” text_orientation=”center” custom_margin=”||10px||false|false” locked=”off” global_colors_info=”{}” header_2_text_color__hover_enabled=”off|desktop”]<\/p>\n

The Real Cost of Operating AI Without Structured Validation<\/p>\n

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The Risk of Unvalidated AI Doesn’t Appear on the Roadmap, It Appears in the Business Headlines<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” _builder_version=”4.27.7″ _module_preset=”default” custom_css_main_element=”align-items:center” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#032442″ custom_padding=”35px|30px|35px|30px|true|true” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_font_size=”50px” text_line_height=”0.6em” global_colors_info=”{}”]<\/p>\n

+50%<\/strong><\/span><\/p>\n

Generative AI projects are abandoned in proof of concept without reaching production<\/span>
– Gartner, 2025<\/span><\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_font_size=”13px” text_line_height=”1.2em” background_color=”#FFFFFF” custom_padding=”5px||5px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}”]<\/p>\n\n\n\n
80%<\/span><\/strong><\/td>\nOf organizations with critical AI lag for QE,
<\/strong>the window for differentiation closes before 2029 (Gartner)<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_font_size=”13px” text_line_height=”1.2em” background_color=”#FFFFFF” custom_padding=”5px||5px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}”]<\/p>\n\n\n\n
66%<\/span><\/strong><\/td>\n\n

Of teams using AI in testing
achieve faster speed to market
<\/strong>the difference is in the framework, not the tool (Gartner)<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_font_size=”13px” text_line_height=”1.2em” background_color=”#FFFFFF” custom_padding=”5px||5px||true|false” border_radii=”on|12px|12px|12px|12px” global_colors_info=”{}”]<\/p>\n\n\n\n
53%<\/span><\/strong><\/td>\n\n

Savings in test case generation
<\/strong>with AI, but only when the system is correctly validated (Gartner, 2025)<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.19.2″ _module_preset=”default” custom_padding_tablet=”0vw||||false|false” custom_padding_phone=”0vw||||false|false” custom_padding_last_edited=”off|desktop” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.8″ _module_preset=”_initial” text_font=”Inter|700|||||||” text_text_color=”#333333″ text_font_size=”25px” text_line_height=”1.4em” hover_enabled=”0″ locked=”off” global_colors_info=”{}” text_orientation=”justified” sticky_enabled=”0″]<\/p>\n

<\/strong>Where Does the Real Cost Appear in Your Organization?<\/p>\n

[\/et_pb_text][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

Hallucinations in production<\/strong> that generate incorrect responses to customers, affect critical decisions, or create undocumented regulatory risk<\/p>\n

[\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

Regulatory non-compliance<\/strong> due to lack of auditable traceability for automated decisions before CNBV, DORA, or other regulatory frameworks<\/p>\n

[\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

Silent model degradation<\/strong> (model drift) without monitoring to detect when production behavior deviates from the validated baseline<\/p>\n

[\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

GenAI projects stuck in proof of concept<\/strong> because no clear criteria define when the system is ready for production<\/p>\n

[\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

Unanticipated production incidents<\/strong> with impact on reputation, operational continuity, or legal exposure due to undetected systematic bias<\/p>\n

[\/et_pb_blurb][et_pb_blurb use_icon=”on” font_icon=”||fa||900″ icon_color=”#F65F34″ icon_placement=”left” image_icon_width=”20px” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins||||||||” header_font_size=”18px” body_font=”Poppins||||||||” body_line_height=”1.2em” image_icon_custom_margin=”0px|0px|0px|0px|true|true” image_icon_custom_padding=”0px|0px|0px|0px|true|true” locked=”off” global_colors_info=”{}” text_orientation=”justified”]<\/p>\n

Loss of competitive advantage<\/strong> against organizations already operating validated AI in production while your team continues iterating in proof of concept<\/p>\n

[\/et_pb_blurb][et_pb_button button_text=”Request AI Diagnostic” _builder_version=”4.27.7″ _module_preset=”default” custom_button=”on” button_text_size=”15px” button_text_color=”#FFFFFF” button_bg_color=”#F65F34″ button_border_width=”0px” button_border_radius=”100px” button_font=”Inter|700|||||||” background_layout=”dark” custom_padding=”15px|20px|15px|20px|true|true” locked=”off” global_colors_info=”{%22gcid-1229dcee-ea6a-4c3d-bd03-a8875c587516%22:%91%22button_text_color%22,%22button_text_color%22,%22button_text_color%22,%22button_text_color%22%93,%22gcid-primary-color%22:%91%22button_text_color%22,%22button_text_color%22%93}”][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ use_custom_gutter=”on” gutter_width=”2″ padding_right_1=”0px” padding_right_2=”0px” padding_left_1=”0px” padding_left_2=”0px” padding_top_bottom_link_1=”false” padding_top_bottom_link_2=”false” padding_left_right_link_1=”false” padding_left_right_link_2=”false” admin_label=”Stats” _builder_version=”4.27.7″ _module_preset=”default” background_color=”rgba(16,18,70,0.6)” background_image=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/07\/MTP_tech_QE.png” background_blend=”multiply” custom_margin=”|0px|||false|false” custom_padding=”4vw|0px|4vw||true|false” collapsed=”on” global_colors_info=”{%22gcid-81a7943e-90c8-4540-8132-675997db2959%22:%91%22top_divider_color%22,%22top_divider_color%22%93}”][et_pb_row _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Inter|800||on|||||” text_text_color=”#F65F34″ text_letter_spacing=”6px” header_2_text_align=”left” header_2_font_size=”32px” text_orientation=”center” custom_margin=”||10px||false|false” locked=”off” global_colors_info=”{}”]<\/p>\n

MTP AI Testing Framework, MTP’s Proprietary Methodology<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”|700|||||||” text_text_color=”#FFFFFF” text_font_size=”36px” text_line_height=”1.1em” header_font_size=”31px” header_2_font=”Inter|700|||||||” header_2_text_align=”left” header_2_text_color=”#FFFFFF” header_2_font_size=”32px” text_orientation=”center” custom_margin=”||||false|false” global_colors_info=”{}”]<\/p>\n

We don’t evaluate AI with generic tools: we use a framework designed for non-deterministic systems<\/h2>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Poppins||||||||” text_text_color=”#FFFFFF” text_line_height=”1.4em” global_colors_info=”{}”]<\/p>\n

The MTP AI Testing Framework establishes acceptance criteria, quality metrics, and governance from the design phase, not as a post-development process.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ use_custom_gutter=”on” gutter_width=”2″ make_equal=”on” _builder_version=”4.27.7″ _module_preset=”default” custom_css_main_element=”align-items:center” locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.27.6″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” global_colors_info=”{}”][et_pb_image src=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/07\/MTP-QA-QE-Servicios-Mexico-31.jpg” title_text=”MTP-QA-QE-Servicios-Mexico-31″ align=”center” admin_label=”Image” _builder_version=”4.27.7″ _module_preset=”default” custom_margin=”|6vw|||false|false” custom_padding=”||||false|false” border_radii=”on|15px|15px|15px|15px” locked=”off” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.18.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_accordion open_toggle_text_color=”#FFFFFF” open_toggle_background_color=”rgba(255,255,255,0.19)” closed_toggle_text_color=”#FFFFFF” closed_toggle_background_color=”RGBA(255,255,255,0.19)” toggle_icon=”;||divi||400″ icon_color=”#f65f34″ use_icon_font_size=”on” icon_font_size=”20px” _builder_version=”4.27.8″ _module_preset=”default” toggle_text_color=”#FFFFFF” toggle_font=”|700|||||||” body_font=”Poppins||||||||” body_text_color=”#FFFFFF” hover_enabled=”0″ border_radii=”on|15px|15px|15px|15px” border_width_all=”0px” global_colors_info=”{}” text_orientation=”justified” sticky_enabled=”0″][et_pb_accordion_item title=”Framework for Non-Deterministic Systems” open=”on” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”]Acceptance criteria for hallucinations, semantic consistency, and bias. Validation does not rely on exact string comparison. It evaluates probabilistic behavior with metrics designed for LLMs and generative systems.[\/et_pb_accordion_item][et_pb_accordion_item title=”Auditable Traceability from Design” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}” open=”off”]Every system decision (input, context, model, version, output) recorded in a structured log. The auditor can reconstruct the reasoning. Compatible with CNBV, DORA, and AI explainability frameworks.[\/et_pb_accordion_item][et_pb_accordion_item title=”Governance in Multi-Model Environments” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}” open=”off”]<\/p>\n

Multiple LLMs, versions, and providers under a single validation framework. Standardization of quality gates across teams and vendors. Only built by those who have operated in top-tier regulated environments. <\/p>\n

[\/et_pb_accordion_item][et_pb_accordion_item title=”Validated Systems, Not Billed Hours” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}” open=”off”]We measure success in systems that reach production with known and controlled risk, not by the volume of test cases generated. For us, the outcome matters more than the time invested.[\/et_pb_accordion_item][\/et_pb_accordion][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.6″ _module_preset=”default” background_image=”https:\/\/mtpinternational.mx\/wp-content\/uploads\/2026\/05\/QA-Testing-MTP3-1.png” background_size=”custom” background_image_width=”25%” background_position=”bottom_left” locked=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” text_font=”Inter|800||on|||||” text_text_color=”#F65F34″ text_letter_spacing=”6px” header_2_text_align=”left” header_2_font_size=”32px” text_orientation=”center” custom_margin=”||10px||false|false” locked=”off” global_colors_info=”{}” header_2_text_color__hover_enabled=”off|desktop”]<\/p>\n

When AI is Validated with the Correct Framework<\/p>\n

[\/et_pb_text][et_pb_text _builder_version=”4.27.7″ _module_preset=”_initial” header_2_font=”Inter|700|||||||” header_2_text_align=”center” header_2_font_size=”35px” custom_margin=”||||false|false” custom_padding=”15px||10px||false|false” locked=”off” global_colors_info=”{}”]<\/p>\n

AI Testing Capabilities Recognized by Business and Auditors<\/h2>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ make_equal=”on” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”30px|20px|30px|20px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#E4EBEE” global_colors_info=”{}”][et_pb_blurb title=”Hallucination and Consistency Testing” use_icon=”on” font_icon=”||fa||900″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” image_icon_width=”30px” icon_alignment=”left” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins|600|||||||” header_text_color=”gcid-143aa564-2dbe-4a69-aba4-6587f516793c” header_font_size=”20px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_text_color=”gcid-heading-color” body_letter_spacing=”0em” body_line_height=”1.1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_width_all_image=”2px” border_color_all_image=”#FFFFFF” global_colors_info=”{%22gcid-heading-color%22:%91%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22%93,%22gcid-143aa564-2dbe-4a69-aba4-6587f516793c%22:%91%22header_text_color%22,%22header_text_color%22%93}”]<\/p>\n

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Factual consistency tests, comparison against ground truth, and semantic evaluation with LLM judges. Clear acceptance criteria for what level of hallucination is tolerable based on use case and regulatory risk. <\/p>\n<\/div>\n

[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”30px|20px|30px|20px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#E4EBEE” global_colors_info=”{}”][et_pb_blurb title=”Bias Detection and Adversarial Robustness” use_icon=”on” font_icon=”||fa||900″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” image_icon_width=”30px” icon_alignment=”left” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins|600|||||||” header_text_color=”gcid-143aa564-2dbe-4a69-aba4-6587f516793c” header_font_size=”20px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_text_color=”gcid-heading-color” body_letter_spacing=”0em” body_line_height=”1.1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_width_all_image=”2px” border_color_all_image=”#FFFFFF” locked=”off” global_colors_info=”{%22gcid-heading-color%22:%91%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22%93,%22gcid-143aa564-2dbe-4a69-aba4-6587f516793c%22:%91%22header_text_color%22,%22header_text_color%22%93}”]<\/p>\n

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Systematic evaluation of bias in decisions \u2014 demographic, contextual, temporal. Robustness tests against adversarial prompts and semantic variations that reveal hidden vulnerabilities before production. <\/p>\n<\/div>\n

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Immutable structured log of every system interaction and decision. Documentation that allows auditors to reconstruct system reasoning for regulators, claims, or regulatory incidents. <\/p>\n<\/div>\n

[\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ make_equal=”on” _builder_version=”4.27.7″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”30px|20px|30px|20px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#E4EBEE” global_colors_info=”{}”][et_pb_blurb title=”CI\/CD Integration for AI Pipelines” use_icon=”on” font_icon=”||fa||900″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” image_icon_width=”30px” icon_alignment=”left” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins|600|||||||” header_text_color=”gcid-143aa564-2dbe-4a69-aba4-6587f516793c” header_font_size=”20px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_text_color=”gcid-heading-color” body_letter_spacing=”0em” body_line_height=”1.1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_width_all_image=”2px” border_color_all_image=”#FFFFFF” global_colors_info=”{%22gcid-heading-color%22:%91%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22%93,%22gcid-143aa564-2dbe-4a69-aba4-6587f516793c%22:%91%22header_text_color%22%93}”]<\/p>\n

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Continuous evaluation integrated into the development pipeline, not as a parallel process. Every model or prompt update passes through automated quality gates before reaching production. <\/p>\n<\/div>\n

[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”30px|20px|30px|20px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#E4EBEE” global_colors_info=”{}”][et_pb_blurb title=”Drift Monitoring in Production” use_icon=”on” font_icon=”||divi||400″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” image_icon_width=”30px” icon_alignment=”left” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins|600|||||||” header_text_color=”gcid-143aa564-2dbe-4a69-aba4-6587f516793c” header_font_size=”20px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_text_color=”gcid-heading-color” body_letter_spacing=”0em” body_line_height=”1.1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_width_all_image=”2px” border_color_all_image=”#FFFFFF” locked=”off” global_colors_info=”{%22gcid-heading-color%22:%91%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22%93,%22gcid-143aa564-2dbe-4a69-aba4-6587f516793c%22:%91%22header_text_color%22%93}”]<\/p>\n

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Early detection when model behavior in production deviates from the validated baseline. Alerts before drift causes incidents, not after the customer notices. <\/p>\n<\/div>\n

[\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.7″ _module_preset=”default” background_color=”#FFFFFF” custom_padding=”30px|20px|30px|20px|true|true” border_radii=”on|12px|12px|12px|12px” border_width_all=”1px” border_color_all=”#E4EBEE” global_colors_info=”{}”][et_pb_blurb title=”53% Acceleration in Test Case Generation” use_icon=”on” font_icon=”||fa||900″ icon_color=”#1E9BB0″ image_icon_background_color=”RGBA(255,255,255,0)” image_icon_width=”30px” icon_alignment=”left” _builder_version=”4.27.7″ _module_preset=”_initial” header_level=”h3″ header_font=”Poppins|600|||||||” header_text_color=”gcid-143aa564-2dbe-4a69-aba4-6587f516793c” header_font_size=”20px” header_letter_spacing=”0em” body_font=”Poppins||||||||” body_text_align=”justify” body_text_color=”gcid-heading-color” body_letter_spacing=”0em” body_line_height=”1.1em” body_ul_line_height=”1.2em” background_enable_color=”off” image_icon_custom_margin=”||||false|false” image_icon_custom_padding=”10px|10px|10px|10px|true|true” custom_margin=”||||false|false” custom_padding=”||||false|false” custom_padding_tablet=”30px|30px|30px|30px|true|true” custom_padding_phone=”20px|20px|20px|20px|true|true” custom_padding_last_edited=”on|phone” border_width_all_image=”2px” border_color_all_image=”#FFFFFF” locked=”off” global_colors_info=”{%22gcid-heading-color%22:%91%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22,%22body_text_color%22%93,%22gcid-143aa564-2dbe-4a69-aba4-6587f516793c%22:%91%22header_text_color%22%93}”]<\/p>\n

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Use of AI as a tool to generate test cases, under defined governance and acceptance criteria. Acceleration is materialized when the validation system is correctly implemented. <\/p>\n<\/div>\n

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GENERATIVE AI TESTING OPERATIONAL PROCESS<\/p>\n

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From Stuck Proof of Concept to Validated AI in Production, This is How We Operate<\/h2>\n

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Analysis of the current stack, highest-risk flows, available traceability, and validation process maturity level. A clear map of where you are and the cost of inaction. <\/p>\n

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Identification of highest-risk decisions \u2014 by financial, regulatory, or reputational impact. Prioritization of what to validate first based on real consequence, not volume of interactions. <\/p>\n

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Acceptance criteria, quality metrics, evaluation stack selection, and governance model \u2014 designed for the specific use case, not as a generic template.<\/p>\n

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Framework connected to the real pipeline \u2014 continuous evaluation integrated into the development process. Automated quality gates before each model deployment or prompt change. <\/p>\n

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Unified visibility of system behavior \u2014 metrics for hallucination, consistency, bias, and drift. Comparable and actionable information for management and technical teams. <\/p>\n

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Early alerts for drift, updating criteria with new model versions, expanding coverage to new higher-risk flows, without waiting for incidents.<\/p>\n

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Experience in Environments Where AI Cannot Fail<\/p>\n

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Two Contexts, Same Demand: AI That Business and Auditors Can Defend<\/h2>\n

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Financial Sector<\/p>\n

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