End-to-end human trajectories capturing real computer workflows for agent training
The client was developing computer-use agents that interact with digital environments such as browsers and desktop applications. To improve these systems, they needed human-generated workflow data that showed how real users complete tasks step by step in a structured, agent-like manner. The required output included both screen recordings and detailed interaction logs, making the project more than simple screen capture or task execution.
The client was developing computer-use agents that interact with digital environments such as browsers and desktop applications. To improve these systems, they needed human-generated workflow data that showed how real users complete tasks step by step in a structured, agent-like manner. The required output included both screen recordings and detailed interaction logs, making the project more than simple screen capture or task execution.
A pharmaceutical distributor serving around 3,000 B2B customers — including pharmacies, clinics, and hospitals — was facing rising pressure on its sales and customer service teams due to a high daily volume of incoming orders, stock checks, product substitution requests, pricing questions, and delivery follow-ups. The company wanted to deploy a smart chatbot capable of handling routine B2B interactions such as automated order intake, inventory queries, product substitution when items were out of stock, and recommendation of high-stock products that the business wanted to prioritize. Because the pharmaceutical domain carries high operational and compliance risk, the chatbot also needed a strong human-in-the-loop layer and precise domain annotation.
A pharmaceutical distributor serving around 3,000 B2B customers — including pharmacies, clinics, and hospitals — was facing rising pressure on its sales and customer service teams due to a high daily volume of incoming orders, stock checks, product substitution requests, pricing questions, and delivery follow-ups. The company wanted to deploy a smart chatbot capable of handling routine B2B interactions such as automated order intake, inventory queries, product substitution when items were out of stock, and recommendation of high-stock products that the business wanted to prioritize. Because the pharmaceutical domain carries high operational and compliance risk, the chatbot also needed a strong human-in-the-loop layer and precise domain annotation.
A large real estate conglomerate with more than 5,000 employees across 15+ subsidiaries — spanning residential development, commercial leasing, building management, brokerage, and construction — was struggling with a persistent operational issue: employees were spending too much time searching for internal policies, HR procedures, compliance guidelines, and operational SOPs scattered across intranet portals, shared drives, archived emails, and printed handbooks. HR and Administration were receiving an estimated 500+ repetitive questions per day on topics such as leave policy, expense reimbursement, onboarding, commission structures, and subsidiary-specific rules. The group wanted to launch an internal AI chatbot, accessible through the intranet and internal messaging platforms, that could answer employee questions instantly and accurately while reflecting the right policy version based on role, entity, and context.
A large real estate conglomerate with more than 5,000 employees across 15+ subsidiaries — spanning residential development, commercial leasing, building management, brokerage, and construction — was struggling with a persistent operational issue: employees were spending too much time searching for internal policies, HR procedures, compliance guidelines, and operational SOPs scattered across intranet portals, shared drives, archived emails, and printed handbooks. HR and Administration were receiving an estimated 500+ repetitive questions per day on topics such as leave policy, expense reimbursement, onboarding, commission structures, and subsidiary-specific rules. The group wanted to launch an internal AI chatbot, accessible through the intranet and internal messaging platforms, that could answer employee questions instantly and accurately while reflecting the right policy version based on role, entity, and context.
The client was building image generation and editing workflows where AI-generated outputs often came close to the intended result but still missed key prompt details or visual consistency requirements. To make these outputs usable at production level, they needed a human refinement layer performed by skilled Photoshop operators who could edit images to better match both the prompt and the original source image.
The client was building image generation and editing workflows where AI-generated outputs often came close to the intended result but still missed key prompt details or visual consistency requirements. To make these outputs usable at production level, they needed a human refinement layer performed by skilled Photoshop operators who could edit images to better match both the prompt and the original source image.
A pharmaceutical distributor serving around 3,000 B2B customers — including pharmacies, clinics, and hospitals — was facing rising pressure on its sales and customer service teams due to a high daily volume of incoming orders, stock checks, product substitution requests, pricing questions, and delivery follow-ups. The company wanted to deploy a smart chatbot capable of handling routine B2B interactions such as automated order intake, inventory queries, product substitution when items were out of stock, and recommendation of high-stock products that the business wanted to prioritize. Because the pharmaceutical domain carries high operational and compliance risk, the chatbot also needed a strong human-in-the-loop layer and precise domain annotation.
A pharmaceutical distributor serving around 3,000 B2B customers — including pharmacies, clinics, and hospitals — was facing rising pressure on its sales and customer service teams due to a high daily volume of incoming orders, stock checks, product substitution requests, pricing questions, and delivery follow-ups. The company wanted to deploy a smart chatbot capable of handling routine B2B interactions such as automated order intake, inventory queries, product substitution when items were out of stock, and recommendation of high-stock products that the business wanted to prioritize. Because the pharmaceutical domain carries high operational and compliance risk, the chatbot also needed a strong human-in-the-loop layer and precise domain annotation.
A large real estate conglomerate with more than 5,000 employees across 15+ subsidiaries — spanning residential development, commercial leasing, building management, brokerage, and construction — was struggling with a persistent operational issue: employees were spending too much time searching for internal policies, HR procedures, compliance guidelines, and operational SOPs scattered across intranet portals, shared drives, archived emails, and printed handbooks. HR and Administration were receiving an estimated 500+ repetitive questions per day on topics such as leave policy, expense reimbursement, onboarding, commission structures, and subsidiary-specific rules. The group wanted to launch an internal AI chatbot, accessible through the intranet and internal messaging platforms, that could answer employee questions instantly and accurately while reflecting the right policy version based on role, entity, and context.
A large real estate conglomerate with more than 5,000 employees across 15+ subsidiaries — spanning residential development, commercial leasing, building management, brokerage, and construction — was struggling with a persistent operational issue: employees were spending too much time searching for internal policies, HR procedures, compliance guidelines, and operational SOPs scattered across intranet portals, shared drives, archived emails, and printed handbooks. HR and Administration were receiving an estimated 500+ repetitive questions per day on topics such as leave policy, expense reimbursement, onboarding, commission structures, and subsidiary-specific rules. The group wanted to launch an internal AI chatbot, accessible through the intranet and internal messaging platforms, that could answer employee questions instantly and accurately while reflecting the right policy version based on role, entity, and context.
The client was building image generation and editing workflows where AI-generated outputs often came close to the intended result but still missed key prompt details or visual consistency requirements. To make these outputs usable at production level, they needed a human refinement layer performed by skilled Photoshop operators who could edit images to better match both the prompt and the original source image.
The client was building image generation and editing workflows where AI-generated outputs often came close to the intended result but still missed key prompt details or visual consistency requirements. To make these outputs usable at production level, they needed a human refinement layer performed by skilled Photoshop operators who could edit images to better match both the prompt and the original source image.
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