Process discovery that once took months,
delivered by AI in weeks.
Tsukumo Labs analyzes PC operation logs with AI.
Map real business workflows with minimal interview effort and a PoC-ready enterprise approach.
Initial consultation with a former McKinsey consultant
Structural advantages
Near-zero
interview workload
As fast as 2 weeks
initial improvement report
Less than half
typical consulting cost
Problem
Business transformation depends on
how accurately work is understood
Traditional interview-led process analysis has structural limits that become costly at enterprise scale.
Interviews cover only what people can recall
BPR and operational improvement projects often spend months on manual interviews. The result depends heavily on the interviewer, and perceived workflows can diverge from what actually happens day to day.
Tacit knowledge is difficult to surface manually
High performers often rely on habits and judgment they do not consciously articulate. Interviews alone rarely explain why performance varies or how the best practices can be scaled across the organization.
AI initiatives fail when the underlying work is not understood
Without reliable process data, teams struggle to decide where AI should be applied. PoCs can miss the mark when they are built on partial or outdated assumptions about frontline work.
Consulting deliverables should reflect operational reality
Large process-mapping engagements can still end with interview-based overview reports. For enterprise teams, the question is whether the analysis captures enough ground truth to justify the investment.
How it works
Employees work as usual.
AI reads the workflow.
No upfront process mapping or heavy interview scheduling required.
AI analyzes PC operation logs and turns everyday work into process intelligence.
Capture
A lightweight application is designed to collect PC operation logs in the background, so teams can continue working without interrupting day-to-day operations. Employee notification and consent workflows are planned as standard.
Analyze
AI analyzes operation logs to identify process flows, person-dependent work patterns, and bottlenecks with a level of resolution that interviews alone cannot provide.
Generate insights
The output is a business improvement report with a chat interface, designed to help prioritize opportunities using actual workflow data.
Features
Why PC operation logs x AI
We use technology to reach the depth and speed of process understanding
that interview-led analysis structurally struggles to provide.
Process visibility with minimal frontline burden
No interviews firstEmployees continue working as usual while AI analyzes operation logs to infer process flows and key work patterns. Interview scheduling, note-taking, and manual consolidation can be substantially reduced.
Higher-resolution workflow understanding
Data-drivenBecause the analysis starts from PC operation data, it can surface time allocation, task sequences, and tool-switching patterns that are hard to reconstruct from memory.
Security architecture for enterprise review
On-prem readyThe product is being designed for on-premise and closed-network environments, with planned automatic masking for personal and confidential information plus employee notification and consent flows.
Lower implementation cost than traditional consulting
ROI-orientedCompared with conventional BPR consulting, the intended PoC model is designed to reduce the cost of process discovery while delivering recommendations grounded in operational data.
Use Cases
Where this approach is designed to help
From BPR and operational improvement to knowledge transfer and AI readiness,
Tsukumo Labs is built for teams that need a clearer view of how work actually happens.
Build an as-is view of work for transformation programs
Give executives and transformation teams a data-backed view of current operations early in a project. Instead of relying only on interviews, AI helps structure operational reality into a usable process database.
Turn high-performer know-how into organizational knowledge
Preparation routines before sales meetings, fast quoting workflows, and coordination habits held by experienced staff can be analyzed as behavior patterns and converted into repeatable operating practices.
Collect the operational knowledge AI agents need
Enterprise AI agents need company-specific process knowledge to be useful. By analyzing PC operation logs, Tsukumo Labs helps convert workflows, decision criteria, and tacit know-how into data for future AI adoption.
Security & Privacy
Designed around security
and employee privacy
Because PC operation logs can be sensitive, security and privacy are treated as core product design requirements rather than afterthoughts. The items below reflect the security principles being built into the product.
Tsukumo Labs is intended to understand business processes, not to monitor individuals. Collected data is designed to be analyzed after aggregation and anonymization, and we do not provide externally identifiable personal activity data.
We are developing the product with APPI-aware design and operations in mind, and we can support enterprise security reviews during PoC discussions.
On-premise / closed-network deployment
Designed to support deployments that remain inside the customer network without sending data externally.
Automatic masking of sensitive information
Planned AI-assisted detection and removal of passwords, personal names, and confidential information before analysis.
Employee notification and consent flows
Planned workflows for purpose notification and opt-out support aligned with APPI-aware operations.
Domestic data storage
Designed around operation in Japanese data centers where cross-border data transfer is not required.
Access control and audit logs
Designed with role-based access control and audit trails for administrative and analysis actions.
Retention controls and deletion
Planned controls to automatically and irreversibly delete data after customer-defined retention periods.
About Us
Company Overview
| Company | Tsukumo Labs, Inc. |
| Founded | February 2026 |
| Address | Shibuya Dogenzaka Tokyu Building 2F-C 1-10-8 Dogenzaka, Shibuya-ku, Tokyo |
| Capital | JPY 2.5 million |
| Business | Generative AI product development Generative AI adoption support and consulting |
Sumiki Hori
CEO / Founder
After graduating from the University of Tokyo, Sumiki worked at McKinsey & Company on operational transformation and new business strategy across manufacturing and TMT clients in Japan, the US, and Europe, and was promoted to manager in under four years. He later contributed to core business growth at Timee around its IPO period before founding Tsukumo Labs with a focus on AI and Japan's productivity challenges.
FAQ
Frequently Asked Questions
How do you protect employee privacy?
Tsukumo Labs is designed for process-level analysis, not individual surveillance. Collected data is intended to be anonymized and aggregated before analysis. Employee notification and consent workflows are planned as part of standard deployment, with APPI-aware operation as a design premise.
How long does implementation take?
Timelines depend on the target environment and scope, but the PoC model is designed to deliver an initial report in as little as two weeks.
How is pricing handled?
We currently discuss PoC scope and pricing based on the size of the target organization and security requirements. Please contact us for a tailored discussion.
Where is data stored?
The product is being designed to support storage on servers in Japan. On-premise and closed-network deployments are also planned for customers that need to avoid cross-border data transfer.
Can Tsukumo Labs integrate with existing systems?
We plan to expand integrations with major SaaS products over time. Specific integration requirements can be discussed as part of a PoC.
How is this different from consulting?
Traditional BPR consulting often relies on interviews and can take months before a report is produced. Tsukumo Labs is designed to reduce the data collection and analysis burden by using AI to analyze PC operation logs. It does not replace consulting judgment; it strengthens the discovery and analysis phase with operational data.
Start by turning your work
into process data.
In an initial discussion, we will review where your workflows may have room for improvement
and whether a PoC is the right next step.
A former McKinsey consultant will help structure your operational questions.
Short inquiry form