Sprint.Sentients.Tech
- AI-Based Design Sprint Automation Platform
A new paradigm where AI accelerates design thinking and humans focus on creative decision-making
Vision and Goals of Sprint.sentients.tech
Vision
"AI accelerates design thinking, and humans focus on creative decision-making."
Sprint.sentients.tech replaces repetitive and time-consuming tasks with AI, allowing users to focus their energy on their inherent creativity and strategic thinking. Through the collaboration of AI and humans, we provide more innovative and efficient processes, enabling anyone to easily produce outstanding results.
Our vision is to realize the democratization and innovation of design through AI technology, and to dramatically increase the speed of problem-solving and expand the opportunities for value creation in various industries.
SMART Goals
- Develop an MVP by Q4 2025 and launch a full commercial service by Q2 2026
- End-to-end (E2E) automate the 5 stages of the design sprint (understand, define, ideate, prototype, test) based on AI
- Reduce the duration of the sprint by half (50%) to enable faster decision-making and value validation
- Maintain a Net Promoter Score (NPS) of 60 or higher to actively manage customer satisfaction
- Increase the precision of AI-based vector search to over 85% to support faster and more accurate information exploration and idea generation
These goals are established based on specific and measurable indicators, guiding Sprint.sentients.tech to become a differentiated design sprint automation platform in the market.
Key Pain Points of Customers
Time and Cost Burden
The traditional design sprint requires 4-7 experts in UX, development, and business to invest at least 5 days (one day per stage), and the costs for workshops and prototype creation are significant. Additional costs are incurred for in-person workshops, coordinating schedules of diverse participants, and hiring external facilitators, which is a serious burden for small/start-up companies. The cost risk becomes even greater when conducting repeated experiments or validating multiple ideas.
Failure Risk
Lack of rapid feedback loops means that if the initial hypothesis is wrong, the loss of manpower and budget can be significant. The additional time and cost required for root cause analysis and alternative design cannot be ignored, and a single failure can easily deplete the team's motivation and resources. The inability to respond quickly to market changes is also a critical issue.
Constraints on Creative Thinking
Due to team composition, expertise, and biases, the range of ideas is limited, and innovation is reduced. Discussions can be skewed by the opinions of certain experts or leaders, and it is difficult to involve diverse talents, which undermines creativity and diversity. The low probability of generating truly "innovative" ideas is a barrier to organizational growth.
Sprint.sentients.tech is designed to overcome the above problems. A founder or planner can automate the End-to-End sprint in 10-20 minutes, allowing them to repeat experiments without the burden of time, cost, or failure. The AI-based guidance expands the scope of creative thinking, and the use of diverse personas and domain knowledge in real-time enables the rapid generation of innovative ideas. This leads to faster market validation, increased resource efficiency, and accelerated innovation.
Core Service Values
Acceleration
With AI-powered automated workflows, we can reduce the time required for design sprints by more than half compared to traditional methods. Through rapid prototyping and immediate AI feedback, we can dramatically increase the speed of product-market fit validation, enabling you to respond more agilely to market changes.
Consistency
By combining standardized processes and quantitative AI evaluation, we ensure consistent quality results across every sprint. By minimizing trial-and-error and increasing decision-making confidence, anyone can replicate professional-level outcomes.
Knowledge Reuse
Leveraging ontology-based data structures, we effectively accumulate and connect insights and deliverables from previous projects. This reduces repetitive work burdens and allows you to quickly reuse past ideas and best practices.
Security
With locally deployed LLMs, data encryption, and access control features, we safely protect your company's confidential design assets and sensitive information. You can innovate with peace of mind, without the risk of external leaks.
Key Target Customers
Strategies to Maximize AI Creativity
AI Inference × Human Intuition Synergy
Step-by-step AI agents generate a wide range of ideas, while people are responsible for selection, refinement, and decision-making to ensure both quality and speed.
GenAI Co-Creation Capabilities
Users can immediately edit and brand the novel patterns and concepts proposed by large-scale generative AI, allowing them to establish completely new planning directions.
Collaboration-Centric Multi-Agent
By sharing and verifying the output of each Agent through an ontology, we maintain logical consistency and a balance of creativity.
Creativity KPIs
Dashboarding the metrics of idea diversity, novelty, and feasibility allows us to quantitatively evaluate sprint performance.
Product Definition and Value Proposition
End-to-End Automated Sprint
The AI Agent automates the entire 5-step process from idea generation to prototyping, user feedback collection, and iterative improvement. At each stage, Human-in-the-loop feedback is incorporated to ensure both quality and practical relevance.
Context Understanding Based on Knowledge Graph
Various types of uploaded data, such as documents, images, and reference materials, are automatically converted into an ontology and vector DB structure, allowing the AI to deeply understand the context and significantly improve the accuracy of idea generation and decision-making.
Local LLM
Through the dedicated AI model built within the platform, sensitive design data of the project and the company is prevented from being leaked outside, providing both security and customization flexibility.
Sprint.sentients.tech supports a wide range of organizations, from innovative startups to UX agencies with multiple clients and fast execution requirements, enterprise DX departments with complex processes and security issues, and even public service design teams. At each stage, AI and users collaborate, balancing automation and creativity, as well as data security, to provide a new design sprint experience. The platform is designed not only to save time and cost, but also to enable anyone to efficiently derive high-quality design results.
Key Features and Stepwise AI Agent
Understand
AI performs NLP analysis on the topic to automatically extract key keywords and semantic networks. It uses the RAG (Retrieval-Augmented Generation) technique to find related research papers, market reports, patent documents, and other external knowledge sources to complement the context. Users can freely input to describe the main issues, or provide various files such as strategic materials and datasets as background information. This stage determines the overall direction and accuracy of the project.
Define
Based on the uploaded information and analysis results, AI generates a clear problem statement, key personas, and HMW (How Might We) phrases. Users can directly review the automatically generated outputs and quickly approve, supplement, or modify them as needed. In this process, human intuition and domain knowledge are combined with AI's logical output, resulting in a definition that can be practically applied in the field.
Ideate
AI uses brainstorming frameworks (SCAMPER, 6 Hats, etc.) to propose fresh ideas from various angles. The generated ideas are clustered based on similarity and provided as a visual map, and users can evaluate, rate, and select meaningful candidates. Through an iterative exploration process, the breadth of ideas can be expanded, and new insights can be actively derived.
Prototype
AI integrates with the Figma API to automatically design lo-fi wireframes and key UI elements based on the ideas. Users can directly apply design touch-ups and additional modifications to the generated prototypes, optimizing for brand characteristics and usability. Rapid visualization and iterative testing enable free experimental attempts.
Test
AI automatically constructs user test scenarios and analyzes survey results and behavior logs in real-time to derive objective insights. Users can interpret the results and select final improvements to be reflected in the actual service development. Repeated testing ensures product quality and usability.
Differentiating Elements
Ontology-RAG Hybrid Search
Through a hybrid search engine that combines semantic relationships and vector similarity, we analyze not only exact keyword matches but also contextual meaning and relevance. This allows us to precisely and comprehensively provide users with the knowledge relevant to their questions or tasks. For example, we can effectively uncover hidden insights related to design trends, practical cases, and user requirements, thereby enhancing the quality of practical innovation and decision-making.
Multi-agent System
Specialized agents are deployed for each design sprint stage, and a Meta Coordinator orchestrates the overall workflow, ensuring a seamless transition between stages. This structure focuses on the expertise and roles of each agent while maintaining consistency throughout the process. In actual projects, it enables the efficient parallel processing of complex tasks such as planning, research, and ideation, providing a collaborative experience with experts.
Prompt & Result Version Management
All prompts and output results within the platform are systematically version-managed, allowing for reproducibility at any time under the same conditions. This enables users to easily track and compare previous work results, as well as gain a comprehensive understanding of the improvement process for workflows and outputs. It enhances efficiency in repetitive experimentation and testing, and supports data-driven decision-making for quality improvement.
Domain Expert Persona AI Expansion
Persona Agent Factory
Automatically generates domain ontologies and specialized AI Agents using topic keywords and reference data, accurately reflecting the needs and characteristics of each domain.
Rapidly designs persona Agents tailored to various industries, and integrates external data and internal knowledge bases to implement more meaningful AI. For example, in the medical field, an Agent specialized in clinical terminology and processes can be created, and in the manufacturing industry, an Agent optimized for equipment management and quality standards can be generated.
Vertical Sprint Coach
Automatically provides AI coaches to support service/product planning specialized for domains such as logistics, healthcare, finance, and manufacturing.
By reflecting industry-specific best practices, regulatory compliance, and the latest trends in real-time, the coaches help users derive more in-depth strategies and implementation plans. Domain-specific expert coaches collaborate and coordinate throughout the sprint process.
Cross-Domain Innovation Workshop
Provides a workshop environment to intensively strengthen the ability to solve complex and multifaceted problems by combining multiple Persona Agents.
By maximizing synergies between heterogeneous domains, such as integrating insights from healthcare and IT, or finance and logistics, innovative ideas can be generated. Through collaboration and discussion among diverse persona Agents, solutions that go beyond traditional limitations can be developed.
Basic System Functions
User Input & Document Upload
Enter natural language problem/task keywords and upload PDF, DOCX, URL references
Vector DB & Ontology Utilization
Vectorize and knowledge graph the uploaded materials for semantic-based search
Web Search Reference & Human-in-the-loop
Real-time Web Search Agent collects latest cases, papers, trends and provides decision gates
End-to-End Full Automation & Dashboard
Fully/semi-automate 5 steps, visualize progress, outputs, KPIs in real-time
Report Generation & LLM Selection
Export results in PDF, Markdown, TTL formats, choose plug-in LLM engines like Gemma, Llama 3, GPT-Neo
Technical Architecture
LLM Layer
The language model layer is based on Gemma 4B/12B (quantized) + LoRA fine-tuning, which efficiently supports a variety of natural language processing tasks.
Through targeted LoRA fine-tuning for specific topics, it is possible to improve the quality of service-tailored responses and generation, as well as quickly incorporate user feedback.
The plug-in structure allows for flexible replacement and expansion with other LLMs such as Llama 3 and GPT-Neo.
Vector DB
Using Chroma and FAISS indexes, documents and various data sources are embedded as vectors and stored.
It supports semantic-based search (Query-to-Vector) and real-time similarity search of large-scale triples and documents, maximizing response speed and search accuracy.
When scaling the system, it is easy to integrate with external databases such as ElasticSearch.
Ontology
The ontology defined in DesignSprint.ttl (e.g., classes such as Persona, Idea, Artifact) supports knowledge structuring and hierarchization.
Through the design of domain-specific ontologies, information linkage and semantic reasoning between AI agents are facilitated.
Based on RDF and SPARQL standards, it is compatible with external knowledge bases and APIs.
DevOps
A container orchestration based on Docker Compose and a CI/CD automation pipeline built with GitHub Actions are implemented.
Integrated monitoring of service status, errors, and resource usage is possible through the Grafana dashboard.
The infrastructure provides operational reliability through features like rapid deployment, scalability, and fault tolerance.
Security Architecture
Sprint.sentients.tech implements a robust security architecture to protect sensitive design assets of enterprises. Crucially, all AI inference processing is executed on a local LLM server within a secure environment, thoroughly ensuring data privacy without external exposure. During API communication, a zero-trust access control and authentication system is adopted, firmly maintaining the system boundary against internal and external threats. Documents and sprint deliverables stored within the service are encrypted at-rest to minimize the risk of data loss and unauthorized access. All prompt inputs and output results are thoroughly logged, but personal information is automatically masked to prevent the disclosure of user identity and sensitive data. Additionally, access to critical audit logs and system information is controlled through administrator privileges, enabling compliance with various security regulations (e.g., GDPR, ISMS).
Through this multi-layered security strategy, Sprint.sentients.tech provides the highest level of protection to ensure that creative assets such as design and planning can be safely utilized in an AI-powered environment. By maintaining flexibility and incorporating the latest security trends, the platform takes responsibility for the reliability and compliance of enterprise environments.
Data and Ontology Design
Development Roadmap
1
Q2-25: Phase 0 PoC
Understand/Define automation, NLP pipeline, Ontology draft
2
Q3-25: MVP α
Ideation·Prototype integration, Idea generator, Figma API integration
3
Q4-25: MVP β
End-to-End sprint + user testing, integrated Demo, feedback report
4
Q1-26: Pilot
Apply to 3 design sites, SLA monitoring, Pilot results·performance dashboard
5
Q2-26: GA 1.0
SaaS launch & On-prem package, Version 1.0 release, sales materials
6
H2-26: V2 Multimodal
Image·video proto generation, Gen-AI module, SDXL templates
Business Model
As additional revenue streams, we offer consulting services such as design sprint coaching and custom ontology building.
Team Composition
Product Manager (1)
As the product owner with expertise in Agile, UX, and AI PM, this person will define the overall development direction and priorities. They will collect and analyze customer requirements, define the core value and MVP, and lead cross-functional collaboration to ensure the project is successfully completed on time. Responsibilities include communication with stakeholders, strategic planning, schedule management, and quality assurance, with a continuous focus on product competitiveness and market fit.
AI/ML Engineers (2)
They will leverage LLM, LangChain, and RAG technologies to build the core AI engine and search system. Drawing on their broad AI/ML skills in natural language processing, prompt engineering, large-scale data preprocessing, and pipeline automation, they will deliver a differentiated user experience. They will also lead the exploration and adoption of the latest open-source models and frameworks, establish evaluation and testing criteria, and be responsible for continuous service performance improvement.
Ontology Architect (1)
Using RDF/OWL and Neo4j technologies, this person will structure the domain knowledge from the design sprint. They will integrate various data sources to design a semantic network based on ontology, support the AI agent's contextual understanding through knowledge graph modeling and query optimization. They will also take a leading role in ongoing domain expansion, quality improvement, documentation, and standardization.
Front-end Developer
They will design and implement intuitive UI/UX using front-end frameworks such as Streamlit, React, and Figma API. Key responsibilities include applying the latest trends, automating the design-development workflow, and implementing responsive design.
UX Researcher
Leveraging their HCI (human-computer interaction) expertise and user testing experience, they will analyze customer needs and behaviors, design and collect product feedback, and propose improvement directions. They will drive user-centric design through persona research, user journey mapping, and usability evaluation.
DevOps / Security Engineer
They will be responsible for the reliability and security of the service, including cloud infrastructure setup with MLOps and Kubernetes. They will optimize the overall operating environment through CI/CD pipeline design, automated deployment, monitoring, threat analysis, and security policy development.
The experts will collaborate to maximize the competitiveness and market fit of the AI-powered design sprint automation platform. Based on effective communication and a culture of continuous capability building, they will proactively respond to rapid changes and deliver innovative results.
Market Analysis & Competition
$7B
Global UX Tools & Services TAM
Global market size for UX design tools and services
50%
Sprint Time Reduction
Improved process efficiency compared to competitors
100%
End-to-End Automation
Competitors only support partial steps
Key competitors include Notion AI, Uizard, and Design.ai, but they are limited to supporting only some steps of the design sprint. Sprint.sentients.tech has the strengths of End-to-End automation and on-device security, and benefits from the growing enterprise DX demand and the growth of synthetic AI. However, the initial learning data limitation and the dominance of Big-Tech's multimodal AI could pose threats.
KPI & Analytics Dashboard
Sprint Completion Rate
Measures the percentage of sprints that are successfully completed out of the total sprints started. The completion rate is a key indicator that provides an intuitive understanding of the overall project health, as it continuously tracks various aspects such as target achievement, team collaboration efficiency, and execution capability. Maintaining a consistently high completion rate can lead to the establishment of a systematic process and the strengthening of team capabilities.
Avg Cycle Time
Tracks the average time taken from the start to the completion of a sprint. This metric represents the speed and efficiency of the entire process, and is crucial for identifying bottlenecks or recurring delay factors. As the Cycle Time is reduced, faster feedback and quicker decision-making become possible, allowing the team to respond more agilely to changes in customer needs.
Idea Diversity Index
Quantifies the diversity and innovativeness of the generated ideas. The more diverse the ideas from various sources and perspectives, the higher the likelihood of developing innovative solutions, and the team's creative thinking capabilities can be objectively assessed. For example, if only a single type of idea is repeatedly generated, it may signal the need to improve brainstorming techniques or tools.
Search Accuracy
Evaluates the relevance of document search results in the RAG system. Higher accuracy enables users to quickly find the desired information, contributing to improved work efficiency. Since this metric directly impacts the reliability of situation analysis, knowledge expansion, and decision-making, its management is crucial for the overall quality of the service.
User NPS
Manages the Net Promoter Score, which measures the user's willingness to recommend the service. NPS reflects the overall user satisfaction and the likelihood of repeat usage and word-of-mouth marketing. If the score decreases, customer feedback can be collected and analyzed to quickly set the direction for service improvements.
Annual Budget Plan
The total annual budget is $654,000, with personnel costs accounting for approximately 83% of the overall budget. Infrastructure costs are allocated for GPU servers and cloud resources, while software and API costs are used for necessary licenses and service subscriptions. Marketing and seminar expenses are budgeted to improve product awareness and user education.
Risk & Mitigation Strategies
LLM Bias
Risk: Low Idea Quality
AI-based generative models may produce repetitive or biased ideas due to limitations in their training data. If the results are skewed towards certain patterns, it may be difficult to provide novel insights.
Mitigation Strategy: Ensemble model, Human QA
Combine multiple LLM engines to ensure diversity in the output, and always include a human quality assurance (QA) step in the final result. Continuously monitor and address bias issues by incorporating expert feedback and user evaluations.
Data Leakage
Risk: Breach of Contract
If sensitive project information or user data is leaked externally, it can lead to legal issues and a significant loss of trust, which is particularly critical for enterprise and public sector customers where data security is a key requirement.
Mitigation Strategy: On-premises, Audit log
Provide an on-premises environment to prevent data from being leaked outside, and implement comprehensive audit logging to track all access and processing activities. Conduct regular security audits and comply with the customer's security guidelines.
Excessive Automation
Risk: Reduced Creativity
If AI automation functions excessively replace human creative involvement and collaboration, the outputs may become standardized and lack innovation.
Mitigation Strategy: Apply Human-gate steps
Introduce user participation and verification procedures at key stages of the sprint, limiting the scope of automation and actively incorporating human critical thinking and creativity. Ensure that the decision-making process for key ideas and final reviews includes a mandatory Human-gate.
Expansion and Roadmap V2
V2-Multimodal
Automatic generation of image and video prototypes (integration with multimodal Gen AI such as SDXL Turbo)
V2-Voice
Real-time voice interview summarization (Whisper-style STT + Summarization Agent)
3
3
V2-Cross-Domain
Knowledge sharing with other Sentients domain Agents (graph-level integration with domains like logistics, healthcare)
V2-Expert Squad
Multi-AI Agent virtual experts (input topic → automatic assembly and discussion of Persona Agents from different domains)
V2-Voting & Super-Vote
Collective opinion gathering + final decision (Agents propose options, users can rank them through voting, and override with 'Super Vote')
🗳️ Voting Flow: 1) Each Persona Agent proposes ideas and priorities → 2) Meta-Coordinator aggregates token-weighted votes → 3) Top options are presented to the user → 4) User can approve, modify, or reject with a 'Super Vote'.
Glossary
Design Sprint
A 5-step intensive workshop process (Understand → Define → Ideate → Prototype → Test) developed by Google Ventures (GV). Diverse experts collaborate for 4-5 days to quickly validate the direction of a product or service.
HMW (How Might We)
A question framing that rephrases a problem as "How might we...?" to elicit creative solutions.
RAG (Retrieval-Augmented Generation)
An AI generation technique that combines external document retrieval results with language model inputs to improve factuality and accuracy.
LoRA (Low-Rank Adaptation)
A fine-tuning method that only learns low-rank matrices to adapt large language models (LLMs) in a lightweight and cost-effective way.
LLM (Large Language Model)
Massive language models (e.g., Gemma, Llama 3) with billions to trillions of parameters that perform natural language understanding and generation.
Ontology
A knowledge model that structures the concepts, attributes, and relationships of a specific domain using description logic, enabling AI to perform semantic reasoning.
Persona Agent
A virtual AI agent with a specific role, expertise, and personality that provides tailored responses and feedback to individual domains or user needs.
Proto
A prototype that visualizes or simply implements the idea of a product or service before actual implementation. It is used to quickly validate user reactions.
STT (Speech-to-Text)
AI speech recognition technology that converts audio signals to text data in real-time or asynchronously.
Ensemble Model
A technique that combines multiple AI/machine learning models to improve reliability and performance over a single model, such as through voting, weighted sum, or boosting methods.
Audit Log
A log that records all actions within a system, such as data access and modifications, which is essential for security, traceability, and compliance management.
Prompt Engineering
The strategic design and adjustment of prompts (input sentences) to elicit the best responses from AI language models.
Meta-Coordinator
A higher-level agent or function that designs and orchestrates the collaboration between multiple AI agents, aggregates the results, and mediates the final decision-making.