A qualitative research support service for B2B marketing, business development, and product teams that repeatedly interviews AI personas based on internal notes, meeting minutes, and existing interviews, and immediately shows the cost savings from the first project. Buyers are mainly small-scale, department-budget-led adopters, with the first payment typically being a 9,800 yen trial or a 19,800 yen/month lite plan. The reason to buy is to avoid the slow speed and high cost of external research and quickly create evidence-backed reports for managers and executives. The biggest current barrier is not ROI but trust and operational clarity; explanations of no training use, retention period, deletion proof, and access control are required before purchase.
Value Proposition (Problem / Solution)
Problem
Existing qualitative research is expensive, slow, and hard to repeat, so planning decisions tend to rely on experience, and there is not enough evidence to get approval from managers or executives. In addition, if the handling of retention terms and training use is unclear when feeding internal documents and project notes into AI, legal and IT approval can easily stall.
Solution
A SaaS service that ingests internal documents to create AI personas and allows 24/7 interviews in chat form. Answers include evidence tags and confidence scores, and after each session it automatically generates an approval report in PDF or PowerPoint. It also clearly states no training use, retention period, deletion process, audit logs, and domestic management in the admin screen and contract, supporting both frontline adoption and review approval.
Target Persona
A person at a B2B company with 50 to 1,000 employees who serves as a marketing lead, product manager, new business lead, or division head, and is responsible for early hypothesis validation and executive presentations. In particular, this is for those who want to run one or two or more validations per month but feel burdened by the cost and coordination required for external research firms.
Core Pain
Hypotheses are too low-resolution, and there is not enough evidence to explain them in meetings or approval requests. In addition, there is insufficient guidance on whether internal documents can be fed into AI, which makes adoption discussions stall easily.
Solution Mechanism
Structure personas from existing internal documents, and allow repeated validation of key issues without [redacted-address], participant recruitment, or waiting for outsourcing. Support accountability in meetings with evidence tags and confidence scores, and connect directly to decision-making materials through report output. For trust, make retention, deletion, no-training, permissions, and audit visibility clear, creating an adoption flow designed for legal and IT review.
Revenue Model
Model: 案件単位の小額開始＋月額固定の二段階
Price Range: Project trial: 9,800 yen per case, Light: 19,800 yen/month, Enterprise: quote required
The initial payer is usually the field proposer or the department budget, and there is strong demand to first confirm ROI for just one project. 9,800 yen is easy to approve as a pre-approval trial, and 19,800 yen per month fits the fixed-cost expectations of small teams handling one to two projects a month. Departments with stricter audit requirements or segregation of duties naturally require a quote because contract, audit, and dedicated tenant support are included.
Differentiation
Differentiation Points
External research firms are high quality but expensive and slow. General-purpose chat AI is cheap, but it is weak on evidence, reproducibility, team operations, and audit readiness. This proposal is positioned not as something in between, but as an operational foundation for running internal qualitative validation on a per-project basis.
Core Features
Persona generation engine
[redacted-address], structure up to 20 personas as JSON in the same workspace. Fix the validation targets for each project first, then stabilize the quality of subsequent interviews.
AI interview chat
A chat UI injected with persona JSON that lets you ask questions anytime, 24/7. Each answer includes evidence tags, and conversation history is kept per session.
Automated report generation
After the interview is complete, export the issue summary, insights, and recommended actions as PDF or PowerPoint so they can be shared directly with managers or executives.
Evidence-based scoring
Show a confidence score and source references for each answer, and warn on low-scoring answers. Make it clear what should be checked instead of trusting the generated results as-is.
Clear statement of no training use and storage controls
State in the admin screen and contract that there is no training use, specify retention period, deletion steps, and domestic data management, and keep a change history of settings.
Deletion request and audit trail export
Allow admins to submit deletion requests and download deletion evidence after completion. Store it in a format that can be used for audit submission.
Small-scale, per-project adoption plan
Offer a trial starting at 9,800 yen per project and a light plan at 19,800 yen per month, so users can verify savings with just one case first.
ROI cost comparison widget
Enter outsourced fees, meeting hours, and number of revisions to instantly show estimated savings when using this service.
Template-based onboarding
Even without materials, users can start from industry-specific templates and later replace them with their own documents.
Confidence scoring
A 0-100 score using logprobs and warning badges
4-step onboarding
Complete the value experience within the first session and present the ROI calculator
No training on user data and tenant isolation
Enterprise trust foundation with Zero Data Retention settings and RLS
Evidence tagging
Link each answer to the relevant section of uploaded data or an external URL
Conversation history persistence
Save chat history to PostgreSQL by session and make it available for later review
14-day free trial
Let users experience starter-tier features without a credit card
Onboarding wizard
Complete the four steps in the first session: upload data -> generate persona -> conduct interview -> export report
ROI cost comparison calculator
Add an input widget for "annual outsourcing cost vs. annual cost of this service" on the onboarding screen and visualize it instantly
Persona templates
A starter set with sample personas by industry (B2B SaaS, EC, new business, etc.) for instant hands-on use
Boss share link generation
Issue a read-only URL when exporting reports and connect it to a viral invite flow
Workspace sharing and permission management (3 levels: view, edit, admin)
Implement in Phase 2 with [redacted-url] + a PostgreSQL permissions table
Slack notification integration
Notify the team with the report link via Slack Webhook when an interview session is completed
Usage dashboard
Show real-time usage by plan, including session count, report exports, and persona count
API rate management
Control per-plan OpenAI API call limits with [redacted-url] middleware to prevent cost overruns
Customer success touchpoint
Automatically show in-app messages after 3 months of use to encourage upgrades to annual billing or enterprise
Always-on disclaimer display
Always display at the top of the chat UI: "This is an AI-generated simulated interview and not a substitute for actual consumer research."
Automatically add an AI-generated label to reports
Automatically add "This report includes AI-generated content" to PDF and PPT footers
Data non-training guarantee
Set the OpenAI Zero Data Retention API option and provide an enterprise DPA (Data Processing Agreement) template
Multi-tenant data isolation with PostgreSQL Row Level Security
Prevent data from all tenants from being mixed at the database layer
Scenario
Validation-driven UX designImprovements and Next Actions
Improvement Points
1. trust: As the legal owner, I cannot approve the use of AI for internal documents or project notes unless the retention period, whether the data is used for training, and how vendors are managed are explained clearly enough to satisfy internal policies. In particular, logistics companies must be strict about handling customer and contract information, and cost savings alone are not enough to make a decision.
Hypothesis: Legal heads ask for storage conditions and proof of non-training before cost effectiveness, so if this is unclear they will not move on to comparison reviews.
Action: Display retain, delete, and non-training using the same wording in contracts, settings screens, and audit materials, and show sample deletion records.
Expected Effect: Improved legal pass rate
Next Validation: Check whether showing storage conditions and deletion records helps legal teams proceed to comparison reviews.
2. trust: It is not clear how far entered internal documents and case notes are retained, or whether they are used for training or secondary use. In financial groups, this lack of transparency alone can stop internal approval.
Hypothesis: In finance, if there is opacity at the review entry point, things tend to stall easily, and accountability must be met before feature value.
Action: Consolidate retention periods, reuse controls, access management, and audit logs into one screen so admins can check them themselves.
Expected Effect: Reduced review burden
Next Validation: Verify whether one screen is enough for the items review staff want to confirm.
3. trust: In the financial sector, if it is unclear how internal documents and case notes are stored or trained on by external AI models, there is concern they may violate information management standards. Rather than a low-cost plan, clear data-handling terms and audit-response evidence are needed first.
Hypothesis: Transparency in data handling determines whether it can be adopted before price does.
Action: Prepare a proposal that prominently presents Zero Data Retention-level operational details and domestic management options.
Expected Effect: Improved conversion rate to sales opportunities
Next Validation: Confirm whether explicit non-training and domestic management become conditions for review.
Next Actions
Create a trust pack within 48 hours with consistent wording across contracts, settings screens, and audit materials
Priority: High
Wireframe a path for the 9,800 yen trial per project and an input wizard that shows the savings from one project
Priority: High
Prepare only three industry-specific templates first to improve first-time success rates
Priority: Medium
Risks / Unknowns
Confidence: medium
Friction: high
地域イベントの直前変更連絡を、漏れなく・証跡付きで・少人数で回せる連絡基盤
本件は、地域イベントや商店街催事の運営担当者が、出店者・出演者・ボランティアへの案内更新や当日変更を手作業で追う負担を減らすための、イベント単位の連絡自動化サービスである。名簿の取り込み、属性別の宛先仕分け、案内文の下書き作成、送信前確認、配信後の再通知、履歴保存までを一つの運用にまとめ、現場の連絡漏れと確認工数を下げる。購入するのはイベント主催の実行委員会責任者、商店街振興組合の事務局長、地域イベントの現場統括で、支払理由は「当日混乱の回避」「問い合わせ削減」「引き継ぎ可能な証跡」が明確に返ってくるため。現時点での購入障壁は、外部人工知能のデータ取り扱い、保存先、保持期間、削除手順、監査ログの説明不足で、特に公的機関や厳格な団体ではここが通過条件になる。したがって次の検証では、人工知能を使わない運用も含めた信頼資料、請求書払いと契約単位の明確化、既存メールや通知手段との併用導線を整え、限定試行に進めるかを確認する。
紙・口頭・転記を、現場の1回入力にまとめる衛生記録基盤
多拠点の惣菜・弁当製造現場向けに、製造指示・温度記録・品質確認を紙と口頭からスマホ中心の記録運用へ置き換える。写真読み取りと音声入力で入力負荷を下げ、未入力・異常値・証跡不足をその場で検知し、監査用の履歴を即時に出せる状態を作る。主な買い手は工場長と品質保証責任者で、現場責任者が日次の回収・確認コスト削減を強く求める。導入は既存帳票の画像からテンプレート化し、1拠点または1ラインから始める段階導入が前提。現時点の購入障壁は、現場の操作不安、切替時の生産影響、削減額の証明不足であり、次の検証では1分以内入力と削減金額の可視化を強めるべき。
設定の矛盾を、原稿の前で止める。
長編創作の個人作家・同人作家向けに、設定資料をアップロードして構造化し、原稿断片との整合性を即時チェックする管理型サービス。月額980円を主力に、無料体験から有料移行までを「設定の見える化→矛盾の根拠提示→修正履歴の保存」でつなぐ。購入者は主に本人決裁の個人作家で、支払い理由は手戻り削減と長編運用の継続性。現時点の最大障害は、汎用感想ツールに見えることによる信頼不足と、機密原稿を外部送信する不安。
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