For reps
A mobile-first daily workspace for agenda, HCP context, call planning, compliant visit reporting, missed-call reasons, and offline-ready capture.
Working Thesis
Build a modern pharma field engagement product inspired by the iDoXs re-engineering direction.
Daloy should be a simpler, real-time system for medical representatives, field managers, and commercial operations teams. It should not begin as a generic CRM.
The product should serve pharma teams that need fast call reporting, compliant customer updates, lightweight approvals, and real-time coaching signals without the burden of a large enterprise CRM implementation.
What is Daloy?
Daloy is a focused pharma field execution workspace, not another generic CRM.
For managers
Live visibility into activity, coverage gaps, approvals, exceptions, and coaching opportunities while the cycle is still active.
For operations
Governed HCP master-data changes, configurable business rules, audit trails, imports/exports, and practical dashboards for SFE teams.
Core promise
Daloy helps pharma teams know what should happen in the field, what is actually happening, and what needs action next.
What “compliant visit reporting” means
In pharma field work, a visit report is not just a note that a representative saw a doctor. It should be a structured, policy-aware activity record that captures what happened in the visit while respecting company rules, promotional boundaries, privacy requirements, and audit expectations.
Daloy should guide reps to record the required facts quickly — who was visited, when, where, what products or topics were discussed, what next action was agreed, and why a planned call was missed or changed — without encouraging free-form claims that could create compliance risk.
What Daloy can help enforce
- Required fields before a visit can be submitted.
- Allowed product discussion based on assignment, territory, or customer profile.
- Time, location, device, and sync metadata where the company requires proof of activity.
- Proof-of-engagement options such as signature or consented photo, only when appropriate and configured.
- Reason codes for missed, rescheduled, or non-field activities.
- Audit trails for edits, approvals, late submissions, and exceptions.
- Exportable records for manager review, internal audit, and compliance investigation.
Strategic Framing
Daloy extends RagingRiver’s pharma IT lineage into AI-native field execution.
RagingRiver’s opportunity is not to present itself as a generic AI vendor. Most local pharma companies can benefit from AI capabilities, but many do not have the internal expertise to identify practical AI use cases, prepare the required data foundation, and govern AI safely in regulated commercial workflows. At the same time, many AI experts do not carry the pharma IT and SFE operating experience needed to understand field execution realities.
Daloy should therefore combine the July 2 product plan with RagingRiver’s deeper positioning: AI for pharma, grounded in decades of pharma field-system experience. iDoXs served as an SFE backbone or nerve system — a way for decision-makers to communicate strategy to the field and measure results back. Daloy keeps that strategy-to-field value proposition, then adds intelligence.
Strategic idea
iDoXs was the SFE backbone. Daloy becomes the intelligent spine — interpreting field signals, detecting gaps, and guiding decision-makers with data-driven recommendations.
AI provider for pharma
RagingRiver can help Philippine pharma companies adopt AI in ways that are practical, workflow-aware, and commercially useful.
Domain credibility
Daloy builds from RagingRiver’s DocCS/iDoXs experience, including SFE workflows, HCP engagement, field reporting, manager visibility, and commercial operations.
Intelligent SFE spine
The platform should not only record activity. It should guide the field, surface risks, explain gaps, and help leaders adjust strategy before the cycle is lost.
Starting Assumptions
Opportunity exists, but needs validation
Previous pharma viability work suggested there is enough opportunity to continue, but it is not yet a validated product bet.
iDoXs philosophy matters
Legacy iDoXs assets point to fewer steps, one-place workflows, minimal backend processing, real-time updates, and manager collaboration.
Likely first buyer
A regional or mid-market pharma company, distributor, or outsourced sales organization with spreadsheet-heavy, delayed, or overbuilt SFE processes.
Regulated from day one
The MVP should be designed for regulated commercial activity from day one, while avoiding patient-level data unless specifically required.
Product Positioning
Daloy
One-liner
A real-time field execution workspace for pharma teams: plan visits, report calls, manage HCP changes, and give managers live visibility without heavyweight CRM friction.
Primary users
- Medical representatives and field sales representatives
- District and regional sales managers
- Sales force effectiveness teams
- Commercial operations administrators
- Compliance reviewers, as secondary users
Jobs to be done
- Representatives record compliant visits quickly while details are fresh.
- Managers see field activity in near real time so they can coach, approve, and intervene.
- Commercial operations manages clean master-data changes without batch delays or manual reconciliation.
- Compliance gets traceable activity records, approval history, and configurable guardrails.
MVP Scope
The MVP should prove fast execution, manager visibility, governed HCP workflows, and compliance-aware activity capture.
Representative Mobile Workspace
- Daily agenda with planned visits, completed visits, missed calls, and non-field activities.
- HCP profile view with visit history, segmentation, specialty, location, and allowed products.
- Call planning view showing required visit frequency, current cycle coverage, due calls, and suggested schedule slots.
- One-tap visit completion with product discussion, notes, next action, and optional location/time metadata.
- Configurable proof-of-engagement capture for HCP signature or consented HCP selfie, including consent status and device metadata.
- Quick reschedule and missed-call reason capture.
- Offline-first capture with sync status and conflict handling.
Call Planning and Frequency Management
- Define required visit frequency by HCP tier, segment, specialty, product focus, or account plan.
- Show cycle coverage, completed calls, planned calls, missed calls, and remaining due calls.
- Flag HCPs below required frequency before the cycle closes.
- Suggest visits based on due frequency, route efficiency, clinic availability, previous missed calls, and product focus.
- Let reps schedule suggested visits from the co-pilot or call planning screen.
- Feed planned calls into the daily agenda, manager dashboard, and plan-health metrics.
- Allow manager review of plan coverage, frequency gaps, and make-up call patterns.
HCP Master Data Requests
- Request new HCP inclusion.
- Request HCP deletion or deactivation.
- Edit limited HCP fields based on permissions.
- Manager approval queue with approve, reject, and comment actions.
- Visible request status for the representative.
Representative HCP Co-pilot
- Conversational HCP search across assigned doctors and possible duplicates.
- Co-pilot actions for find HCP, add HCP, update profile, request deletion, and plan follow-up.
- Guided inclusion flow checking specialty, clinic, license ID, territory, and duplicate risk.
- Guided profile edit flow for address, clinic schedule, specialty, segmentation, contact preference, and rep notes.
- Follow-up recommendations based on cycle gaps, HCP tier, prior notes, and missed visits.
- Request generation for master-data changes requiring manager or operations approval.
- Status tracking for pending, approved, rejected, or needs-evidence HCP requests.
Manager Live Feed
- Real-time stream of visits, notes, exceptions, and data-change requests.
- Team activity board by representative, territory, product, and day.
- Coaching comments on selected activity.
- Approval queue for HCP changes and selected exceptions.
Commercial Operations Admin
- User, team, territory, product, and HCP list management.
- Configurable labels and fields.
- Business rules for visit windows, advance reporting, allowed products, and approval chains.
- CSV import/export for early pilots.
Dashboards
- Planned vs. actual calls.
- Call frequency by HCP tier or segment.
- Missed calls and non-field days.
- Product detailing mix.
- Approval aging.
- Sync health and late reporting.
HCP Co-pilot Governance
Make HCP management fast and conversational while preserving governed master data.
- Rep initiates action: asks the co-pilot or selects a quick action.
- Co-pilot checks controls: duplicates, required fields, territory assignment, allowed products, and evidence requirements.
- Daloy creates a structured request: inclusion, edit, deletion, or follow-up.
- Manager or operations reviews: approve, reject, comment, or request evidence.
- Approved changes update the governed HCP profile: and downstream agenda where relevant.
- Audit trail records the change: requester, approver, changed fields, timestamps, and before/after values.
Compliance and Trust Requirements
Compliance constraints should be built in from the start, not added later.
Core requirements
- Avoid patient-level data in MVP unless a pilot requires it.
- Maintain immutable audit logs for visits, approvals, edits, and admin changes.
- Store proof-of-engagement metadata when required: proof method, consent status, capture timestamp, and visit ID.
- Use role-based access across rep, manager, operations, and compliance roles.
- Support tenant-level configuration for promotional rules, product availability, visit timing, and retention.
- Encrypt data in transit and at rest.
- Build exportable activity history for audits and internal investigations.
- Include compliance review in pilot readiness.
Open legal/compliance questions
- Which launch geography comes first?
- Is sample accountability in version one?
- For Philippine pilots, should proof be signature, consented selfie, or both?
- Will Daloy process transfers of value, meals, speaker programs, or only field call activity?
- Will customers expect HIPAA, 21 CFR Part 11, GDPR, local data residency, or validation package support?
- What claims can be made without over-positioning compliance?
Differentiation
Compete on speed, simplicity, field adoption, and manager immediacy.
Faster deployment
Faster to deploy than enterprise CRM.
Rep usability
Easier for representatives to use every day.
Live collaboration
Built around live manager collaboration, not end-of-month reporting.
Configurable but light
Regional pharma configuration without becoming operationally heavy.
Avoid competing head-on with large CRM suites on breadth. Daloy should win as a focused field execution and manager visibility layer.
Validation Plan
Validate the problem, prototype, and pilot before overbuilding.
Phase 1: Problem Discovery
Target: 10–15 interviews.
- 4–6 medical representatives
- 3–4 field managers
- 2–3 commercial operations or SFE leaders
- 1–2 compliance stakeholders
Exit criteria: 3 buyer-side stakeholders confirm active, budget-relevant pain; 2 organizations agree to review a clickable prototype or pilot proposal; first-market scope is selected.
Phase 2: Prototype Validation
Clickable prototype should show daily agenda, call planning, HCP profile, HCP co-pilot management, report visit flow, HCP inclusion request, manager live feed, approval queue, and KPI dashboard.
Exit criteria: reps complete the core visit flow in under 60 seconds; managers identify at least 3 actionable live-feed events; operations confirms the admin model fits their sales model.
Phase 3: Pilot MVP
Pilot shape: 1 company, 1–2 teams, 10–30 representatives, 2–4 managers, 8–12 weeks.
- 80% weekly active rep usage
- Median visit report completion below 60 seconds
- Same-day manager visibility for 90% of completed visits
- HCP request approval time reduced by 50%
- Fewer manual spreadsheet reconciliations
- Positive manager confidence in dashboard accuracy
Build Roadmap
Month 0: DefinitionConfirm launch market and regulatory assumptions, create detailed product requirements, define tenant/role/data model, map user journeys, identify pilot candidates.
Months 1–2: Prototype and Design Partner WorkBuild clickable prototype, run interviews and usability sessions, draft pilot proposal and pricing hypothesis, define security and compliance baseline, decide sample accountability scope.
Months 3–5: MVP BuildRepresentative mobile web/native app, manager web app, admin web app, auth, RBAC, tenant config, audit logs, offline queue/sync, CSV imports/exports, basic dashboards.
Months 6–7: PilotOnboard pilot users and master data, run weekly adoption reviews, track analytics/support issues, conduct midpoint compliance and operations review, prepare ROI/case-study package.
Months 8–9: Commercialization ReadinessHarden security, audit exports, monitoring, and backups; package onboarding playbook; create sales deck and demo environment; finalize pricing; decide next segment.
Technical Direction
Recommended MVP architecture
Architecture
- Mobile-first representative app with offline-capable local storage.
- Web manager and admin app.
- API backend with tenant isolation.
- Event-driven activity feed for manager updates.
- Append-only audit log for regulated actions.
- Configurable rules engine for visit and approval behavior.
- Analytics layer separated from transactional records.
Key build decisions
- Start with mobile web/PWA unless native device capabilities become mandatory.
- Keep patient data out of the core model.
- Design HCP and product master-data imports around messy customer spreadsheets.
- Make sync state visible so user trust does not collapse when connectivity is poor.
Pricing Hypotheses
Keep pricing simple and pilot-friendly.
- Per active field user per month.
- Manager/admin seats included or priced lower.
- Paid onboarding for import, configuration, and training.
- Optional compliance/export package for larger customers.
Pricing should be tested during discovery rather than locked now.
Internal Build Workspace
Behind the public Daloy story is a controlled AI-assisted workspace for product, design, engineering, testing, deployment, and learning.
Daloy should be built through an AI-assisted software development life cycle, but not as uncontrolled automation. The portal should guide the team through each facet of development, capture decisions, generate artifacts, and keep human ownership clear.
The practical goal is a working cockpit: Boss Rio and the team can ask AI for help in a specific phase, review the generated output, approve decisions, and move the artifact into the next stage with traceability.
Portal concept
Inside the secure workspace, the team should be able to steer AI across the full SDLC — project management, requirements, conceptual design, architecture, development, deployment, testing, validation, and pilot learning.
Project Management
Maintain the product roadmap, sprint goals, backlog, risks, owners, decisions, and open questions.
- AI drafts sprint plans and task breakdowns
- Human owner confirms priority and scope
- Output: roadmap, backlog, decision log, risk register
Requirements Definition
Convert strategy, interviews, and pharma workflow knowledge into clear requirements with acceptance criteria.
- User stories by role
- Business rules and edge cases
- Output: PRD, user stories, acceptance criteria
Conceptual Design
Explore product flows, journey maps, information architecture, screen concepts, and operating scenarios.
- Rep, manager, admin, and compliance journeys
- Clickable mockup scenarios
- Output: flows, wireframes, prototype scripts
Architecture
Design the tenant model, RBAC, audit logging, offline sync, AI boundaries, integrations, and data architecture.
- System diagrams and API contracts
- Security/privacy design checks
- Output: architecture notes, data model, API spec
Development
Use AI to scaffold components, services, database rules, tests, documentation, and refactors under engineering review.
- Code generation with review
- Implementation checklists
- Output: working increments and reviewed code
Testing and QA
Generate test cases from requirements, verify edge cases, run regression checks, and prepare UAT scripts.
- Unit, integration, UAT, and offline/sync scenarios
- Traceability from requirement to test
- Output: test plan, QA matrix, release blockers
Deployment
Prepare release notes, deployment checklists, environment checks, rollback plans, and production readiness reviews.
- Build and deploy gates
- Configuration and migration checks
- Output: release checklist, deployment log, rollback plan
Validation and Pilot Learning
Analyze user feedback, pilot metrics, adoption issues, support tickets, and improvement opportunities.
- Feedback clustering and adoption analysis
- Pilot success metrics review
- Output: learning report and next sprint recommendations
Governance
Protect privacy, compliance, security, domain correctness, and human accountability across every AI-assisted output.
- Named owner for every artifact
- No sensitive data in unmanaged AI tools
- Output: approval trail and governance checklist
How Boss Rio should use it
- Select the SDLC facet to work on.
- Give the AI the current objective, constraints, and target artifact.
- Review the generated output and decide: accept, revise, defer, or reject.
- Promote approved output into the plan, backlog, mockup, codebase, or release notes.
AI agent modes
- Analyst: synthesize research, interviews, metrics, and decisions.
- Product assistant: draft requirements, stories, flows, and acceptance criteria.
- Architect: propose architecture, data models, APIs, and risk controls.
- Developer: implement scoped changes with tests and review notes.
- QA: generate and execute test plans, regression checks, and UAT scripts.
Control rules
- AI drafts; named humans approve.
- Every major artifact must show source, owner, status, and next action.
- Security, privacy, and compliance gates cannot be skipped.
- Production deploys and real data changes require explicit approval.
Suggested portal modules
- Dashboard: current phase, sprint goal, blockers, pending approvals.
- Backlog: epics, user stories, acceptance criteria, priorities.
- Requirements: PRD sections, business rules, traceability matrix.
- Design Lab: flows, mockups, demo scripts, usability findings.
- Architecture: diagrams, data model, API contracts, AI boundaries.
- Build Room: implementation tasks, code review notes, release branches.
- QA Center: test plans, defects, UAT scripts, regression status.
- Deployment: release checklist, environment status, deploy history.
- Decision Log: accepted decisions, rejected options, rationale, owner.
Artifact status model
- Draft: AI or team has produced a first version.
- Under Review: human owner is checking accuracy, feasibility, and fit.
- Approved: accepted as current working direction.
- Implemented: reflected in mockup, code, config, or process.
- Validated: tested through QA, stakeholder review, or pilot evidence.
- Revisit: known uncertainty, dependency, or decision deferred.
Recommended next build step: use the secured portal to manage prompts, artifact templates, status fields, owners, and links to the current plan/mockup/code outputs.
Project Team Composition
Daloy needs a small, senior, cross-functional team with clear ownership.
The first Daloy team should be lean, but it cannot be purely technical. The product depends on pharma/SFE domain judgment, AI capability, workflow design, secure engineering, validation, and pilot execution. Each role should have clear accountability so AI-assisted development accelerates the work without blurring ownership.
For the prototype and MVP stage, some people may hold multiple roles. What matters is that every responsibility has an owner: product decisions, pharma workflow correctness, technical architecture, AI behavior, data governance, quality, deployment, and customer validation.
Executive Sponsor / Product Vision
Owns: overall direction, RagingRiver positioning, strategic tradeoffs, partner/customer alignment, and final go/no-go decisions.
- Protects the product thesis
- Approves scope and positioning
- Connects Daloy to target pharma buyers
Product Lead
Owns: product requirements, roadmap, user journeys, backlog priority, acceptance criteria, and release readiness.
- Turns strategy into buildable scope
- Maintains product plan and mockup
- Decides MVP vs later scope
Pharma / SFE Domain Lead
Owns: correctness of field-force workflows, targeting, call frequency, HCP rules, manager workflows, and commercial operations assumptions.
- Validates pharma realism
- Defines SFE rules and pilot scenarios
- Reviews AI recommendations for field fit
UX / Prototype Lead
Owns: clickable prototype, screen flows, interaction design, demo narrative, usability testing, and field usability simplification.
- Designs rep-first workflows
- Keeps reporting fast and simple
- Prepares demo scripts and test tasks
Technical Architect / Engineering Lead
Owns: architecture, codebase standards, API boundaries, data model, offline/sync approach, deployment, and technical feasibility.
- Defines tenant/RBAC/audit architecture
- Reviews AI-generated code
- Owns build quality and maintainability
AI / Data Lead
Owns: AI use cases, recommendation logic, prompt behavior, data preparation, model boundaries, explainability, and evaluation criteria.
- Defines AI-assisted workflows
- Prevents unsafe automation
- Measures recommendation usefulness
QA / Validation Lead
Owns: test strategy, regression coverage, UAT scripts, pilot acceptance criteria, and traceability between requirements and behavior.
- Creates test cases from requirements
- Validates offline/proof/approval edge cases
- Tracks defects and release blockers
Security / Compliance Advisor
Owns: privacy, access control, audit trail expectations, proof-of-engagement policy review, retention assumptions, and compliance claims.
- Reviews data governance
- Checks proof and consent flows
- Prevents over-claiming compliance
Pilot / Customer Success Lead
Owns: pilot onboarding, training, support workflow, feedback collection, adoption metrics, and customer success reporting.
- Coordinates pilot users and master data
- Runs weekly adoption reviews
- Feeds learnings back into roadmap
Suggested lean MVP team
- 1 Executive Sponsor / Product Vision owner
- 1 Product Lead with pharma workflow support
- 1 UX/prototype designer or product designer
- 1 Engineering Lead / full-stack developer
- 1 AI/data engineer or AI workflow owner
- 1 QA/validation owner, part-time at first
- 1 Compliance/security advisor, part-time but involved early
- 1 Pilot/customer success owner when design partners are active
Responsibility rule
AI can help any role draft, analyze, test, summarize, or prototype. But AI should not be treated as the accountable owner. Every generated output must have a human reviewer and a named decision owner before it becomes part of the product, codebase, customer proposal, or compliance position.
Clickable Mockup
Explore the first HTML mockup for Daloy’s field execution workflow.
The mockup demonstrates Rep Today, Call Planning, HCP Co-pilot, Visit Report, Manager Command Center, Approvals, and Admin/Audit screens.
Immediate Next Steps
- Choose first market and target segment.
- Turn this plan into a one-page concept brief for interviews.
- Draft the interview guide.
- Build the clickable prototype outline.
- Identify 20 candidate interview contacts.
- Decide whether the first artifact should be a Figma prototype, lightweight web demo, or sales-style concept deck.