# Route Intelligence Flow v0.1

**Date:** 2026-07-03  
**Status:** Product flow / source of truth  
**Workspace area:** `02-product/sales-companion/`  
**Related files:** `mobile-field-companion-principle-v0.1.md`, `mobile-capture-flow-v0.1.md`, `product-principles.md`, `WORKING_CONTEXT.md`, `01-company/decision-log/decision-log.md`

## Core Idea

Sales Companion does not start only after a visit.

It must also support the beginning of every city or regional sales trip.

A Sales Manager often enters a territory with a mixed agenda:

- fixed appointments with existing customers,
- acquisition of potential new partners,
- spontaneous local discovery,
- tips from people in the area,
- and a changing amount of available time during the day.

Companion should turn this into a flexible field route, not a static list.

> The user defines the field day. Companion discovers potential partners, builds a route, adapts during the day and keeps the trip connected to relationship memory and field capture.

## Why This Matters

Today, a lot of early trip planning happens through fragmented tools:

- Google Maps searches,
- notes from previous trips,
- WhatsApp tips,
- existing customer appointments,
- manually saved places,
- memory,
- intuition.

Typical search inputs might be:

- `coole Inneneinrichter Wien`,
- `skandinavisches Design Wien`,
- `interior design store Vienna`,
- `architecture office near me`,
- `concept store design furniture`,
- `hotel interior studio`,
- `furniture showroom`,
- `design shop near current location`.

Sales Companion should understand this mode of work and make it operational.

The goal is not to replace the Sales Manager's intuition.

The goal is to support it, structure it and make the best use of limited field time.

## Trip Types

### 1. Existing Customer Route

The day is built around fixed meetings with existing customers or partners.

Companion should:

- lock confirmed appointment times,
- calculate travel windows,
- suggest nearby optional visits,
- prepare customer context before each appointment,
- create capture prompts after each visit,
- protect the fixed schedule from overload.

### 2. Acquisition Route

The day has no fixed appointments, or only very few.

The objective is to identify and visit potential new partners in a city or region.

Companion should:

- discover relevant prospect categories,
- cluster them geographically,
- rank by probable fit,
- suggest walk-in route options,
- mark uncertainty clearly,
- avoid pretending that unverified prospects are qualified leads.

### 3. Hybrid Route

Most real field days are hybrid.

There may be one or two fixed appointments, plus open time blocks for acquisition, inspiration or local tips.

Companion should:

- protect fixed meetings,
- fill free windows with nearby prospects,
- keep fallback options ready,
- adapt if a meeting takes longer,
- suggest a realistic next best stop instead of forcing the original plan.

## What The User Gives Companion

At the start of a trip, the user may provide:

- city or region,
- date and available time window,
- hotel, train station, airport, parking or starting point,
- existing appointments,
- preferred end point,
- product focus,
- target partner types,
- known customers,
- no-fit categories,
- preferred travel mode,
- maximum walking time,
- lunch or break constraints,
- personal tips from the area,
- search prompts or vibe descriptions.

The user should be able to say something like:

> I am in Vienna tomorrow from 10:00 to 17:30. I have a fixed appointment at Scandinavian Design House at 14:00. Before and after that, find relevant interior stores, planning studios and Scandinavian design contexts for Pedestal. I am open to walk-ins. Keep it realistic and include nearby tips if something is close.

## Partner Discovery Model

Companion should support discovery across several categories.

### Retail / Dealer Potential

- interior stores,
- furniture dealers,
- design stores,
- concept stores,
- premium lifestyle stores,
- museum shops with design relevance,
- department store design floors.

### Planning / Project Potential

- interior architects,
- architecture offices,
- planning studios,
- hospitality design studios,
- workplace consultants,
- commercial interior planners.

### Hospitality / Public Space Potential

- boutique hotels,
- serviced apartments,
- co-working spaces,
- meeting and event spaces,
- design-led cafés or lobbies,
- cultural institutions.

### Industry Intelligence

- local design people,
- makers,
- curators,
- store owners with strong market knowledge,
- journalists,
- local insiders.

## Discovery Queries

Companion should be able to translate natural search intent into search clusters.

Example user input:

> Search for cool interior people and Scandinavian design contexts near me.

Possible query clusters:

```yaml
discovery_queries:
  german:
    - coole Inneneinrichter Wien
    - skandinavisches Design Wien
    - Designmöbel Wien
    - Interior Studio Wien
    - Einrichtungshaus Wien Design
  english:
    - interior design store Vienna
    - Scandinavian design Vienna
    - furniture showroom Vienna
    - interior architecture studio Vienna
    - concept store design Vienna
  category_expansion:
    - architecture office
    - planning studio
    - boutique hotel interior
    - design museum shop
    - coworking design space
```

The exact external data source is not defined in this file.

For a real implementation this could involve map search, local business data, public web search, existing Companion memory or manually entered tips.

## Qualification Model

Discovery alone is not enough.

Companion should rank and label prospects without overclaiming certainty.

Minimum qualification fields:

```yaml
prospect:
  name: string
  city: string
  category:
    - retail
    - planning studio
    - architecture office
    - hospitality
    - cultural / museum
    - local insider
  source:
    - existing memory
    - map search
    - web search
    - user tip
    - discovered nearby
  fit_hypothesis: string
  product_fit:
    - Pedestal TV stands
    - Power Bars / power solutions
    - accessories
    - planning use
    - POS use
    - unclear
  visit_mode:
    - fixed appointment
    - walk-in candidate
    - call first
    - research only
  confidence:
    - low
    - medium
    - high
  uncertainty:
    - opening hours unverified
    - contact unknown
    - category unclear
    - address needs verification
```

Companion must separate three things:

1. known facts,
2. fit hypothesis,
3. open uncertainty.

## Route Building Logic

A good field route is not only the shortest path.

It should consider:

- fixed appointment times,
- opening hours where known,
- realistic travel time,
- walking or driving mode,
- geographic clusters,
- relevance score,
- time needed per visit,
- relationship value,
- product fit,
- weather or transport disruption if available,
- fatigue and human realism,
- fallback options.

The route should produce different layers:

```yaml
route:
  locked_appointments:
    - existing customers or confirmed meetings
  primary_visits:
    - high-fit planned prospects
  optional_nearby:
    - good fits if time remains
  fallback_list:
    - useful alternatives if a visit fails
  tips_nearby:
    - food, coffee, parking, local context, visual inspiration
```

## During-Day Adaptation

The field day changes.

Companion should support live adjustment when:

- a meeting takes longer,
- a visit is a no-fit,
- someone gives a new tip,
- a prospect is closed,
- the user is nearby a relevant place,
- the user has only 30 minutes left,
- the user wants to prioritize quality over quantity,
- the day should end near the hotel, station or parking location.

Example:

> I just left studio phyne. I have 45 minutes before the next appointment. Is there anything relevant nearby?

Companion should answer with a small set of realistic options, not a long generic list.

## Nearby Tips

Nearby tips are part of the Companion experience.

They can include:

- relevant prospects,
- interesting local stores,
- design-related places,
- good cafés for a short pause,
- parking or transit notes,
- places that add field understanding even if they are not direct leads.

This makes Companion feel like a real field companion, not an office system.

However, tips should stay disciplined:

- do not overload the user,
- explain why a tip is relevant,
- distinguish lead potential from inspiration,
- mark information that still needs checking.

## Outputs Before The Trip

Before the field day, Companion should generate:

- trip brief,
- fixed appointment overview,
- route sequence,
- prospect shortlist,
- map-ready stop list,
- open uncertainties,
- files or samples to prepare,
- customer context cards,
- capture prompts for each planned stop.

## Outputs During The Trip

During the day, Companion should generate:

- next best stop,
- nearby option list,
- route adjustment,
- visit prep card,
- post-visit capture prompt,
- follow-up queue,
- no-fit note,
- new lead entry,
- field-report marker.

## Outputs After The Trip

After the field day, Companion should generate:

- day summary,
- completed visits,
- missed visits,
- new leads,
- no-fits,
- follow-ups,
- sample tasks,
- product-market learnings,
- city knowledge update,
- Field Report draft or addendum.

## Example — Vienna Hybrid Route

Input:

```yaml
city: Vienna
trip_type: hybrid
available_window: 10:00-17:30
fixed_appointments:
  - Scandinavian Design House at 14:00
product_focus:
  - Pedestal TV stands
  - Power Bars
  - planning use
acquisition_targets:
  - interior stores
  - Scandinavian design contexts
  - planning studios
  - concept stores
travel_mode: walking / public transport
```

Companion should produce:

```yaml
morning:
  objective: acquisition cluster before fixed appointment
  visits:
    - high-fit interior / design stores near morning route
    - optional nearby design context
midday:
  objective: arrive safely for fixed appointment
  locked_visit: Scandinavian Design House
  prep_card:
    - existing context
    - product focus
    - likely follow-up files
afternoon:
  objective: planning studio / nearby acquisition
  visits:
    - planning studios or strong prospect cluster near appointment area
    - fallback options if time remains
end_of_day:
  objective: capture and consolidate
  outputs:
    - follow-up queue
    - field report signals
    - route learning
```

## MVP Version

The first MVP does not need full automation.

It can work as a guided planning assistant:

1. User enters city, date, time window and fixed appointments.
2. User defines product focus and target partner types.
3. Companion proposes search clusters.
4. User approves or edits the discovery direction.
5. Companion creates a shortlist and route draft.
6. User confirms the route.
7. During the day, user can ask for nearby options or re-routing.
8. After each visit, the Mobile Capture Flow takes over.
9. At the end, Companion creates a day summary and follow-up queue.

## Relation To Mobile Capture Flow

Route Intelligence starts the day.

Mobile Capture Flow processes each visit.

Together they form the first complete Sales Companion field loop:

```text
Plan the route → visit the field → capture each stop → generate follow-ups → update memory → improve the next route
```

## Non-Negotiables

- Do not invent addresses, contacts or opening hours.
- Mark source and confidence.
- Keep fixed appointments protected.
- Keep the route human and realistic.
- Avoid overloading the user with generic lists.
- Distinguish lead potential from inspiration.
- Preserve the user's field intuition.
- Use Markdown as source of truth.
- Use HTML as the visual surface.
- Treat trip planning as part of field intelligence, not admin work.


## Connection To Market Map Layer

Route Intelligence should not start from an empty map.

It should use the Market Map Layer to understand:

- existing customers,
- known prospects,
- local tips,
- white spots,
- product-fit hotspots,
- category clusters,
- recently visited areas,
- and follow-up-sensitive contacts nearby.

After each route, the Market Map Layer should be updated so the next trip becomes smarter.