Before We Begin

Next Steps — What We Need From You

Before we can move forward with development, we need to see how your current workflows actually work in practice. The best way to do this:

Step 1

Send us detailed walkthroughs of your two key workflows

For each workflow — Ideally: video screen recordings (e.g. Loom → loom.com) showing the full end-to-end flow. At minimum: screenshots of each step with annotations.

Flow A — Sales Proposal Process

Show the full flow from request (WhatsApp, email, call) through to finished proposal in Odoo. We want to see the Odoo screens, internal documents, pricing logic, and where the bottlenecks happen.

Sample proposals — 3–5 routine/simple and 3–5 complex, so we can see the format, data fields, and level of variation.

Flow B — Technical Product Matching

Show how a matching request arrives, how the team processes it, and what the output looks like. Include catalog tools, any cross-reference documents, and a real example (simple + complex).

Sample requests — 2–3 simple and 1 complex project-level (e.g. hospital-scale).

Step 2

Two calls (45 min–1 hour each) — one with the Sales Director, one with the Technical Director

Sales Director call: Edge cases, exceptions, pricing rules, what a great proposal looks like versus a painful one.

Technical Director call: Matching complexity, catalog structure, engineering team workflow, edge cases.

3. Everything else is on hold. Distributor self-service (WhatsApp assistant), marketing automation, and all other opportunities stay on the table — but we park them until we have validated these two workflows. We will revisit together once Modules 1 and 2 are underway.

Summary

TL;DR

What we propose

Module 1

Sales Proposal Automation. 700 proposals/week, most routine, all required before invoicing. AI auto-drafts routine proposals and pre-populates complex ones. Salespeople recover 1.5–2 hours/day for selling.

Module 2

Technical Product Matching. 3-person engineering team permanently backlogged on competitor-to-Aronlight matching. AI-powered product knowledge base turns days-long projects into hours.

Timeline: ~16–20 weeks total, first results in 6–10 weeks.
Up next: Distributor self-service (WhatsApp assistant) and marketing content automation are natural next modules once these two are running.

Context

Executive Summary

Aronlight distributes LED lighting solutions across Europe through 1,000+ B2B partners, having recently migrated to Odoo as its core operating system. Through two discovery calls and a detailed pain-sizing questionnaire, two areas emerged as the most urgent, highest-impact opportunities for AI automation:

Sales proposal generation — the single highest-volume process in the company
Technical product matching — the most time-intensive knowledge work, permanently backlogged

This proposal covers these two modules in a phased engagement designed to deliver first results within 6–10 weeks.

Two additional high-priority areas — distributor self-service via WhatsApp and multi-language marketing content automation — are identified as natural follow-on modules once these foundations are in place.

Discovery

The Pain — In Your Numbers

These are the figures shared directly by Manuel and the team during our discovery process:

700 proposals/week, every one required before invoicing in Odoo. ~600 are routine; 60–100 are complex and queue up in the technical department.
2+ hours/day per salesperson lost to admin instead of selling.
3-person engineering team permanently backlogged on product matching. A single hospital project (200+ line items) can take days. 20 lighting projects currently in queue.
200–300 inbound requests/day to the back office — the biggest growth bottleneck. Earmarked for the next phase.
Distributors are old-school family businesses who communicate via phone and WhatsApp — any solution must meet them where they are.

Module 1: Sales Proposal Automation

4–8 weeks development

The Problem

Every invoice requires a proposal first — it is an Odoo hard requirement. With 700 proposals per week, the sales team spends more time generating documents than selling. Simple proposals are fast individually but add up across the team. Complex proposals (60–100/week) involve custom pricing, technical specs, and often wait in a queue. Lost time means lost deals.

Salespeople currently work through WhatsApp, manually convert to email, and have no direct stock access from the field.

What We Will Build

An AI-powered proposal engine integrated with Odoo via API:

Routine proposals (~600/week): The system auto-drafts based on customer history, current pricing rules, and real-time stock. The salesperson reviews and approves with one click — from minutes to seconds.
Complex proposals (60–100/week): The system pre-populates everything it can (customer data, standard items, pricing tiers, stock status) and highlights what needs human judgment. Prep time cut by 50–70%.
Mobile-friendly interface so field salespeople can generate, review, and send proposals without routing through the back office.

Expected Impact

Routine proposals become near-instant. Complex proposal prep time drops dramatically. Salespeople recover 1.5–2 hours/day for revenue-generating activity. Turnaround time on quotes shortens, reducing lost deals.

Effort Estimate

4–8 weeks development. Requires Odoo API access, pricing logic documentation, and 2–3 working sessions with the Sales Director.

Module 2: Technical Product Matching Engine

6–12 weeks development

The Problem

Distributors and the sales team constantly need help matching competitor proposals to Aronlight equivalents. Simple requests go to the back office; complex ones go to a 3-person engineering team that is permanently backlogged. A hospital project with 200+ line items can take days to complete. Manuel identified this as one of the company’s key bottlenecks and asked directly whether AI could help.

What We Will Build

A product intelligence system powered by Aronlight’s full catalog:

Product knowledge base built from all catalog data — technical specs, datasheets, application guides, certifications — using RAG (retrieval-augmented generation).
Competitor matching tool: Upload or paste a competitor proposal, and the AI returns Aronlight equivalents ranked by spec match quality. Clear flags where no direct match exists or where manual review is needed.
Escalation path: Complex matches (novel specs, edge cases) are routed to the engineering team with the AI’s best suggestions pre-loaded — turning a blank-page exercise into a review-and-refine task.

Expected Impact

Simple product matching becomes near-instant for distributors and salespeople. Complex project matching (hospital-scale) reduced from days to hours. Engineering team freed to focus on Dialux projects and genuinely novel technical challenges rather than repetitive cross-referencing.

Effort Estimate

6–12 weeks development. Requires complete product catalog in digital format with technical specs, any existing competitor cross-reference data, and 3–4 working sessions with the Technical Director.

Schedule

Timeline Overview

Module	Focus	Dev Effort	Timeline
1. Sales Proposals	Proposal Automation	4–8 weeks	Weeks 1–10
2. Product Matching	Technical Matching	6–12 weeks	Weeks 6–20

Module 1 begins immediately. Module 2 begins as Module 1 enters pilot/rollout, building on the same Odoo API infrastructure.

Total engagement: approximately 16–20 weeks from kickoff to full rollout of both modules.

Process

How We Work

Each module follows the same cycle:

Phase 1

Discover

1 week. Working sessions with the relevant department head to document the current process, edge cases, and success criteria.

Phase 2

Build

2–5 weeks. Development with weekly demos to the department champion.

Phase 3

Pilot

1–2 weeks. Limited rollout with a small user group. Feedback, fixes, iteration.

Phase 4

Roll Out

1 week. Full deployment with training and documentation.

Each module includes 30 days of post-rollout support and optimization.

Two Critical Dependencies

Department head buy-in. As Manuel emphasized, without each director’s conviction, projects fail before they start. We will hold a dedicated kickoff session with each department head before beginning their module, and they will have a direct line to us throughout.

Odoo stability. Odoo implementation has 1–2 months remaining to fully stabilize. All integrations are designed to be resilient — if a specific Odoo module isn’t fully stable, we build with a temporary data bridge and reconnect once it’s ready.

Pricing

Investment

Pricing will be detailed in a follow-up conversation once scope is confirmed and we have completed the validation calls with the Sales Director and Technical Director. We can structure the engagement as fixed-price per module, a monthly retainer, or a hybrid — whichever model works best for Aronlight.

Each module’s investment covers: discovery, development, pilot, rollout, training, documentation, and 30 days post-rollout support.

Roadmap

What Comes After These Two Modules

Two additional high-priority areas are ready to scope once Modules 1 and 2 are underway:

Module 3 — Distributor Self-Service (WhatsApp Assistant)

200–300 daily back office requests, 50% simple and automatable. A WhatsApp-integrated AI assistant connected to Odoo would handle the routine half autonomously, freeing the back office for complex work and enabling growth without adding headcount.

Module 4 — Multi-Language Marketing Content Automation

3 people producing content for 300–500 product launches/year across 4 languages. Manuel estimated this team could go from 3 to 1. The product knowledge base from Module 2 becomes the foundation for automated datasheets, instructions, packaging copy, and newsletters.

Other areas for future consideration include Dialux project acceleration, inventory demand forecasting, and container consolidation optimization.