AI Pricing & Valuation System

# Tender Brief – AI Pricing & Valuation System

## Project Overview

We are a multi-site second-hand retail business operating a buy, sell and buyback model.

We currently value products using a combination of:

* eBay sold prices
* CEX prices
* Customer history
* Our own large historical transaction database
* Current stock levels
* Staff experience

We are looking for a software / AI development partner to build an intelligent pricing engine that integrates with our existing EPOS system.

## Objective

The system should recommend the optimal price we should offer a customer for a product.

It should use both our own historical data and external market data to produce a more dynamic and commercially accurate valuation.

The aim is to improve:

* Buying accuracy
* Gross profit
* Stock turn
* Consistency between stores
* Offer acceptance
* Inventory management

## Initial System Requirements

The first version should ideally:

* Identify and standardise the product being valued
* Analyse our historic purchase and selling data
* Incorporate eBay and CEX market pricing
* Predict likely resale price
* Predict expected time to sale
* Consider current stock and sales demand
* Recommend an acquisition price
* Provide a confidence score
* Allow staff to override the recommendation
* Record results so the system can improve over time

Example output:

**Recommended offer: £405**

Expected resale: £550
Expected sale time: 19 days
Current stock: 4
30-day sales: 27
AI confidence: 96%

## Technical Approach

We expect the solution to integrate with our existing EPOS through an API.

We are open to the supplier recommending the most appropriate technologies, potentially including:

* Machine learning such as XGBoost or LightGBM
* OpenAI or similar AI models for product identification and image analysis
* External market-data integrations
* Predictive pricing models
* Inventory and demand modelling

We do not expect a large-language model alone to determine product prices.

## Our Data

We hold a substantial database including information such as:

* Products
* Purchase prices
* Selling prices
* Purchase and sale dates
* Stock history
* Store/location
* Product condition
* Customer history
* Buyback history
* Staff valuations
* Current inventory

The successful supplier will need to assess the quality and structure of this data and recommend how it should be used.

## Initial Rollout

We would ideally operate the system in **shadow mode** initially.

The AI would calculate its recommended price alongside our existing staff valuations without influencing the actual transaction.

This would allow us to compare:

* AI recommendation
* Staff recommendation
* Actual amount paid
* Eventual selling price
* Stock days
* Actual profitability

Only once the model has demonstrated improved performance would it be introduced into live pricing.

## Future Development

The system should ideally be capable of later expanding into:

* Buyback-specific pricing
* Dynamic retail pricing
* Automatic markdown recommendations
* Store-to-store stock transfers
* Inventory optimisation
* Product demand forecasting

## What We Would Like From Suppliers

Please provide:

* Your proposed technical solution
* Relevant experience
* Recommended AI / machine-learning approach
* How you would integrate with our EPOS
* How you would use our existing data
* Approach to eBay / CEX market data
* Development costs
* Ongoing costs
* Proposed project stages
* Examples of similar work

We are particularly interested in suppliers with experience in retail pricing, second-hand goods, machine learning, dynamic pricing, inventory optimisation or EPOS integrations.

## Key Question

As part of your proposal, please explain:

**How would you prove that the AI pricing recommendations are commercially better than the decisions currently being made by our experienced staff?**