Tezeract Build an AI Matching Engine That Profiles Brands in Minutes and Connects Them to the Right Wholesalers Automatically

Impact

Project Overview

Fashion B2B moves on relationships, but relationships don’t scale in spreadsheets. Fashionnet Consulting Corp (FN-AD) had built a real business connecting fashion brands with wholesalers across markets and seasons. The problem wasn’t the model. It was the infrastructure holding it back.

Brand profiles lived in Excel files. Different team members kept different versions. Fields were missing, no MOQ, no lead times, no logistics notes. Matching was done by hand, one profile at a time, at roughly 10 profiles per person per day. When buying season opened, the team was already behind.

Tezeract built FN-AD Match: a custom fashion brand matchmaking platform with an AI matching engine at its core, one that scrapes and profiles brands automatically, scores fit against wholesaler criteria, and surfaces ranked matches with reason codes the team can act on immediately.

“The team was organized in their approach to project management. I was most satisfied by their advanced understanding and experience in AI technology and understanding of current trends and capabilities.”

Jan, Executive & CEO, Fashionnet Consulting Corp (FN-AD)

Customer Profile

FN-AD Match is a program inside Fashionnet Consulting Corp. The team works in fashion B2B. They help brands find the right wholesalers across markets and seasons. They speak with brands and agents every day, so they see real problems in partner discovery and follow up.

Before Tezeract, FN-AD used Excel files to profile brands, capture leads, and track outreach. People kept separate copies. Edits got lost. Data sat in folders and email. Category labels were not standard. Many profiles missed basics like MOQ, lead times, and logistics. There was no data pipeline to pull and refresh profiles. There was no scoring or KPI tracking for match quality or time to match.

When Your Entire Business Depends on Making the Right Match - and Your Only Tool Is a Spreadsheet

FN-AD’s value proposition was clear: connect the right brand to the right wholesale partner, faster than anyone else in the market. The reality of execution was the opposite of that promise.

Primary Challenge

Every brand profile was built by hand. A team member would visit a brand’s website, scroll through their social pages, open a lookbook PDF, and manually fill in a spreadsheet row, product category, target market, price band, region, style notes. At roughly 10 profiles per person per day, scaling the operation meant hiring more people to do the same manual work.

There was no automation, no pipeline, and no intelligence layer to score fit or surface the best matches.

Secondary Challenges

Brand profiles were stored in separate Excel files across team members, no version control, no single source of truth, and no way to know which copy was current

Critical fields were routinely missing.

Category labels and style tags weren’t standardized across the team, making it impossible to run reliable comparisons or filters

Matching was entirely manual, a team member would review profiles side by side and make a judgment call, with no scoring, no reason codes, and no feedback loop to improve over time

Seasonal buying windows are short in fashion. When the team was still building profiles during peak season, deals were being missed

There was no KPI tracking, no visibility into time to first match, match acceptance rate, or operator throughput

Turn Manual Brand Matching Into an Automated System

Stop spending hours on research and guesswork. Build a system that profiles brands and surfaces the right matches instantly.

Why FN-AD Chose a Custom Build Over Off-the-Shelf Tools

Jan had looked at the obvious alternatives before approaching Tezeract. Generic CRM platforms could store profiles, but couldn’t score fit. Marketplace SaaS tools existed for wholesale discovery but weren’t built for FN-AD’s consulting model, where the team acts as the intelligent intermediary.

No off-the-shelf tool could replicate the nuance of how FN-AD actually worked.

Four paths were evaluated:

Generic CRM + spreadsheet forms – better than raw Excel, but still manual at the profiling and matching layer. No AI, no scoring, no automation.
Wholesale marketplace SaaS – designed for brands to self-list, not for a consulting team to manage and curate matches on behalf of clients. Wrong model entirely.
Internal build with freelancers – possible in theory, but no single freelancer had the full stack needed: scraping, NLP, computer vision, match scoring, and a production-grade web app.
Custom build with Tezeract – higher upfront investment, but the only path that could automate profiling end-to-end, build a real AI matching engine for fashion, and deliver a platform that fit FN-AD’s actual workflow.

The evaluation came down to five criteria:

Profiling quality – could the system pull accurate, complete brand data from websites, social pages, and lookbooks automatically?
Match explainability – could the team see why a match was recommended, not just a score?
Taxonomy control – could FN-AD define and enforce their own category labels, style tags, and price bands?
Delivery confidence – weekly demos, clear milestones, and a team that understood fashion B2B – not just AI in the abstract

Tezeract was selected on all five. The proposal included a working data model in week one, a live prototype by week six, and a team structure that matched FN-AD’s pace.

The Solution

Tezeract built FN-AD Match as a two-sided platform – a Brand Panel and a Wholesaler Panel – connected by an AI matching engine for fashion that scores fit, surfaces ranked matches with reason codes, and learns from every accept or reject decision the team makes.

Core Architecture

The platform runs in three layers. A data ingestion layer that scrapes brand websites, social pages, and lookbooks automatically. An intelligence layer that classifies brands using NLP, reads product imagery using computer vision, scores fit against wholesaler criteria, and generates ranked match recommendations. A platform layer where the FN-AD team manages profiles, reviews matches, and tracks KPIs – all in one place.

AI Technologies Implemented

AI matching engine with a feedback loop – scores brand-wholesaler fit across category, price band, target market, and region; learns from accepted and rejected matches to improve recommendations over time

LLaVA (Large Language and Vision Assistant) for computer vision – reads lookbook images and product photos to extract style themes, aesthetic tags, and product category signals

NLP for brand classification – standardizes category labels, style tags, and keywords across all profiles regardless of how the source data was formatted

Web scraping via Apollo Scraper and LinkedIn Scraper to pull brand data from websites and professional profiles automatically

Platform Features

Brand Panel

Auto-generated brand profiles from scraped data – editable, searchable, and version-controlled.
Standardized fields: location, product category, target group, price band, preferred market, wholesaler group, age group.
Real-time search and filter across the full brand database.

Wholesaler Panel

Full wholesaler profiles with focus areas, preferred categories, regions, and price bands
Reverse matching flow – collects needs and recommends brands based on current inventory and positioning.

Intelligent Brand Classification

Groups similar brands by style, category, and market positioning
Surfaces competitor and “next-to” brands to enrich profiles and give wholesalers market context
Improves fashion business automation by removing the need for manual tagging and categorization

Want These Features in Your Own Product?

From auto profiling to intelligent match scoring, we can build the same capabilities for your business.

Empowering FN-AD with Our Cutting-Edge AI Technology Stack for Automation Excellence

React js

Next js

Python

OpenAI

AWS

PostgreSQL

FastAPI

TensorFlow

Pytorch

Apollo Scrapper

LinkedIn Scrapper

CI/CD

LLaVA

Tools & Technologies

Description

Frontend Development

Backend Development

AI Server

Database Management

Authentication and Security

Development Tools

Cloud Infrastructure & Analytics

Phases wise Deployment

Data Model & Taxonomy Design

Audited all existing Excel files and mapped every field across brand and wholesaler profiles. Defined a clean, standardized schema – product category, target group, region, price band, style tags, MOQ, lead times, logistics. Set dedupe rules and merge logic. Migrated historical data from spreadsheets into PostgreSQL in batches, with a staging layer to catch and fix errors before loading.

Key milestone: Clean database live. Taxonomy locked. Historical data migrated with zero field loss.

Scraping, NLP, Computer Vision & Matching Engine

Built the scraping pipeline using Apollo and LinkedIn scrapers. Integrated LLaVA for lookbook image analysis. Trained the NLP classification layer on FN-AD’s taxonomy. Built the match scoring engine with reason code generation. Stood up the Brand Panel and Wholesaler Panel with real-time search, filter, and profile editing.

Key milestone: First 1,000 brand profiles auto-generated. Match scoring engine live with reason codes.

Pilot & Validation

Ran the platform on a sample set of brands and wholesalers. FN-AD reviewed match recommendations against their own judgment. Adjusted score weights, refined reason code labels, and fixed classification errors surfaced during live review.

Key milestone: First 100 accepted matches validated. Match accuracy confirmed at 75%+.

Tuning, Hardening & KPI Dashboards

Tuned models based on pilot feedback. Hardened the API for production load. Launched Metabase KPI dashboards for time to first match, acceptance rate, and operator throughput. Rolled out to the full FN-AD team with role-based access and an audit trail.

Key milestone: KPI dashboard live. Platform in active use across the full team. 60% reduction in manual work per match cycle confirmed.

Key Features

Automated Brand Profiling

The platform builds a complete brand profile in under 5two minutes. Fields are standardized, deduplicated, and version-controlled. What used to take a team member50-60 30–45 minutes of manual research now happens without human input. This is fashion business automation applied to the most time-consuming part of the matchmaking workflow.

AI Match Scoring with Reason Codes

Every match recommendation comes with a score and a plain-language reason code – “Women’s Contemporary | EU Focus | Mid Price.” The team doesn’t have to guess why a match was surfaced. They can accept or reject it in one click, and every decision feeds back into the model to sharpen future recommendations. This is what separates a real AI matching engine for fashion from a simple filter or search tool.

Reverse Matching for Retailers

Most matchmaking platforms work in one direction – brand to wholesaler. FN-AD Match also runs in reverse. A wholeselerretailer submits their buying criteria – category, region, price band, season – and the platform recommends brands that fit. This opens a new service line for FN-AD without adding operational complexity, and it’s the foundation for the next phase of the platform’s growth.

Competitor & Next-To Brand Discovery

The NLP classification layer doesn’t just tag brands. It groups them. Similar brands are clustered by style, category, and market positioning, so wholesalers can see the competitive landscape around any brand they’re evaluating. This context makes match decisions faster and more confident, and it gives FN-AD a richer conversation to have with both sides of the market.

Ready to Build Your Own AI Matching Engine?

If FN-AD Match shows what’s possible, let’s build a system tailored to your business model and market.

The Results

FN-AD Match turned a manual, spreadsheet-dependent operation into a platform that profiles brands automatically, scores matches intelligently, and gives the team the visibility they need to move fast during buying season.

Stakeholder Impact

For the FN-AD team

Brand profiling reduced from 30–45 minutes to under two minutes

Match reviews handled in a single dashboard

Faster and more consistent data processing

Ranked recommendations with simple accept or reject actions

For Fashion Brands

Faster connections with the right wholesale partners

Matches based on category, pricing, and target audience

No long waiting times for introductions

Better fit from the start

For Wholesalers & Retailers

Each match includes scores and clear reasoning

Curated list of brand recommendations

Insights on market position and competition

Decisions supported by real data

For FN-AD Leadership

Platform scales across regions and product categories

Supports new partners without increasing team size

Full visibility into performance through KPI dashboards

Better results without adding more coaching hours

“Flexible, thinking alongside me, strong in communication. All KPIs were met on time. Adjustable to any additional needs from me.”

Jan, Executive & CEO, Fashionnet Consulting Corp

Benefits of the Fashion Brand Matchmaking Platform

Smarter Brand-Wholesaler Connections

With AI-powered matchmaking in fashion, clients using FN-AD’s platform can now instantly find relevant wholesale partners, powered by AI for ideal customer profiling and intelligent brand classification.

Stronger Brand Positioning through Insights

Through fashion brand profiling and automated competitor analysis, fashion brands get valuable visibility into their positioning and potential matches, improving their go-to-market strategy.

Scalable Fashion Business Automation

The platform provides long-term scalability for all users, from boutique labels to large-scale fashion groups, helping them find ideal customer profiles with AI and automate their expansion with ease.

Precision Targeting with AI-Powered Insights

Brands on the platform benefit from AI for ideal customer profiling, which helps them discover their most promising wholesale partners based on target market, category, and region.

Fashion Tech-Driven Growth Acceleration

The platform exemplifies fashion tech success stories, enabling small to mid-sized fashion brands to compete with enterprise-level operations using intelligent tools.

Smart Filtering and Navigation for Better Matchmaking

With advanced search and brand classification using NLP and AI-powered matchmaking, platform users can easily navigate and filter through thousands of brands and wholesalers based on custom criteria.

What FN-AD Match Proves

The fashion industry’s B2B layer – the part where brands find wholesale partners and wholesalers discover new labels – has been running on personal networks and spreadsheets for decades. FN-AD Match shows what happens when you replace that infrastructure with a purpose-built AI matching engine for fashion: profiling gets faster, matches get smarter, and the business can grow without the headcount growing with it. If you’re running a fashion consulting, wholesale, or distribution business and your team is still building profiles by hand and matching on instinct, that’s a solvable problem. Let’s talk.

Frequently Asked Questions

What is an AI matching engine for fashion and how does it work?

An AI matching engine scores the fit between a fashion brand and a wholesale partner. It reads brand data such as product category, price band, target market, region, and style. It can also read images to spot themes and tags. On the partner side, it stores buyer focus, season needs, regions, and price bands. The engine compares both sides and produces a ranked list with reason codes like “women’s contemporary, EU focus, mid price.” Teams can accept or reject matches. The system learns from these actions and improves over time. This helps you connect fashion brands with wholesalers in minutes. It also supports recommending fashion brands to retailers for reverse matching. Leaders get clear KPIs such as time to first match, acceptance rate, and operator throughput. The result is a repeatable process that scales as your network grows.

How does AI match fashion brands with the right wholesalers or distributors?

AI enables automated fashion brand matching with wholesalers by analyzing key brand attributes—such as product type, target market, and price range—and comparing them with the preferences of wholesalers and distributors. The platform applies intelligent brand-wholesaler matching to instantly connect fashion brands with relevant retailers, improving match quality and eliminating manual guesswork in wholesale matchmaking in fashion.

Can AI recommend retailers based on product style, pricing, and audience?

Yes, AI can recommend retailers based on factors like product aesthetics, pricing tiers, and target demographics. With AI-powered matchmaking in fashion, the system uses historical data and brand insights to suggest the most compatible retail partners. This helps in connecting fashion brands with retailers that are more likely to align with the brand’s positioning and increase sales opportunities.

How can I automate the process of profiling fashion brands?

You can automate fashion brand profiling using a dedicated Fashion Brand Matchmaking Platform that combines web scraping, AI, and fashion business automation. The platform gathers brand data—location, product type, audience, etc.—and builds searchable, editable profiles in real time. This solution removes reliance on spreadsheets and manual research, supporting scalability and precision in fashion brand management.

Can AI help in finding fashion retail partners?

Absolutely. AI identifies and connects fashion brands with suitable retail partners by leveraging customer behavior data, market trends, and compatibility signals. Through automated fashion brand matching with wholesalers, it enables quicker, smarter wholesale matchmaking in fashion—supporting growth for both emerging and established brands. This removes the manual friction in connecting fashion brands with retailers.

How is AI used in fashion brand management?

AI plays a pivotal role in modern fashion brand management by automating brand profiling, improving classification with NLP, and matching brands with ideal retail and wholesale partners. It streamlines workflows, improves decision-making, and reduces operational costs through fashion business automation. This empowers branding agencies and fashion brands to scale their operations and make data-driven choices.

What are the benefits of AI-powered matchmaking in fashion?

AI-powered matchmaking in fashion significantly boosts operational efficiency by reducing manual work and enhancing the accuracy of brand-wholesaler connections. It allows faster brand discovery, supports fashion brand growth challenges, and enables precise AI for ideal customer profiling. As a result, brands gain access to new markets and wholesalers benefit from relevant, data-backed recommendations.

How does AI improve the speed and precision of B2B fashion connections?

AI enhances B2B fashion connections by analyzing large volumes of brand and market data in real time. With intelligent brand-wholesaler matching, brands can be profiled in just minutes, and the platform delivers highly relevant matches with 75%+ accuracy. This speed and precision help brands scale faster while solving traditional challenges in wholesale matchmaking in fashion.

How do we convert Excel to a database for Brand Matchmaking?

Start with a data audit. List current sheets and fields. Define a clean schema for brands and partners. Set standard tags for category, target group, region, and price band. Create dedupe rules for names and sites. Migrate data in batches. Use a staging area to fix errors before loading into the main database. Add a simple operator console for search and edits. Turn on role based access and an audit trail. Link a scraping pipeline to refresh profiles on a schedule. Add KPI tables for match metrics. When this is live, you replace spreadsheet risk with one source of truth. This step is key to Brand Matchmaking at scale and reduces rework across teams.

How does the FN-AD platform connect fashion brands with wholesalers, retailers, and distributors?

The system keeps two sides. A brand panel holds profiles with tags and images. A wholesaler and retailer panel holds focus areas and past results. The ai matching engine compares both sides and produces ranked matches. Teams can filter by region, price band, or category. For retailers, a reverse matching flow collects needs and then recommends brands to retailers. For distributors, profiles include region rights and logistics. The platform also helps connect retailers with brands through CRM and email tools. This creates a repeatable flow to connect brands with wholesalers and to connect fashion brands with distributor networks.