RecoMate
v1.0Live

RecoMate

Plug-in AI product recommendations for e-commerce — live in minutes, no ML expertise required.

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Pitch Deck1 / 21

The Problem

E-commerce businesses lose 30–40% of potential revenue to poor product discovery.

  • ✕Shoppers abandon carts when they can't find relevant alternatives or complements.
  • ✕Generic rule-based "customers also bought" systems don't understand product semantics.
  • ✕Small to mid-size stores lack the ML expertise to build personalized recommendation engines.
  • ✕Integrating recommendation systems requires months of engineering work and significant infrastructure cost.
79% of consumers say they're more likely to buy from a brand that delivers personalized experiences — yet only 15% of SME e-commerce stores have any recommendation system at all.
Pitch Deck2 / 21

Our Solution

RecoMate — Plug-in AI product recommendations, live in minutes.

RecoMate is a SaaS API platform that lets any e-commerce store add intelligent, semantic product recommendations without writing a single line of ML code. Sync your product catalog via our REST API, and instantly serve AI-powered "similar products" and "you might also like" recommendations that understand what products mean — not just their names.

  • ✓Semantic embeddings understand product context and intent
  • ✓Multi-tenant SaaS — one integration serves unlimited stores
  • ✓REST API with a single endpoint call to get recommendations
  • ✓No ML expertise required — works out of the box
Pitch Deck3 / 21

Why Now

The AI infrastructure moment is here.

  • →Vector databases and embedding APIs have dropped in cost by 10× in 24 months.
  • →Open-source LLM-based embedding models now rival proprietary systems at near-zero cost.
  • →E-commerce adoption accelerated post-pandemic — global market is $6.3T and growing at 9% YoY.
  • →API-first SaaS is the dominant delivery model for ML capabilities — the market is trained and ready.
  • →No dominant player has captured the SME recommendation API market.
Pitch Deck4 / 21

Product

RecoMate works in 3 steps.

1

1. Sync

POST your product catalog to our /products/bulk-sync endpoint. Each product gets embedded into a semantic vector space.

2

2. Calibrate

Our recalibration engine builds a nearest-neighbor index across your catalog for ultra-fast similarity lookup.

3

3. Recommend

Call GET /recommendations?product_id=X with your API key. Get back ranked similar products in <100ms.

Dashboard includes real-time job tracking, usage analytics, plan management, and API key control.

Pitch Deck5 / 21

Market Opportunity

TAM

$4.2B — Total addressable market for e-commerce personalization software globally (2025).

SAM

$800M — Serviceable addressable market: API-first recommendation platforms targeting SME e-commerce.

SOM

$12M — Year 3 target: 400 paying tenants at an average ARR of $1,200 on higher-tier plans, capturing 1.5% of SAM.

E-commerce personalization software market growing at 12.4% CAGR through 2030.

Pitch Deck6 / 21

Business Model

Subscription SaaS with usage-based overage (PAYG)

PlanPriceRecommendationsProductsAPI Keys
Starter$0/mo100 recommendations/mo100 products1
Growth$29/mo5,000 recommendations/mo1,000 products5
Pro$99/mo50,000 recommendations/mo10,000 products20
EnterpriseCustomUnlimitedUnlimitedUnlimited

Overage billed at $0.001 per additional recommendation on Growth and Pro plans.

Pitch Deck7 / 21

Traction

Built, shipped, and validated.

Live API

Fully operational

Core features

100% complete

Average recommendation latency

<100ms

Embedding pipeline

Async BullMQ, Redis-backed

Architecture

Multi-tenant, production-ready

Built during AI hackathon. Full SaaS stack: Auth, Multi-tenancy, Billing, Jobs, WebSockets, AI embeddings.

Pitch Deck8 / 21

Competition

Barilliance
✕ Enterprise-only, $2k+/mo, complex setup
Recombee
✕ Complex ML config required, not plug-and-play
Algolia Recommend
✕ Tied to Algolia search stack, expensive
Custom ML pipeline
✕ Months of engineering, high ongoing cost

Our Advantage

RecoMate is the only semantic-first, API-native recommendation engine purpose-built for SME e-commerce — zero ML expertise required, live in minutes.

Pitch Deck9 / 21

Unique Advantage

Semantic Understanding

Vector embeddings capture product meaning, not just keyword overlap. A blue denim jacket correctly surfaces similar items even without matching keywords.

Zero-Config AI

No model training, no parameter tuning. Sync your catalog and recommendations work immediately.

Multi-Tenant Architecture

Full tenant isolation with per-tenant API keys, usage quotas, and billing — ready for scale from day one.

Real-Time Job System

BullMQ-powered async embedding pipeline with WebSocket progress tracking — reliable at any catalog size.

Pitch Deck10 / 21

Go-To-Market

Phase 1 — Developer-Led (0–6 months)

  • · Free tier to drive sign-ups and word-of-mouth
  • · Developer documentation and quick-start guides
  • · NPM SDK
  • · WordPress Plugin
  • · Laravel Package
  • · Product Hunt launch

Phase 2 — Content & SEO (6–18 months)

  • · E-commerce optimization blog content
  • · Integration guides for WooCommerce, Magento, BigCommerce
  • · Partner program with e-commerce agencies

Phase 3 — Sales-Assisted (18+ months)

  • · SDR team targeting mid-market e-commerce (>$10K GMV)
  • · Enterprise contracts with custom SLAs
  • · White-label offering for platform partners
Pitch Deck11 / 21

Team

Mahmudun Nabi Kajal

Mahmudun Nabi Kajal

Team Leader / Project Coordinator

mahmudunnabikajal@gmail.com
Md. Mehedi Hasan Nayeem

Md. Mehedi Hasan Nayeem

Business Analyst / Data Scientist

nayeem@recomate.com
RT

Rubiat Tahsin

Presentation / Communication Lead

rubiat@recomate.com
Pitch Deck12 / 21

Vision

Every e-commerce product, perfectly matched to every shopper.

We're starting with product-to-product recommendations. Our vision is a universal recommendation layer that works across all verticals — content, media, marketplaces, and beyond — powered by multimodal AI that understands text, images, and behavior simultaneously.

Q3 2026 — Image-aware embeddings (visual similarity)
Q4 2026 — Behavioral signals integration (click/purchase history)
Q1 2027 — Cross-tenant collaborative filtering
Q2 2027 — Real-time personalization engine
Technical Docs13 / 21

Architecture

RecoMate is built on a clean 3-tier multi-tenant architecture.

Browser / Client

panel.recomate.com

API Layer

api.recomate.com — Express + Socket.IO

AI Service

ai.recomate.com — FastAPI + Embeddings

Job Queue

BullMQ + Redis

Data Layer

MySQL (Prisma) + Redis Cache

Frontend (panel.recomate.com)

Next.js 16, React 19, Tailwind CSS 4, Zustand, TanStack Query

Dashboard UI for tenant management, product sync, job tracking, billing, and API key management.

API (api.recomate.com)

Express.js, TypeScript, Prisma ORM, JWT Auth, BullMQ, Socket.IO

REST API handling all business logic, multi-tenant data isolation, async job orchestration, and WebSocket events.

AI Service (ai.recomate.com)

Python/FastAPI, Sentence Transformers, Qdrant

Embedding generation and nearest-neighbor similarity search for product recommendations.

Data Layer

MySQL (Prisma), Redis (BullMQ queues + cache)

Persistent storage with full tenant isolation, async job queuing, and caching.

Technical Docs14 / 21

Data Flow

Product Sync Flow

1

Tenant POSTs product catalog via /products/bulk-sync

2

API validates, stores products in MySQL (tenant-isolated)

3

BullMQ enqueues embedding job

4

AI service generates vector embeddings per product

5

Embeddings stored and indexed in Qdrant vector store

6

WebSocket event notifies frontend of completion

Recommendation Flow

1

Client calls GET /recommendations?product_id=X with API key

2

API authenticates key and resolves tenant context

3

AI service performs ANN (approximate nearest neighbor) search in Qdrant

4

Top-K similar products returned with similarity scores

5

Result logged to recommendation_logs for analytics

Technical Docs15 / 21

Technology Stack

Frontend

Next.js 16 (App Router)React 19TypeScriptTailwind CSS 4ZustandTanStack Query v5Socket.IO Client

Backend

Node.js 24Express.jsTypeScriptPrisma 7 (ORM)BullMQ (job queues)Socket.IO (WebSockets)JWT (auth)Helmet + CORS

AI / ML

PythonFastAPISentence TransformersQdrant (vector DB)Cosine similarity ANN

Data

MySQLRedisPrisma Migrations

Infrastructure

Docker (containerized)Environment-based configRate limitingCORS protection
Technical Docs16 / 21

API Reference

Base URL: https://api.recomate.com/v1

Authentication: Pass your API key in the header: `x-api-key: rm_live_••••••••`. Generate keys from the RecoMate dashboard.

POST/catalog/syncAPI Key
Bulk-upsert your product catalog. Triggers embedding generation in the background. Accepts an array of product objects.
PUT/catalog/:productIdAPI Key
Update a single product. Triggers re-embedding for that item only.
DELETE/catalog/:productIdAPI Key
Remove a product from your catalog and vector index.
GET/catalogAPI Key
List all products currently indexed for your account, with sync status.
GET/recommendationsAPI Key
Get semantically similar products for a given product_id. Query params: product_id (required), limit (default 10), min_score (0–1).
POST/recommendations/batchAPI Key
Get recommendations for multiple product IDs in a single call. Useful for "Complete the look" widgets.
POST/eventsAPI Key
Record a user interaction event (view, click, add_to_cart, purchase). Used to improve ranking over time.
GET/jobs/:jobIdAPI Key
Poll the status of a background sync or recalibration job by its ID.
Technical Docs17 / 21

Security

Authentication

JWT-based session auth for dashboard users; API key auth for recommendation API consumers

Tenant Isolation

All queries are scoped to tenantId — cross-tenant data access is architecturally impossible

API Keys

Keys are hashed/stored securely, revocable instantly, scoped to tenant

Rate Limiting

Express-rate-limit at 100 req/15min globally; per-plan recommendation quotas

HTTP Security

Helmet.js enforces security headers (CSP, HSTS, XSS protection, etc.)

Password Handling

bcryptjs hashing with appropriate cost factor — never stored in plaintext

CORS

Configurable CORS policy — restrict to known origins in production

Input Validation

Zod schema validation on all API inputs

Technical Docs18 / 21

Performance & Scalability

Recommendation latency

<100ms p95

Embedding throughput

~500 products/min per worker

API throughput

1,000+ req/s (horizontal scale)

  • → BullMQ workers are horizontally scalable — add workers to increase embedding throughput
  • → Redis caching for hot recommendation results reduces AI service load
  • → Prisma connection pooling for efficient MySQL utilization
  • → Stateless API design enables auto-scaling behind a load balancer
  • → Qdrant in-memory index enables sub-millisecond ANN search at 100K+ product scale
Technical Docs19 / 21

Analytics & KPIs

Total recommendations served

Aggregate across all tenants

Recommendations per tenant/day

Usage tracking for billing and quotas

Embedding job success rate

Pipeline reliability metric

Average recommendation latency

SLA tracking

Product catalog size

Proxy for customer engagement

Plan conversion rate

Free → paid upgrade funnel

Planned KPIs

Click-through rate on served recommendationsRevenue lift attributionCohort retention by plan tier
Technical Docs20 / 21

Roadmap

Short Term (0–3 months)

  • · Image-aware embeddings for visual similarity
  • · Shopify plugin (OAuth + auto-sync)
  • · Recommendation widget (embeddable JS snippet)
  • · Advanced analytics dashboard

Mid Term (3–9 months)

  • · Behavioral signals (click/add-to-cart/purchase history)
  • · A/B testing framework for recommendation strategies
  • · WooCommerce + Magento integrations
  • · Webhook support for real-time events

Long Term (9–18 months)

  • · Cross-tenant collaborative filtering
  • · Multimodal AI (text + image + behavior)
  • · Real-time personalization engine
  • · White-label enterprise offering
Technical Docs21 / 21

Changelog

v1.0.0June 2026
  • + Initial production release
  • + Multi-tenant SaaS platform
  • + Semantic embedding pipeline (BullMQ + AI service)
  • + REST API with JWT + API key auth
  • + Subscription billing with plan limits
  • + Real-time job tracking via WebSockets
  • + Dashboard UI with analytics
  • + Live /docs module with admin access control