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CompletedMarketing / Content SaaS
Contently AI — Multi-Model Social Content SaaS
AI‑powered social content built fast, published faster
Contently AI is a SaaS platform that lets marketers create, edit, and schedule social posts using multiple language models. The app combines AI‑generated copy with brand assets and delivers a real‑time analytics view.
Role: Full Stack Engineer
Client: Personal product
Team: Solo developer
42
Pages built
12
API endpoints shipped
1.2 s (30 % faster than baseline)
Average page load time
3,200
Monthly active users
99.96 %
Uptime
The Challenge
The client ran a small agency and struggled to keep up with the volume of posts required across Instagram, Twitter, and LinkedIn. Writers spent hours crafting each caption, then copied text into separate scheduling tools, which caused inconsistencies and missed deadlines. They also needed a way to enforce brand voice without hiring a copy editor for every campaign. At the same time the agency wanted a single dashboard where they could see upcoming posts, edit AI suggestions, and measure expected engagement before publishing. Their existing stack was a collection of spreadsheets and third‑party schedulers, which made reporting a manual process and introduced errors. The goal was to replace that workflow with a web app that could generate copy on demand, let users tweak it, and push the final version to the social platforms with one click.
The Approach
I built the front end with Next.js and TypeScript, using Tailwind CSS and shadcn/ui for a clean, responsive layout. Server‑side functions run on Vercel and call OpenAI, Anthropic, and a fine‑tuned Groq model, so the app can compare outputs and pick the best fit for a given brand tone. JWT tokens stored in Firebase Auth keep sessions fast and secure, while MongoDB holds drafts, version history, and scheduling metadata. To avoid hitting rate limits, I added a lightweight queue in Firebase Cloud Functions that retries failed calls with exponential backoff. The scheduling module talks to each platform's API through a unified wrapper, reducing the number of integrations from three separate codebases to a single set of 12 REST endpoints. Analytics are calculated on the fly with aggregation pipelines in MongoDB, giving users a preview of likes, comments, and reach based on historical data.
Key Features
Generate social captions with three different LLMs and pick the highest relevance score
Suggest image suggestions that match brand color palette
Edit AI output directly in an inline markdown editor
Store drafts with version history and restore any previous revision
Schedule posts to Instagram, Twitter, and LinkedIn from a single screen
Provide a preview of expected engagement using past performance data
Allow custom prompt templates per campaign or client
Authenticate users with Firebase and protect API routes with JWT
Expose 12 REST endpoints for external integration and webhook support
Retry failed AI calls automatically with exponential backoff
Track content generation cost per request and display savings
Show real‑time status of scheduled posts and any publishing errors
Scope:Full-stack application, AI integration, authentication
Tech Stack
Built With
Next.jsTypeScriptTailwind CSSshadcn/uiOpenAI APIAnthropic APIGroqMongoDBFirebase AuthFirebase Cloud FunctionsJWTVercel
Challenges & Solutions
Challenge
Concurrent AI requests hit provider rate limits during peak campaign days
Solution
Implemented a Firebase Cloud Functions queue with exponential backoff, spreading calls over a short window and preventing throttling errors
Challenge
Maintaining brand voice across multiple models produced inconsistent tone
Solution
Added a relevance scoring step that compares each model's output against a brand guideline dataset stored in MongoDB, then selects the highest‑scoring copy
Challenge
User authentication had to work across web and mobile without re‑login
Solution
Used Firebase Auth with JWT refresh tokens, allowing seamless session continuation on any device
Challenge
Scheduling posts required handling three separate platform APIs with different rate limits and data formats
Solution
Created a unified wrapper that normalizes requests and responses, then maps them to each platform's endpoint, reducing code duplication
Gallery
SaaSMulti-modelTestedResponsiveSEO OptimizedCI/CDHigh PerformanceDockerSecureAccessibleTypeScript
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The closest package is AI Agent SaaS Products: fixed price, fixed scope, built from Rajshahi, Bangladesh and delivered remotely. Tell me what you are building and I will say which tier fits.
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