Skilled AI Developer for Consistly.ai Automation

@rajat07me

The real challenge here is not generating AI content pieces individually, but making the whole chain from strategy to LinkedIn publishing reliable under real user activity, retries, failures, and usage limits. For Consistly, I’d start with an audit of the current workflow map, queue logic, API dependencies, and failure points first, then tighten the weak parts instead of rebuilding what already works. I’ve worked on AI automation systems that combine LLM prompts, user-specific outputs, webhook-driven actions, billing controls, and production logic around background jobs. This kind of SaaS flow usually breaks at handoff points like OAuth refresh, async jobs, prompt consistency, or Stripe gating, so that is where I’d focus first to make it stable and scalable. A small audit report with workflow gaps and priority fixes can be the first deliverable so you get clarity before deeper implementation. - Is the current stack mainly Node/TypeScript, or is part of the automation already running in n8n/Make? - Which workflow is currently the least reliable: publishing, voice matching, scheduling, or Stripe usage enforcement? - Do you want the bid to cover the audit first, or audit plus implementation in one phase?

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in API Integration, PHP, Website Design, HTML, Node.js, Python, WordPress As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@ost

As an AI and Full-Stack developer, I have a robust 10+ year track record tailoring reliable, scalable, and cost-effective solutions for various clients. My specialization in Node.js, Python AI integration, API development, and automation workflows (such as n8n) aligns perfectly with the needs of your project. In fact, in my previous project at Gemini, I successfully integrated OpenAI, Claude and other language models for advanced text processing and content generation tasks. Moreover, my extensive experience with LinkedIn API/OAuth and similar social media automation projects make me uniquely qualified to fine-tune the existing layouts in Consistly.ai. I have successfully collaborated with projects using Stripe for payment functions and possess strong skills in relational databases like PostgreSQL that are vital for the back-end structure of your platform.

@ranjika08

Your platform already has the core concept in place, so the important part now is making the automation dependable from one stage to the next instead of starting over. I would review the current codebase and workflow path first, identify bottlenecks in generation, approvals, publishing, and background processing, and then complete the missing pieces in a production-safe way. I’ve handled API-heavy systems where AI outputs, webhooks, subscriptions, and scheduled actions all need to stay in sync. For a tool like Consistly, things like LinkedIn OAuth expiry, job retries, queue failures, and per-user usage enforcement matter just as much as the content generation itself, because those are usually what affect user trust. I can also help structure the work into an audit-first phase so priorities become clear before development expands. - Is there already a queue system in place for scheduling/publishing, or does that still need to be standardized? - Are weekly insights and engagement recommendations already partially implemented, or still at planning stage? - Would you like the first pass focused on stability only, or stability plus scale readiness?

@denisSolution

Hi, I'm Denis, a developer who has built and maintained AI automation workflows for SaaS platforms similar to Consistly.ai. I understand you need a reliable, scalable automation pipeline for content generation, voice matching, scheduling, and publishing on LinkedIn. The core challenge is making these workflows resilient to errors, API limits, and user-specific constraints while ensuring smooth data flow from content strategy to publishing. I recently worked on a system where LLM APIs, background jobs, and third-party integrations needed careful orchestration to prevent bottlenecks. The approach involved auditing the existing flow, identifying weak points in retries and error handling, then tightening the integration layers with proper rate limiting, webhooks, and fallback logic. One common risk here is LinkedIn API throttling or OAuth token expiration. I’d add automated retries with exponential backoff and a refresh mechanism, along with monitoring for failed jobs. For Stripe limits, I’d implement usage tracking at the user level with clear error messages when thresholds are hit. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@krupnovdenis

Hello! We can audit your existing platform and complete the automation workflows you need. 1. What is the first workflow you want us to stabilize? 2. Do you already have access to the current codebase and integrations? — About us We are dZENcode – a full-cycle IT company for digital product development: from design and programming to integrations and post-release support. We build projects from scratch and also work on existing solutions that need further development, improvements, or technical support. You can find detailed information about our services and rates on our official website: https://dzencode.com. Please review it – after that, we can discuss the details and agree on the next step. ⚠️ After clarifying all details, we will define the scope, the suitable cooperation format – task-based, outsourcing, or outstaffing – and the final cost. Projects are guaranteed to reach release with us: • 10+ years providing IT services; • 90+ in-house specialists; • 250+ public reviews since 2015; • We support products under SLA after launch; • We work under NDA and a company contract!

@Tanmoy236

Hello! I'm an experienced AI automation and full-stack developer with deep expertise in building reliable, scalable SaaS workflows. I've worked on similar AI-powered content platforms involving LLM APIs, LinkedIn integration, and automated publishing systems. Here's how I can help: - Audit your existing automation flow to identify gaps, bottlenecks, and reliability issues - Complete and improve key workflows: content strategy, AI post generation, voice matching, and image creation - Implement content approval, scheduling, and LinkedIn OAuth/publishing - Set up background jobs, retries, and error handling for production stability - Integrate Stripe for subscription-based automation and usage limits - Add email/in-app notifications and weekly performance insights I'm comfortable working with existing codebases and making them production-ready. I have experience with Node.js, Python, OpenAI/Gemini APIs, and n8n workflows. Let's make Consistly's automation reliable and scalable.

@temoberishvili

Hello! I see you're looking for an AI Automation Workflow Developer for Consistly.ai. Your goal of optimizing workflows using AI is right up my alley. With over 10 years of experience in PHP, Python, and API integration, I can help design and implement efficient automation solutions that meet your needs. Let's chat about how we can make this project a success!

@shayankhan333

LinkedIn OAuth tokens expiring mid-schedule is the usual reason "publishing works in test but fails in production", so the audit starts with token refresh, queue retries and Stripe usage gates before touching the AI layer. I'll harden the workflow: OpenAI voice-matching with cached embeddings, BullMQ-backed scheduling with idempotent retries, and LinkedIn publish with proper error surfacing. Available to start straight away. 1) Is the current stack Node/TypeScript with Postgres, and is n8n already in play or all custom? 2) Which step fails most often now, generation, scheduling, or publishing? Looking forward to it. Shayan

@basit20ali

Consistly isn't just missing a few automation steps. It's an end-to-end content pipeline where each stage feeds the next, so a weak link anywhere quietly breaks everything downstream from it. Before the audit starts, one thing matters more than any other: when the pipeline fails partway through a run, does the system know exactly where it stopped and pick back up cleanly, or does it silently drop the user's content? This matters because a broken retry point doesn't just cause one failed post. It erodes trust in the whole platform, since users won't know their content is missing until they check LinkedIn themselves. Are you available for a brief 15-minute diagnostic chat today or tomorrow to walk through this?

@mughiraaa

Hello, I have already completed similar AI automation and SaaS projects involving LLM APIs, LinkedIn integrations, webhooks, background jobs, Stripe, and PostgreSQL. I have strong experience in web development, APIs, and scalable software solutions, so I can quickly understand the existing Consistly codebase and focus on completing what is actually needed. I can first audit the current workflows, then fix reliability issues across content generation, voice matching, scheduling, LinkedIn publishing, notifications, retries, analytics, and usage limits without disrupting the existing platform. Which workflow is currently causing the most failures or manual intervention? Happy to review the codebase and discuss the priorities in a quick meeting. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@ffulb

Most automation chains like this break at voice matching because there's no consistent fingerprint to train against, LinkedIn posts are too short and varied, and the model drifts every time a user edits something before publishing. I'd run a hybrid approach: extract stylistic markers during onboarding (sentence length distribution, punctuation density, formality score), store those as constraints in Postgres, then use them as system prompts during generation instead of trying to fine-tune. Keeps it deterministic without needing huge training datasets per user. I built an AI travel planner with multi-step Claude workflows and a full invoicing platform with 81-table Postgres and background job orchestration, both at ffulb.com. I'd wire this with n8n for the scheduling/retry logic, Node for the API layer, and Supabase Edge Functions if you want the job queue isolated from the main app. Once I get access to the existing codebase, database, and your AI/LinkedIn API keys I can audit what's already there and patch the weak points without rebuilding from scratch.

@AndrewJSMAN

I’ve built and scaled AI automation workflows for SaaS platforms using OpenAI/Gemini APIs, n8n workflows, and Node.js/Python backends with PostgreSQL and Stripe—so this is direct domain experience. I’ll audit the current flow for reliability gaps (retries, rate limits, OAuth session handling), then extend the existing automation chain with validated modules for voice matching (preferably ElevenLabs/TTS API), carousel generation (DALL·E), and LinkedIn publishing safeguards. The core stack (Node.js, n8n, PostgreSQL, Stripe) is already present, so I’ll integrate missing pieces with minimal refactor—focusing on idempotency, background jobs via BullMQ, and webhook-driven notifications. Hidden concern: OAuth token rotation for LinkedIn publishing to prevent downtime. I can start immediately. Thanks, Andrii

@rosscarter1011

Hey, Consistly is already built, so I would focus on tracing the existing automation chain from content strategy through LinkedIn publishing and analytics rather than rebuilding the platform. I have hands on experience with Python automation, AI projects, SaaS development, API integrations, and dashboards, including AI visual inspection and crypto sentiment systems with reproducible Docker based workflows. I would first map the existing jobs, webhooks, API calls, retries, and usage limits, then fix the points causing unreliable execution or duplicated work. LinkedIn OAuth, background processing, Stripe limits, and LLM calls should be treated as separate failure boundaries so one failed step does not break the complete content pipeline. Can you share the current repository and automation setup so I can identify the first workflow that needs stabilising?

@rofeald

Hello, With 9 years of experience in PHP, Python, WordPress, HTML, Node.js, and API integration, I am well-equipped to handle the complexity of the AI Automation Workflow Developer role for Consistly.ai. I understand the need to audit and enhance the existing automation workflows to ensure reliability, scalability, and production readiness. I am confident in my ability to optimize the key workflows, such as AI-powered content generation, voice matching, content scheduling, LinkedIn publishing, and engagement recommendations. I am keen to discuss your project further and collaborate on a professional solution tailored to your requirements. I would like to connect with you in chat to delve deeper into the specifics of the project and share my insights on how to streamline and enhance the automation processes for Consistly.ai. Best regards

@jaroslavcaprata

The main risk here is not generating AI content—it is making the full automation chain reliable when one external dependency fails, a job retries, a subscription limit changes, or LinkedIn publishing is delayed. I would start by auditing the existing codebase and mapping the full workflow from profile → strategy → weekly ideas → generation → approval → scheduling → publishing → analytics. From there, I would identify where state is currently fragile, which steps need queues/retries, and where duplicate execution or partial failures can occur. For production reliability, I would use explicit workflow states, idempotent jobs, retry/backoff rules, queue-based background processing, structured error logging, and usage checks tied to Stripe plans. LinkedIn OAuth/publishing would be isolated from AI generation so a publishing failure never forces content regeneration. For AI output quality, I would keep strategy, voice profile, generation, and engagement recommendations as separate structured stages rather than one oversized prompt. I would preserve the existing stack where practical and improve it incrementally rather than rebuild stable parts. Can you share the current architecture and clarify which parts are already working versus incomplete or unreliable?

@emrahg7

Hi, Before I touch any code, I'd want to get into the actual automation layer and see how the pieces talk to each other right now: is it n8n/Make, custom queue workers, or a mix? I'd also want to see how LinkedIn OAuth tokens are stored and refreshed, and how retries and failures are handled today, since that's usually where things quietly break in production. My guess is the AI generation and scheduling steps mostly work in isolation, but the error handling, retry logic, and usage limits tied to Stripe are thin or missing, that's the pattern I've seen on every platform like this. Once I've got that picture, I can map out exactly what's solid, what needs rework, and what's missing, then start closing gaps starting with the flakiest part of the chain. I've built this kind of pipeline before, content generation into scheduling into publishing, so I know where queues silently drop jobs if you're not careful. I can have a full audit and fix plan back to you fast, and the full build stabilized in about 2 days. Are the LinkedIn publishing and Stripe integrations already live and working, or still partial? That changes how much of this is fixing versus finishing. Best, Emrah

@samyak8840

Hi, I am Samyak. I having 5.2 years of experience in AI Industory. I worked on creating workflows using N8N, Python , Zapier and build up the application using FastAPI. I had read the description. I will create the workflow. Will share my resume for confidence in assigning project. We can connect on chatbox. Thanks

@adriank06

Hello, The most important reliability risk in this workflow is not the AI prompt itself—it is ensuring that retries never publish the same LinkedIn post twice, consume usage twice, or leave a user’s content in an inconsistent state. I would treat the automation as a persistent, idempotent workflow with explicit states, retry policies, and auditability. I have reviewed Consistly’s public product flow and will assess the existing implementation rather than proposing an unnecessary rebuild. For an initial fixed milestone of €250, delivered within five calendar days, I will: * Run the existing application and map its services, database, integrations, and automation workflows * Trace one complete journey from user profile through strategy, generation, approval, scheduling, and publishing * Identify incomplete, unreliable, duplicated, or mocked functionality * Review LLM prompts, voice-matching context, usage tracking, and failure handling * Audit background jobs, retries, timeouts, idempotency, and duplicate-publishing protection * Review LinkedIn OAuth, token storage, expiry/revocation handling, publishing scopes, and available analytics permissions * Review Stripe webhooks, subscription entitlements, and usage-limit enforcement * Check notification workflows, logging, monitoring, and security-sensitive configuration * Provide a prioritized gap report and production-readiness roadmap * Deliver a reliable implementation estimate divided into clear milestones * Apply one contained critical fix during the audit if it fits safely within the milestone My relevant background includes Python/FastAPI services, PostgreSQL, authentication, third-party APIs, asynchronous background processing, rate-limit handling, automated tests, Docker, CI, structured logging, and defensive failure recovery. A critical prerequisite is the current approval status of the LinkedIn application. Publishing and retrieving engagement analytics require different permissions, and some member-level read access is restricted. I will verify what the approved API can support and will not replace missing permissions with fragile scraping or browser automation. Before starting, please provide: 1. The repository and local setup instructions 2. A list of workflows currently working, incomplete, or failing 3. The current frontend, backend, queue, and deployment stack 4. LinkedIn application products and approved OAuth scopes 5. Stripe test-mode access and subscription structure 6. A staging environment with sanitized credentials 7. Existing tests, logs, and known production issues After the audit, you will have a clear technical diagnosis, prioritized remediation plan, and realistic price and schedule for completing the production workflows. Best regards