AI SaaS Built from POC to Investor Ready with a Full Vibe Coding Cleanup

Industry

Media, Entertainment & Sports

Geography

Washington, U.S.

Cooperation Period

8 months

Summary

The cofounders built the POC with Clixlogix, validated the token unit economics, then rebuilt the platform inside an AI coding tool. When that build hit a ceiling, Clixlogix came back to rescue it.

9 signed scopes across 2 phases. Cost per cycle engineered from about $1.72 to about $0.20. POC and economics validated on a lean n8n POC. Around 90 percent of catalogued issues cleared. 6 week active remediation window inside the rescue phase.

About the Client

The Client is a two-founder SaaS startup operating across North America and the Nordics, with experience in entrepreneurship, community ventures, and enterprise software leadership. The startup operates a small portfolio of AI products across content safety, context aware multimodal moderation and social tooling, each product built around the same moderation core.

The flagship product, the subject of this engagement, screens user content across text, image, video and audio before it publishes to connected social accounts. The product generates content, checks posts against brand and policy documents, and meters usage through tiered subscription billing. The Client sells into teams that carry reputational and compliance risk on everything they publish, so moderation accuracy across language, region and tone, plus a predictable cost per cycle, decide whether the product earns its place.

The cofounders ran lean. They needed a dedicated expert team and a fast cadence, which shaped how the engagement ran and forced the build economics into focus from day one.

Challenge

The Client engaged Clixlogix to build an AI content moderation MVP and carry it to a market ready product. Four commercial pressures shaped the engagement.

The product needed to work across languages, tone and cultural context before it could sell. The flagship moderation product targeted teams carrying reputational and compliance risk. Keyword detection alone could not handle sarcasm, code switching, regional slang, religious insults masked in nuance, or gibberish content that carried no traditional toxic keywords. The product had no credible path to market until those gaps closed with a moderation architecture that read cultural and situational meaning.

The cost per cycle had no defensible floor. Early estimates put a single moderation cycle around $1.72, driven by LLM token cost and per transaction payment processing fees. At that level, a tiered token subscription model could not carry a free tier or compete against incumbents on price. The cofounders needed cost engineering before they could price the product.

The cofounders needed to validate the POC and unit economics with investors before committing to a full build. They needed a working POC that proved the moderation capability and the unit economics, without burning their runway on production infrastructure. A lean architecture that demonstrated the product’s commercial viability carried more value at that stage than a production grade system.

A mid engagement rebuild in an AI coding tool left a downstream customer contract exposed. After Phase 1 delivered the POC and user portal, the cofounders rebuilt the platform inside Emergent, an AI coding tool that accelerated feature work through generated code. The generated codebase hit a ceiling with loose structure, broken persistence, billing logic disconnected from usage tracking, and an embedded tool dependency that blocked deployment on standard infrastructure. The cofounders carried a downstream customer under contract and absorbed cost every day the build sat unfinished, with a user acceptance window approaching and runway burning on a codebase that could not reach production.

Solution

Clixlogix delivered the engagement across 2 phases. Phase 1 built the moderation POC, validated the economics, designed the moderation architecture, and delivered the user portal through Milestone 1 across 5 signed scopes and 2 projects. The AI stack evolved across phases: Hive AI for classifier testing, Claude Haiku as the low cost LLM fallback, Google Cloud Vision and Whisper for image and audio moderation, and GPT-4o for the multilingual agentic architecture in Phase 1. Phase 2 introduced Google Gemini Vision for image and video moderation and retained OpenAI APIs for text. Phase 2 rescued the cofounders’ self built version after they rebuilt the platform inside Emergent, an AI coding tool, governed the ongoing Emergent output through an agentic audit and deployment pipeline, and carried the build to a UAT ready state in a 6 week active remediation window during the rescue phase.

Phase 1. Discovery, POC and web application build

Discovery and the n8n POC

Clixlogix ran a paid discovery to validate the product against the market and to map a defensible cost model before the cofounders committed budget to a full build. The team tested the leading commercial moderation classifier, Hive AI, across 8 content scenarios that mattered to the Client’s target users, including multilingual posts, gibberish, sarcasm, fake health claims, religious and political extremism, code switching and slang, and live samples drawn from existing social channels. Hive handled overt hate speech, nudity, violence and illegal content reliably across text, image and video. It missed sarcasm, gibberish, code switched content, religious insults masked in nuance, and regional slurs that carried no traditional toxic keywords.

That capability map drove the moderation architecture. The team designed a 3 stage flow with rule based filters and keyword dictionaries handling regional edge cases first, Hive carrying the volume of explicit content classification second, and a large language model fallback closing the gaps Hive missed. The team evaluated Claude Haiku against GPT-3.5 Turbo for the fallback, chose Claude Haiku for its lower token cost at adequate reasoning quality, and kept the system modular so the model could swap as the market shifted.

The discovery also fixed the product’s unit economics. The team built the POC on n8n as a deliberate lean architecture choice, with Telegram and SMS as the user facing channels through Twilio. That setup let the cofounders demonstrate working moderation capability and defensible unit economics to investors without committing to any heavy build spend. Moving the n8n automation off the standard cloud plan onto dedicated VPS hosting lifted a 5 workflow ceiling, unlocked unlimited workflows, and engineered cost per cycle down from about $1.72 to about $0.20. The team then mapped a tiered token model from a free tier through enterprise, anchored on Claude Haiku token pricing, with response size caps and guardrails that kept the cost defensible at every tier.

Fig 1 – Per Cycle Cost Engineering

Consulting Insight

Engineer the unit economics before scaling. A metered product lives or dies on cost per cycle, and the tiered token model only holds if the floor is engineered down before volume arrives.

Clixlogix documented the full prompt architecture and shared it with the cofounders so the moderation logic remained transparent and auditable as the system grew. The modular architecture proved its value early. When OpenAI shut down the text-moderation-007 endpoint in October 2025, the product absorbed the change with zero disruption because the team had already designed the moderation stack to swap models and endpoints without touching the rest of the system.

Architecture and context aware moderation

The 3 stage moderation flow extended across modalities. Clixlogix added image, video and audio moderation using Google Cloud Vision and Whisper, and built context aware multimodal moderation so the system reads cultural and situational meaning that keyword detection misses. A document compliance flow ran uploaded brand and policy files through a chunking pipeline into a MongoDB vector store, then retrieved the relevant clauses through a RAG pipeline to check user posts against the Client’s contract terms before publication.

Fig 2 – Three Stage Moderation Flow

Language addon and content generation

As the product’s multilingual requirements grew, Clixlogix evaluated the cost of scaling the commercial classifier to full enterprise API access. The vendor’s enterprise plan required a significant upfront deposit for access, which would have locked the cofounders into a single vendor commitment before the product had validated its market. Clixlogix advised the cofounders to bypass that commitment and build a GPT-4o supported agentic moderation architecture that handled the same content scenarios at a lower running cost and covered a wider range of languages than the enterprise classifier offered. The scope expanded through a signed change request to cover the language addon for multilingual moderation support on this new architecture. A second change request expanded the moderation stack into content generation capability, so the product could generate compliant content alongside its moderation function.

Web application build

Up to this point, users accessed the moderation system through Telegram and SMS. That worked for validation, but it could not support the dashboard views, admin controls and branding that enterprise buyers expected. The cofounders engaged Clixlogix on a second parallel project to build a web application frontend on Chakra UI, Next.js and React that connected to the n8n backend and gave the product a UI that could grow into a future enterprise platform.

Clixlogix ran a dedicated UI and UX phase covering the visual design system, interaction patterns, and the first milestone of the user portal. The team delivered the design through structured review sessions and validated the portal’s layout, navigation, and moderation workflows in 2 live demos before the cofounders made their next build decision.

Fig 3 – User Portal Milestone 1

By the end of Phase 1, the cofounders held a working moderation POC with validated economics, a user portal through Milestone 1, a proven LLM fit, and a cost model they could defend to investors. That foundation set the stage for what came next.

Consulting Insight

Build the POC lean enough to validate the market, then expand scope through signed change requests as confidence grows. 5 signed scopes across 2 projects let the cofounders control spend at every decision point.

Phase 2. The Emergent rebuild and the rescue

Between Phase 1 and Phase 2, the cofounders rebuilt the platform inside Emergent. The move shifted the stack from Next.js and Node.js to Python and Next.js on MongoDB and produced a large surface of new functionality, but hit the ceiling described above. The cofounders brought Clixlogix back in to assess the Emergent codebase and steer it to production.

Consulting Insight

Vibe coding systems drift before they break. Each generated session produces code that is coherent in isolation and arbitrary at the system level, so the codebase loses coherence quietly across iterations. Auditing every batch on the way in and governing the single integration point preserved the build velocity without letting the drift compound.

AI assisted code review and findings

Clixlogix ran an AI assisted review of the full Emergent codebase across the Python backend, the Next.js frontend and MongoDB. The review worked through five layers: git history to map commit patterns, session boundaries and drift points across the build; markdown docs and meeting notes to surface gaps between agreed scope and what the build contained; an architecture and non functional assessment covering coupling, storage design and infrastructure fit; a code quality checklist across persistence, authentication and billing logic; and a dependency audit to flag undeclared packages, runtime path conflicts and vendor lock. The review produced a scope versus implementation matrix, a concrete issue list and a revised delivery plan. 7 findings shaped the work.

Loose structure. The build coupled its parts loosely across the project and left no dependable backbone to extend.
No cloud storage. The code stored media on local disk and ignored the AWS S3 storage the deployment required.
Broken persistence and interface gaps. Profile images never reached the database, and content generation, dropdowns and authentication failed off the local machine.
Billing not wired in. The analysis logic ran on fixed values and tracked no token usage against a user’s plan.
Scope drift. The build carried features outside the agreed scope, including an enterprise flow, with no record of what aligned to scope.
Stack divergence. The shift to Python and Next.js on MongoDB set aside the earlier Milestone 1 and Milestone 2 work delivered in Phase 1.
Embedded tool dependency. An Emergent package sat inside the runtime paths and blocked the system from running on standard infrastructure.

Fig 4 – Vibe Coding Audit Pipeline

Remediation and stabilization

The Emergent codebase carried two cost problems alongside its structural ones. Keeping the application live on Emergent’s managed hosting consumed 50 credits per month, which on a standard plan ate half the monthly allocation before a single line of new code ran. Every iteration and fix the AI generated against its own errors burned additional credits with no predictable ceiling, making it impossible to budget the build or defend the spend to the downstream customer. The cofounders needed to break out of that credit loop and move to infrastructure they controlled and could scale without a metered ceiling above them.

Clixlogix designed a cloud deployment strategy to solve both the structural findings and the hosting constraint together. The team removed the Emergent package from the runtime paths and replaced it with native OpenAI and Stripe SDKs, which let the application run on standard infrastructure without the tool dependency. Engineers restored authentication, onboarding, Twilio messaging, content moderation, image generation and Stripe billing. They added credit proration for mid cycle plan changes and tiered limits on connected social accounts. The team then set AWS as the production target, stood up the staged deployment path with CI/CD, and moved the application off Emergent hosting entirely, freeing the credit allocation for build work and putting the application on infrastructure the cofounders could scale, monitor and control. A documented audit and remediation report captured every change and became the project’s engineering trail. The team cleared around 90 percent of the catalogued issues.

Agentic build and deployment pipeline

The cofounders chose to keep their build cycle inside Emergent. Clixlogix supported that decision and built an agentic delivery pipeline around it. The team set up a branch workflow with main, develop, bugfix and feature branches, and the cofounders pushed each batch of Emergent generated code to a feature branch. An AI driven audit ran on that code automatically before it reached the integration branch, produced a code review report for each batch, and flagged what needed fixing, so every change carried a record of what the audit found and how the team resolved it. A CI/CD pipeline on the develop branch then deployed validated code to staging on each merge.

Consulting Insight

Forcing founders off a tool mid rescue burns runway twice. The migration cost and the retraining time both land on a team that is already behind. Governing the output through a gate preserves the build velocity while moving the quality control point upstream where it can actually hold.

The pipeline ran on every push, around the clock. The cofounders worked across North America and the Nordics on their own cadence, pushed code at any hour, and the agentic audit and the CI/CD gate triaged and deployed it without waiting for the team to come online. The cofounders shipped features in parallel while the gate held the quality line on the integration branch.

Fig 5 – Agentic Audit and Deploy Gate

Consulting Insight

Harden the core on controlled branches while the cofounders keep shipping features. Two work streams, one integration point, and a gate that holds the quality line without slowing the cofounders down.

Auditing every batch on the way in protected the integration branch from the loose structure that had stalled the original build, and gave the Client a delivery trail their downstream customer could see.

Cloud deployment

The cofounders found the Emergent hosting limiting for production and asked Clixlogix to move the application onto managed cloud infrastructure. Clixlogix set AWS as the production target and stood up the staging environment with the CI/CD pipeline. Clixlogix evaluated Amazon Bedrock to make use of AWS startup credits available to the cofounders, but the available model setup on Bedrock did not satisfy the product’s image and video moderation requirements. The team moved image and video moderation to the Google Gemini vision APIs. Text moderation and generation stayed on the OpenAI APIs.

Fig 6 – AWS Deployment Architecture

The AWS staging setup gave the cofounders a production-ready target they could scale on, kept the moderation models on the vision APIs the product depended on, and put every change behind a CI/CD pipeline they could test against on each merge.

Delivery and go live

Across the full engagement, Clixlogix delivered 9 signed scopes across 2 phases. Phase 1 ran 5 scopes across 2 projects: the lean MVP and POC, the language addon, the web application planning and kickoff, the Hive AI alternative and content generation expansion, and the image design and user portal Milestone 1. Each scope signed before the next began, which let the cofounders control spend at every decision point and gave investors a staged evidence trail from POC economics through to a working portal.

Phase 2 ran 4 scopes through the rescue: a code review and onboarding wireframe scope, a code review and go live scope, a first fix pack, and a regression and UAT ready build scope. The team validated moderation across Arabic, Norwegian and Hindi and confirmed posting live across the supported social channels. 2 channels stayed gated on external platform approvals, which the cofounders cleared shortly after.

Staging the rescue across 4 signed scopes preserved the cofounders’ weekly build cadence, gave the downstream customer a clear sequence of milestones to hold against the contract, and kept every change traceable to a signed off deliverable.

Results

The Client received a validated moderation product across 2 phases of work. Phase 1 delivered a working POC with proven economics, a moderation architecture that handled cultural and situational meaning across modalities, and a user portal through Milestone 1. Phase 2 rescued the Emergent self build, cleared around 90 percent of the catalogued issues, moved the application onto AWS, and delivered a UAT ready build with an agentic audit and CI/CD pipeline governing every batch of generated code around the clock.

POC and Economics Validated on a Lean n8n Build

The cofounders held a working POC with validated economics 2 weeks after kickoff. The Hive AI test across 8 content scenarios, the cost engineering, and the LLM selection all closed in the first sprint, proving the model to investors before any production spend.

Cost Per Cycle Engineered from $1.72 to $0.20

Moving the n8n automation onto dedicated VPS hosting lifted a 5 workflow ceiling and dropped the per cycle cost from about $1.72 to about $0.20. That margin gave the tiered token model a defensible floor across every tier before the product went to market.

Around 90 Percent of Catalogued Issues Cleared

The AI assisted code review produced a concrete issue list across structure, storage, persistence, billing, scope and stack. The team worked the list down across 4 signed scopes and cleared around 90 percent of the catalogued issues during stabilization.

6 Week Active Rescue Inside an 8 Month Engagement

The 8 month engagement covered Phase 1 (discovery, POC, web app build and Milestone 1 delivery) and Phase 2 (the Emergent rebuild assessment and rescue). The active rescue turnaround from catalogued issues to a UAT ready staged build ran 6 weeks.

Round the Clock Triage Through an Agentic Deployment Pipeline

The agentic audit and CI/CD pipeline triaged every batch of generated code and deployed it to staging on each merge, around the clock with no wait for the team to come online. The cofounders pushed code on their own cadence and shipped features in parallel.

In the Client's Words

The cofounders credited the team's weekend push to merge and stabilize the code, noting the effort moved the product forward and accomplished a great deal in a short window.

Technologies and Tools

Group	Stack
Build and automation	Emergent, n8n
Moderation and AI	Hive AI, OpenAI GPT, Anthropic Claude, Google Cloud Vision (Phase 1), Google Gemini Vision (Phase 2), Whisper, RAG retrieval
Messaging	Twilio, Telegram
Data and storage	MongoDB Atlas with vector store, AWS S3
Payments	Stripe
Frontend	React, Next.js, Chakra UI
Backend	Python, Node.js
Posting and access	GetLate, Okta SSO
Cloud and delivery	AWS, GitHub, CI/CD pipeline, Amazon Bedrock evaluated

Services Delivered

Digital Engineering, Generative AI & ML, Consulting Service, Quality Assurance & Testing

Team Composition

Senior Delivery Manager, Technical Architect, Senior Business Analyst, Backend and Full Stack Engineers, QA Engineer, UI and UX Designer

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AI SaaS Built from POC to Investor Ready with a Full Vibe Coding Cleanup

Summary

About the Client

Challenge

Solution

Phase 1. Discovery, POC and web application build

Discovery and the n8n POC

Architecture and context aware moderation

Language addon and content generation

Web application build

Phase 2. The Emergent rebuild and the rescue

AI assisted code review and findings

Remediation and stabilization

Agentic build and deployment pipeline

Cloud deployment

Delivery and go live

Results

POC and Economics Validated on a Lean n8n Build

Cost Per Cycle Engineered from $1.72 to $0.20

Around 90 Percent of Catalogued Issues Cleared

6 Week Active Rescue Inside an 8 Month Engagement

Round the Clock Triage Through an Agentic Deployment Pipeline

Technologies and Tools

Have a question for our team or need help with your project?

Related Case Studies

How a UAE Short Term Rental Platform Reached #1 Rankings and AI Search Citations in 14 Months

Custom Patient App, AI Care Companion, and Revenue Cycle Automation for Cardiovascular Disease Management

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