HeroContent/Dataroom
Jakub
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Tech Stack

🛠️
Multi-cloud production stack. Web apps on AWS Amplify, core APIs on DigitalOcean App Platform, async processing on AWS Lambda + SQS, integration and AI services on Google Cloud Run. Main data infrastructure EU-hosted.

Architecture overview

The platform is built primarily in Python and TypeScript. Frontends use Next.js / React. Backend APIs and automation services are Python-based. The AI Support Bot uses FastAPI and LangGraph.

Multi-cloud is intentional — best-of-breed services per layer (AWS for static hosting + storage, DigitalOcean for managed APIs, GCP for AI / integration cloud and vector search).

Backend & infrastructure

Languages and frameworks

Databases & storage

Hosting & runtime

AI stack by task

Text generation

  • OpenAI GPT-5.4 / GPT-5.4-mini for content / context analysis, USP generation, image rules parsing, structured extraction, and visual selection workflows
  • OpenAI Responses API with web search for USP generation
  • GMI Cloud proxy as primary proxy / failover layer for OpenAI text/vision calls
  • AI Support Bot: Gemini 3.1 Pro Preview as primary, OpenAI GPT-5.2 as fallback
Multi-provider LLM stack with provider-abstracted architecture and fallback paths.

Image generation / editing

  • Gemini 3 Pro Image Preview via Google Vertex AI / AI Studio
  • OpenRouter as paid fallback for Gemini image generation/editing
  • SeedDream v4.5 via WaveSpeed for image editing/generation templates
  • FLUX Kontext Max via WaveSpeed for specific image editing contexts

Image enhancement

  • ClaidAI for real client photos in posts/stories and publishing safety checks
  • Cloudinary as fallback for enhancement / resizing
  • Local Pillow processing for AI-generated grid stylization (cost-optimized — avoids ClaidAI fees where no measurable quality gain)

Image search & selection

  • Semantic image search using Google multimodalembedding@001 + Cloud SQL PostgreSQL + pgvector
  • Restaurant-scoped image metadata: AI alt text, source, quality score, risk score, season, image type, vector embeddings
  • Image Rules Engine parses client directives via GPT-5.4-mini, then applies SQL source filters + AI visual / content rules
Proprietary restaurant image search layer indexes photos with multimodal embeddings and applies restaurant-specific business rules to choose safe, relevant assets for content generation.

Video / reels

  • Meta publishing pipeline supports Instagram + Facebook reels and video stories via Meta Graph API video container / upload flows
  • Creatomate for older / template generation paths
  • Automated creative generation today is primarily image / static-content focused

Fine-tuning, RAG, agents

  • No fine-tuning currently required — advantage comes from orchestration, proprietary client context, retrieval, and human-in-the-loop agent workflows
  • Retrieval systems: semantic image search over restaurant galleries, AI Support Bot memory / context retrieval, per-restaurant business context and client learnings
  • Agents: AI Support Bot (LangGraph), Client Context Analyzer, Image Rules Engine, USP / handover automations

Integrations

WhatsApp

  • Provider: Whapi.cloud (Twilio previously used, deprecated due to no group-messaging support)
  • Main company number + Marketa bot identity use separate Whapi channels
  • Multiple webhooks per number: admin API (logging / media processing) + AI Support Bot (response generation)
  • AI responses are never auto-sent. All AI-generated client replies require manager approval in Telegram before send
  • Outbound automated messages centralized through AI Support Bot outbound layer for consistent sender identity

Meta

  • Meta app status: Live · Business · Business Verified · Access Verified
  • Meta Graph API used for: Instagram + Facebook posts / stories / reels publishing, social metrics / insights, ads analytics, campaign creation MVP
  • OAuth / token-based access — Facebook / Instagram passwords are not stored
  • Custom client onboarding flow replaces Leadsie-style partner-access flow (stores connection audit data)
  • Admin OAuth includes ads scopes (ads_management, ads_read)

Other

  • Stripe — checkout + subscription billing
  • Planfix — CRM (linked to restaurant records via task references)
  • Google — Maps / Business Profile data ingestion, Vertex AI, Cloud Run, Cloud SQL

Security

Authentication

  • Client / admin apps use Firebase authentication
  • API requests include session / user headers + API access token
  • Integrations API uses app-level auth: X-CRM-Token, X-Access-Token, Stripe signature verification, Cloud Tasks secret, Telegram webhook secret (per endpoint)
  • Invalid auth on integrations endpoints returns 404 to reduce endpoint discovery
  • Meta accounts use OAuth / token flows — no Facebook / Instagram passwords are stored
  • Meta tokens stored server-side, used for publishing

Secrets management

  • Cloud Run services use Google Secret Manager for sensitive environment variables
  • Processor uses AWS Secrets Manager for Google service account credentials in production
  • Local .env files for development only

Data residency

Major production data infrastructure is EU-hosted:

  • AWS RDS, S3, Lambda in EU regions (Frankfurt eu-central-1)
  • GCP Cloud Run in europe-west1 and europe-west3
  • PostHog EU host

Operational monitoring

  • Google Cloud Logging — Cloud Run logs
  • AWS CloudWatch — Lambda logs
  • AWS Amplify — web app deployment/build logs
  • DigitalOcean App Platform — main API logs
  • Telegram alerts — failures, auth issues, webhook problems, payments, activation, invoicing, processing errors
  • Cloud Build CI/CD health checks for AI Support Bot and integrations service

Per-restaurant data architecture

The platform stores a restaurant-specific operating profile:

  • Photos (gallery from Google Maps, website, admin / client uploads, WhatsApp)
  • AI image metadata: alt text, quality score, risk score, season, image type, source
  • Generated content (posts, stories, reels, approval states, publish states)
  • Brand / style data: colors, fonts, AI style selections, template preferences
  • Client context profile: tone of voice, content directives, custom rules, image selector rules, USP, handover notes
  • Meta OAuth / page / Instagram tokens + connection audit events
  • WhatsApp chat mapping + message logs
  • Social performance metrics: IG / FB account metrics, media insights, ads data snapshots
  • Payment + billing history (Stripe checkout / subscription events)

This per-restaurant context is consumed by AI generation, human review, publishing, and customer support workflows.

Technical moat

  1. Vertical, end-to-end restaurant SMM operating system — onboarding, CRM, WhatsApp, media ingestion, AI generation, approval workflows, Meta publishing, billing, performance reporting in one integrated system
  2. Proprietary per-restaurant data layer — accumulating photos, brand voice, content rules, WhatsApp history, social metrics, approval history that improves future content generation and support workflows
  3. Productionized AI media pipeline — multi-provider AI image pipeline, semantic image search, image rules enforcement, automated enhancement, human review, direct Meta publishing. Much harder to replicate than a generic prompt wrapper
  4. Human-in-the-loop customer support automation — AI Support Bot handles WhatsApp intake, stage-specific reasoning, CRM tools, manager approval in Telegram, automation without losing quality control
  5. Built-in Meta onboarding & publishing infrastructure — proprietary client onboarding flow replaces third-party tools (Leadsie-style), with diagnostics around client page / Instagram connection state