Cuculi Notification Architecture: AI Microservices for Precision Nudges

As we kick off the notification program inside Cuculi, we are designing AI microservices that engineers can depend on to deliver psychologically informed nudges with the precision of a concierge team. The blueprint below turns that moonshot into a shared and testable architecture.

Explore the collaboration space

Dive into the evolving research notes, prompt experiments, and visual artifacts that complement this article: Google Doc Blueprint, Gemini Design Companion, and Claude Artifact Walkthrough.

A Technical Blueprint for AI-Powered Microservices to Drive User Engagement in a Social Dining Application

Hyper-personalized nudges are the antidote to broadcast fatigue. When every notification distills intent, timing, and tone, members stop feeling marketed to and start feeling guided. That shift requires a service mesh that can perceive context, infer intent, and produce psychologically tuned copy in real time—all while remaining observable, auditable, and maintainable by a lean team.

Strategic Foundation: A Modern Architecture for Personalized Engagement

Generic, broadcast-style notifications are increasingly ineffective, leading to user fatigue and abandonment. The alternative is hyper-personalization—context-aware, psychologically informed nudges that guide behavior toward valuable outcomes.¹ Modern recommendation engines fuse AI, NLP, and vector search to deliver real-time personalization that drives satisfaction and business metrics.³

The foundation of this personalization is understanding user intent—the motivation behind each action, not merely the action itself.⁴⁵ By inferring intent, the system can craft intuitive, high-signal experiences that increase loyalty and conversion. This architecture translates those principles into a production-ready stack tailored for a social dining product.

Architectural Paradigm: LLM-as-a-Microservice

Treat each large language model interaction as a discrete microservice, encapsulated behind a well-defined API. The LLM-as-a-Microservice pattern decouples experimental AI work from stable application code, enabling rapid evolution of prompts, model selections, and guardrails without destabilizing the broader platform.⁶

Encapsulation: Each service owns its prompts, retries, and post-processing, isolating LLM complexity from the rest of the system.⁶
Composability: Lightweight REST contracts allow intent, recommendation, and targeting agents to orchestrate sophisticated workflows.⁶
Independent Deployment & Scalability: Teams can A/B test prompts, swap models, and ship updates independently—critical for fast-paced AI iteration.⁶⁷

The trade-offs—additional network hops and distributed-system complexity—are mitigated through strong observability, contract testing, and infrastructure automation.

System Infrastructure: A Serverless-First Deployment on AWS

The Deployment Decision: AWS Lambda vs. Containerized Flask

Two deployment paths dominate Python microservices: serverless functions (AWS Lambda) and containerized Flask workloads on ECS or EC2. Lambda's pay-per-use economics, automatic scaling, and managed infrastructure make it ideal for sporadic, event-driven workloads such as engagement nudges.⁹¹¹

Table 1: Deployment Strategy Comparison (Lambda vs. Containerized Flask on ECS/EC2)
Feature	AWS Lambda	Containerized Flask (ECS/EC2)
Cost Model	Pay-per-invocation and duration; no idle costs.¹¹	Always-on instance spend regardless of traffic volume.¹¹
Scalability	Instant scaling from zero to thousands of concurrent executions managed by AWS.⁹	Requires configuring auto-scaling groups, load balancers, and container orchestration.
Maintenance Overhead	No server management; AWS handles patching and runtime updates.⁹	Teams manage EC2 instances, container runtimes, and orchestration policies.¹⁰
Cold Start Latency	Possible cold starts mitigated with provisioned concurrency.¹¹	Warm instances deliver consistent response times but incur idle costs.
Developer Experience	Testing requires tools like AWS SAM to emulate the cloud runtime.¹¹¹⁸	Familiar local development flows but more complex deployment pipelines.¹²
State Management	Stateless execution encourages externalizing shared data.¹¹	In-memory state is possible but complicates horizontal scaling.

Because notifications trigger on discrete events or timed checks rather than constant traffic, dedicated Lambda functions per microservice maximize cost efficiency and alignment with Cuculi's engagement rhythms.

System Architecture Overview

Amazon API Gateway fronts each microservice endpoint, providing routing, authentication, rate limiting, and throttling before requests reach their Lambda handlers.¹⁴¹⁵¹⁶ MongoDB Atlas anchors the data layer, storing operational documents alongside embeddings so retrieval and personalization happen inside a unified datastore.²¹²²

High-level architecture showing the mobile app connecting through API Gateway to Lambda microservices backed by MongoDB Atlas — Figure 1: Request flow from client to AI agent microservices and MongoDB Atlas.

The AI-Powered Engagement Engine

Boosting user retention for a social dining app with psychology-driven microservices.

Microservice Journey

🧑‍💻

User

→

📱

Social Dining App

→

AI Agent Microservices

(Python / Lambda)

→

🍃

MongoDB Atlas

Intent Service: The Strategist

Analyzes user activity to decide the \"why\" and \"when\" for each notification. It is the routing brain that picks the nudge moment.

User Recommendation: The Friend

Crafts personalized event suggestions by pairing vector search with a friendly, FOMO-aware tone.

Event Targeting: The Networker

Surfaces the most compatible audience for each event, ensuring invitations feel exclusive and relevant.

Profile Completion: The Host

Encourages new members to finish onboarding with warm, value-driven nudges.

The Psychology of Engagement

⏳

Urgency & Scarcity

Limited availability triggers FOMO and faster action.

🎯

Personalization

Specific matches demonstrate that the app understands each diner.

🤝

Social Proof

Highlighting friends and community momentum reinforces trust.

Mitigating Serverless Challenges

Cold starts introduce latency when Lambda functions scale from zero. Provisioned Concurrency eliminates the delay for latency-sensitive services, while scheduled warmers suffice for background tasks.¹¹ Local development parity improves through AWS SAM, which emulates Lambda and API Gateway locally for test-driven workflows.¹⁸

Dependency management relies on Lambda Layers for shared libraries (e.g., pymongo, langchain-mongodb) and function-specific deployment bundles assembled via SAM or the Serverless Framework.¹⁹

The Data Layer: Powering Semantics with MongoDB Atlas Vector Search

MongoDB Atlas' flexible document model stores evolving user profiles, event catalogs, and embeddings in a single operational plane.²¹²² Embeddings live alongside their narrative context, eliminating synchronization overhead with separate vector databases.²¹

Data Modeling and Enrichment

Enriched narratives dramatically improve semantic retrieval. Raw profile attributes transform into persona stories before embedding, producing higher-quality matches.²³²⁴ Below are representative schemas for the users and events collections.

{
  "_id": "ObjectId('...')",
  "name": "Alex Johnson",
  "created_at": "ISODate('...')",
  "profile_info": {
    "dietary_preferences": ["vegetarian"],
    "favorite_cuisines": ["Italian", "Mexican"],
    "profile_completion_percentage": 75
  },
  "activity_history": {
    "last_login": "ISODate('...')",
    "recent_events_viewed": ["ObjectId('...')"]
  },
  "user_persona_narrative": "Alex is a social diner who prefers vegetarian options...",
  "user_persona_embedding": [0.012, -0.345, ..., 0.987]
}

{
  "_id": "ObjectId('...')",
  "title": "Neighborhood Pasta Making Class",
  "description": "Learn to make fresh pasta from scratch!",
  "location": {"type": "Point", "coordinates": [-73.987, 40.748]},
  "time": "ISODate('...')",
  "tags": ["cooking class", "italian", "social"],
  "spots_left": 5,
  "attendees": ["ObjectId('...')"],
  "event_narrative": "This is an intimate, hands-on cooking class...",
  "event_narrative_embedding": [0.543, 0.123, ..., -0.654]
}

Implementing Vector Search

Generate Embeddings: Use consistent embedding models (OpenAI, Voyage AI, Hugging Face) to ensure vectors share the same semantic space.²⁶²⁷
Store Embeddings: Persist vectors within the corresponding MongoDB documents for unified access.²¹
Create Vector Indexes: Define Atlas Vector Search indexes specifying field, dimensions, and similarity metric.²⁵
Query with $vectorSearch: Combine semantic similarity with filters such as geography or recency for hybrid search experiences.²²²⁸

This approach keeps recommendations accurate, low-latency, and always in sync with the freshest behavioral data.⁴¹⁴²

The AI Agent Microservices: A Deep Dive

Table 2: Microservice Agent API Specification
Service Name	Endpoint	HTTP Method	Request Payload	Success Response
Intent Service	`/nudge/intent`	POST	`{"userId": "...", "triggerEvent": "app_opened_no_rsvp"}`	`{"nudgeType": "event_recommendation", "targetService": "/recommendations/user"}`
User Recommendation	`/recommendations/user`	POST	`{"userId": "..."}`	`{"notificationText": "Foodie alert! A sushi making class is happening near you tonight. Only 3 spots left!"}`
Event Targeting	`/targeting/event`	POST	`{"eventId": "..."}`	`{"userIds": ["...", "...", "..."]}`
Profile Completion	`/onboarding/profile-completion`	POST	`{"userId": "..."}`	`{"notificationText": "Welcome, Alex! Complete your profile to get personalized event matches."}`

Intent Service Agent

The Intent Service is the routing brain. V1 launches as a transparent rules engine so we can ship value before accumulating labeled outcomes. Each rule inspects MongoDB for recency, momentum, and state to select the next best action.³⁰³¹³²³³

Rule Examples: Profile completion nudges for new members, re-engagement nudges after 24 hours of inactivity, event reminders for upcoming RSVPs, and follow-up suggestions for viewed-but-not-booked events.
Phase Two: After capturing outcomes, upgrade to a classifier (e.g., Gradient Boosted Trees) that predicts the highest-probability nudge for each context.³¹

User Recommendation Service Agent

This agent implements a Retrieval-Augmented Generation (RAG) pipeline that pairs semantic retrieval with a tightly scoped prompt.³⁴³⁵³⁶ It fetches the user persona, runs a vector search against enriched events, filters for availability and geography, then instructs the LLM to generate a notification under 140 characters highlighting urgency and relevance.³⁷³⁸³⁹⁴⁰

Event Targeting Service Agent

The targeting agent inverts the query: it uses an event's embedding to find compatible diners. Business rules ensure we avoid fatigue (no duplicate nudges, respect opt-outs) while offering event creators a summarized audience snapshot.

Profile Completion Service Agent

Onboarding nudges focus on benefit framing—"complete your profile to unlock better matches"—and a single CTA. Tone guidelines keep the voice supportive and optimistic.³⁷⁴³⁴⁴⁴⁵

Table 3: Prompt Engineering Framework Application
Microservice Agent	Persona	Core Goal	Psychological Principle	Key Prompt Technique
User Recommendation	Enthusiastic Community Manager	Drive RSVP	Scarcity / Urgency (FOMO)³⁸	Role-playing & Context Injection
Event Targeting	Data Analyst	Inform Promoter	N/A (Internal-Facing)	Data Summarization
Profile Completion	Welcoming Onboarding Assistant	Drive Profile Edit	Benefit Highlighting⁴³	Single CTA & Persona Role-playing

Real-Time Data Pipeline: The Central Nervous System

Event-driven architecture ensures that every tap, browse, and period of inactivity can trigger the right microservice at the right time.⁴⁶⁴⁷⁴⁸ Mobile SDKs capture lifecycle, navigation, and interaction events, streaming them through a Kafka REST proxy to the ingestion layer.⁵⁰⁵¹⁵²⁵³

Event-driven data pipeline with mobile SDK, Kafka REST proxy, stream processor, and intent service triggers — Figure 2: Event-driven data pipeline powering real-time triggers.

Kafka provides durable, replayable streams for building robust trigger logic and analytics.⁴⁶⁴⁹⁵⁴⁵⁵ Consumer services maintain lightweight timers for inactivity thresholds and forward structured triggers to the Intent Service, keeping stream processing separate from AI orchestration.

Translating the Blueprint into Day-to-Day Execution

Beginning: Define the Intent and the Standard

North Star

Every notification should compress intelligence, context, and urgency into fewer than 140 characters while feeling unmistakably "Cuculi." That means consistent tone systems, audit-ready telemetry, and end-to-end observability from prompt to delivery.

We codify the definition of an ideal nudge: concise CTA, direct behavioral reference, and a purposeful psychological trigger. Treating each microservice as a product—with its own metrics, guardrails, and persona—keeps creativity and accountability in balance. The organizational frame borrows from AWS guidance on containerized Flask microservices and serverless agent orchestration patterns to anchor routing discipline before the first prompt ships.¹⁸¹⁹

Middle: Architect the Agent Mesh

The system is a mesh of RESTful agents, each deployed as a Lambda function fronted by API Gateway. Every service boots with its own persona prompt, guardrails, and telemetry. MongoDB Atlas handles primary storage, enabling low-latency access to user, event, and embedding collections. Vector search becomes the personalization backbone, echoing MongoDB's reference architectures.²¹²²

Transparency is non-negotiable. Responses are templated, logs capture prompt/context/completion/delivery, and observability mirrors best practices from modern push notification playbooks. This foundation lets us iterate quickly without sacrificing trust.

Service	Endpoint	Input / Output	Key Internal Logic	Prompt Personality Highlights
Intent Nudge	`/api/intent-nudge`	In: `user_id`, `recent_activity` Out: notification text < 140 characters	Classifies member momentum and selects the nudge type using recency and sentiment signals.	Coach-like, direct, and time-bound with scarcity cues when activity dips.
User Event Recommender	`/api/recommend-event`	In: `user_id`, profile vector Out: event invite message	Runs similarity search on event embeddings and cross-checks social proximity.	Conversational, enthusiastic friend persona anchored on shared interests.
Event-to-User Matcher	`/api/event-target`	In: `event_id` Out: batch of {user_id, message}	Generates ranked audience lists with fairness weighting.	High-energy promoter emphasizing exclusivity without overpromising.
Profile Completion Guide	`/api/complete-profile`	In: `user_id`, profile status Out: motivational reminder	Surfaces missing profile fields and the benefits unlocked by completing them.	Encouraging mentor tone centered on belonging and value.

Prompt Blueprint

Each agent maintains a system prompt snippet that keeps outputs concise, actionable, and psychologically tuned.

You are the Intent Nudge agent for Cuculi.
- Output a single push notification under 140 characters.
- Reference the user's latest dining signal.
- End with one CTA.
- Use urgency or scarcity only when activity has dipped.
- Never fabricate people, places, or rewards.
Context: {{recent_activity_snapshot}}
Goal: Drive the next RSVP within 24 hours.

End: Orchestrate, Measure, and Keep Human in the Loop

A lightweight router validates payloads, manages retries, and escalates slow LLM calls to async workers powered by serverless queues and Step Functions. Delivery channels (FCM, APNs, SMS) feed open rates and RSVPs back into the loop so prompts evolve with observed behavior.

Shared dashboards illuminate which agent versions drove lifts across product, engineering, and community teams.
Prompt retrospectives treat copy changes like product releases with documented hypotheses and QA sign-off.
Human escalation paths keep concierges ready to intervene when nuance demands human judgment.

The outcome is an elegant, modular notification stack that feels personal, scales responsibly, and keeps cross-functional teams in sync.

Cuculi Message Engine

A deep dive into the AI microservice that crafts personalized, psychologically-driven nudges.

The AI Content Generation Workflow

The Brain of the Operation

If a user is eligible for a nudge, the Message Engine microservice—an AWS Lambda function—is invoked. Its sole purpose is to craft the perfect message to inspire action. This isn't just a simple template; it's a sophisticated process that combines data retrieval, prompt engineering, and cutting-edge AI to generate content that feels personal and relevant.

Core Logic: From Data to Delivery

The engine's logic follows a three-step process:

Retrieval-Augmented Generation (RAG): The first step is to gather all available context. The engine queries our MongoDB database to retrieve a rich dataset, including the user's name and known interests, specific event details like cuisine and remaining spots, and powerful social proof data, such as which of their friends are also interested in the event.
Dynamic Prompt Engineering: This raw data is then dynamically assembled into a highly-detailed, goal-oriented prompt. We don't just ask the AI to "write a message." We provide it with the full context, the psychological principle we want to use (e.g., scarcity, social proof), and a clear objective (e.g., "Create FOMO and encourage an immediate RSVP").
AI-Powered Generation: The final, context-rich prompt is sent to OpenAI's GPT-4o model. The AI uses the provided information to generate a creative, human-like, and persuasive message that is tailored specifically to that user and that moment.

Code in Action

Below is a conceptual Python snippet illustrating how the Message Engine assembles its prompt and calls the OpenAI API.

# Conceptual Python for the Message Engine
import openai

async def generate_nudge_message(user, event, nudge_type, friends_interested):
    # 1. Assemble context via RAG (Data is pre-fetched and passed in)
    context = {
        "user_name": user.get("name"),
        "event_title": event.get("title"),
        "spots_left": event.get("total_participants") - len(event.get("rsvps")),
        "friend_names": ", ".join(friends_interested)
    }

    # 2. Dynamically build the prompt based on nudge type
    prompt = f"""
    You are an expert copywriter for a social dining app. Your goal is to convert interest into RSVPs.
    
    Nudge Type: "{nudge_type}"
    User Name: {context['user_name']}
    Event: {context['event_title']}
    
    Context:
    - There are only {context['spots_left']} spots remaining for this event.
    - The user's friend(s) {context['friend_names']} are also interested.

    Task:
    Write a short, exciting, and friendly push notification (max 160 characters).
    Use the provided context to create a sense of urgency and social proof.
    Address the user by their first name.
    """

    # 3. Call OpenAI API
    response = await openai.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content.strip()

Sample Generated Nudges

Scarcity Alert

"Hey Maya! Heads up - only 3 spots left for the Taco Tuesday Fiesta at La Condesa. Grab yours before they're gone!"

Social Proof

"Looks like you and Ben have great taste! You both checked out the Sushi Making Class. Why not go together?"

New Match

"Hi Sam! Based on your love for Italian, we think you'll be a big fan of the new Gnocchi Night at Giovanni's. Check it out!"

Strategic Roadmap and Conclusion

Phase 1 – Foundational Layers: Stand up MongoDB Atlas, define schemas, build CRUD APIs, integrate analytics SDKs, and stream raw events into Kafka.
Phase 2 – First Value: Deploy the Profile Completion agent with the rules-based Intent Service to drive immediate onboarding improvements.
Phase 3 – Core AI Features: Enrich data via LLMs, generate embeddings, configure vector indexes, and launch the Recommendation and Targeting agents.
Phase 4 – Optimization & Adaptation: Analyze outcomes, train a machine-learning-powered Intent Service, and evolve prompts based on behavioral feedback.

This architecture pairs serverless agility with semantic intelligence so Cuculi can move beyond generic notifications. By inferring intent, enriching data, and orchestrating persona-rich agents, every nudge becomes a strategic invitation that compounds retention and community value.

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