influencer-platform-technical-design

Technical Design Document — v2.0

AI-Powered Influencer Campaign Automation Platform

Version: 2.0 (Revised — incorporates Grok market research, Claude architecture review, and open-source tooling audit)
Date: April 30, 2026
Author: Technical Architecture Team
Status: PROPOSAL — Pre-Scoping Phase

📋 View v1.0 (archived) | This is the current version.

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Changelog from v1.0

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Table of Contents

1. Executive Summary
2. Problem Statement
3. Architecture Overview
4. Module 1 — Outreach & Negotiation Agent
5. Module 2 — Instagram Engagement Dashboard
6. Module 3 — Influencer Discovery Engine
7. Data Architecture
8. Infrastructure & DevOps
9. Security & Compliance
10. Cost Estimation
11. Risk Register & Mitigations
12. Development Timeline
13. Team Structure
14. Assumptions, Dependencies & Out of Scope
15. Open-Source Tooling Map — Build vs. Reuse
16. Competitive Landscape & Market Intel
17. Appendix — Tech Lead Review
18. Appendix — API Rate Limits
19. Appendix — Glossary
20. Next Steps

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1. Executive Summary

This document defines the architecture for an end-to-end influencer campaign automation platform with three modules:

Key architectural philosophy: Use open-source where it saves >1 week of dev time; build custom only where differentiation matters (the negotiation agent FSM).

The platform maximizes open-source tooling to avoid reinventing the wheel:

• n8n (52K+ ⭐) for workflow orchestration instead of custom schedulers
• Chatwoot (21K+ ⭐) for conversation inbox instead of custom chat UI
• Metabase (40K+ ⭐) for v1 dashboard instead of custom charts
• Evolution API (2K+ ⭐) or WAHA for WhatsApp dev/test sandbox
• cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual for bulk sentiment (free, multilingual)
• pgvector (Supabase-native) for FAQ retrieval instead of Pinecone

Custom code is concentrated in the LangGraph negotiation state machine — the core IP of the platform.

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2. Problem Statement

2.1 Current State

• Campaign managers maintain influencer lists in Google Sheets
• Each influencer contacted individually via phone/WhatsApp
• Pitching, FAQ handling, rate negotiation done person-to-person
• Zero post-campaign visibility into engagement, ROI, or influencer performance
• New influencer sourcing is ad hoc with no systematic discovery

2.2 Target State

• Automated outreach triggered from Google Sheets data
• AI agent handles pitch → FAQ → negotiation → close/escalate
• Humans only intervene for hot leads flagged by the agent
• Real-time IG engagement data per influencer and per campaign
• Systematic creator discovery refreshing the pipeline every 2-3 days

2.3 Market Context (from Grok Research)

What the market says about this category:

• Sentiment: "Cautiously optimistic with growing adoption" — brands see value in automating "80% of grunt work"
• Key pain point: Generic templates lead to low response rates; influencers spot templated pitches
• Critical success factor: Hyper-personalization referencing specific posts/content, not just "Hi {name}"
• Competitive edge of our approach: Most existing tools (Grin, AspireIQ, Modash) are discovery + tracking platforms, NOT automated negotiation agents. Janney AI is the closest competitor but doesn't support WhatsApp as primary channel
• India-specific: WhatsApp is dominant; Hinglish (Hindi+English mix) is common in influencer conversations; regional language support is a differentiator

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3. Architecture Overview

┌────────────────────────────────────────────────────────────────────────────┐
│                         CLIENT INTERFACE LAYER                             │
│                                                                            │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ Google Sheets │  │    Slack     │  │  Metabase    │  │  Chatwoot    │  │
│  │ (Influencer  │  │  (Human     │  │  Dashboard   │  │  (Conv.      │  │
│  │  Master List)│  │   Handoff)  │  │  (IG metrics)│  │   Inbox)     │  │
│  └──────┬───────┘  └──────▲──────┘  └──────▲───────┘  └──────▲───────┘  │
└─────────┼─────────────────┼────────────────┼──────────────────┼──────────┘
          │                 │                │                  │
┌─────────┼─────────────────┼────────────────┼──────────────────┼──────────┐
│         ▼                 │                │                  │          │
│  ┌─────────────────────────────────────────────────────────────────────┐ │
│  │                    n8n ORCHESTRATION ENGINE                         │ │
│  │  (self-hosted, 52K+ ⭐)                                            │ │
│  │                                                                     │ │
│  │  Workflows:                                                         │ │
│  │  ┌─────────────────┐  ┌─────────────────┐  ┌────────────────────┐  │ │
│  │  │ Sheet Sync      │  │ Outreach        │  │ IG Polling         │  │ │
│  │  │ (every 60s or   │  │ Trigger         │  │ (every 4h metrics  │  │ │
│  │  │  webhook)       │  │ (new rows →     │  │  every 2h posts)   │  │ │
│  │  │                 │  │  queue → agent)  │  │                    │  │ │
│  │  └─────────────────┘  └─────────────────┘  └────────────────────┘  │ │
│  │  ┌─────────────────┐  ┌─────────────────┐  ┌────────────────────┐  │ │
│  │  │ Discovery       │  │ Sentiment       │  │ Anomaly Detection  │  │ │
│  │  │ (every 3 days)  │  │ Batch           │  │ (daily 2 AM)       │  │ │
│  │  │                 │  │ (every 4h)      │  │                    │  │ │
│  │  └─────────────────┘  └─────────────────┘  └────────────────────┘  │ │
│  └─────────────────────────────────────────────────────────────────────┘ │
│                                                                          │
│  ┌───────────────────────┐  ┌────────────────────────────────────────┐   │
│  │  AI AGENT SERVICE     │  │  DATA SERVICES                        │   │
│  │  (Python / FastAPI)   │  │                                        │   │
│  │                       │  │  ┌────────────────────────────────┐    │   │
│  │  ┌─────────────────┐  │  │  │ Supabase                       │    │   │
│  │  │ LangGraph FSM   │  │  │  │ (PostgreSQL + pgvector +       │    │   │
│  │  │ (negotiation    │  │  │  │  Realtime + Auth + RLS)        │    │   │
│  │  │  state machine) │  │  │  └────────────────────────────────┘    │   │
│  │  ├─────────────────┤  │  │  ┌────────────────────────────────┐    │   │
│  │  │ Budget          │  │  │  │ Redis                           │    │   │
│  │  │ Guardrail Layer │  │  │  │ (message queue, session cache)  │    │   │
│  │  │ (hard-coded,    │  │  │  └────────────────────────────────┘    │   │
│  │  │  outside LLM)   │  │  │                                        │   │
│  │  └─────────────────┘  │  └────────────────────────────────────────┘   │
│  └───────────────────────┘                                               │
│                                                                          │
│  ┌──────────────────────────────────────────────────────────────────┐    │
│  │  EXTERNAL APIs                                                   │    │
│  │                                                                   │    │
│  │  WhatsApp Cloud API (Meta) ←── primary messaging channel          │    │
│  │  Twilio Voice + Deepgram + ElevenLabs ←── voice fallback         │    │
│  │  OpenAI GPT-4o / GPT-4o-mini ←── LLM backbone                   │    │
│  │  Instagram Graph API ←── engagement data (free, permissioned)     │    │
│  │  Phyllo API ←── engagement fallback (paid, no permissions needed) │    │
│  │  Modash API ←── influencer discovery                              │    │
│  │  Slack API ←── human handoff notifications                        │    │
│  │  HuggingFace Inference API ←── XLM-RoBERTa sentiment (free tier) │    │
│  └──────────────────────────────────────────────────────────────────┘    │
└──────────────────────────────────────────────────────────────────────────┘

Hosting: n8n + AI Agent Service on Railway (or Render) | Supabase (managed) | Metabase on Railway (Docker) | Vercel (if/when custom Next.js dashboard in v2)

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4. Module 1 — Outreach & Negotiation Agent

4.1 Messaging Channel: WhatsApp Cloud API (Direct)

Decision unchanged from v1: WhatsApp Cloud API (Meta-hosted) — NOT Twilio-mediated.

Why Cloud API still wins: We need raw webhook control for our LangGraph agent. WATI/360dialog abstract too much. Twilio adds unnecessary cost and latency.

NEW in v2 — Dev/Test Sandbox: Use Evolution API (open-source, 2K+ ⭐) or WAHA for local development. These self-hosted WhatsApp HTTP APIs let engineers test message flows without burning Meta API quota or waiting for template approvals.

Critical constraints (unchanged):

1. 24-hour conversation window rule
2. Template categories: marketing (outreach) = most expensive
3. Quality rating system — pace at 50 new outreach/hr max
4. Business verification required Day 1 (1-2 weeks lead time)

4.2 Voice Fallback: AI Voice Agent (Upgraded from v1)

v1: Pre-recorded pitch + live transfer.
v2: Full AI voice agent using Twilio Voice + Deepgram STT + ElevenLabs TTS.

Influencer picks up phone
        │
        ▼
┌─────────────────────────┐
│ Twilio Voice streams     │
│ audio to Deepgram STT    │──→ Text transcription
└─────────────────────────┘         │
                                    ▼
                            ┌──────────────┐
                            │ LangGraph    │──→ Response text
                            │ Agent        │
                            │ (same FSM as │
                            │  WhatsApp)   │
                            └──────────────┘
                                    │
                                    ▼
                            ┌──────────────┐
                            │ ElevenLabs   │──→ Audio stream
                            │ TTS          │     back to Twilio
                            └──────────────┘

Why upgrade: Grok research shows indie builders achieving this stack. It's a differentiator vs. Janney AI (email-only outreach). Same LangGraph FSM handles both WhatsApp text and voice — one codebase, two channels.

Guardrail: If voice agent detects confusion or negative sentiment for >2 turns → warm transfer to human. Agent says: "Let me connect you with our team directly."

4.3 AI Negotiation Agent: LangGraph State Machine

Architecture unchanged from v1 but with two critical additions from Claude research:

Addition 1: Budget Guardrail Layer OUTSIDE the LLM

# Guardrail layer — runs BEFORE LLM response is sent
class BudgetGuardrail:
    """Hard-coded rules. LLM never sees max budget."""
    
    def validate_offer(self, agent_response, campaign_config):
        proposed_rate = extract_rate(agent_response)
        
        if proposed_rate is None:
            return agent_response  # Not a rate offer, pass through
        
        if proposed_rate > campaign_config.budget_max:
            # BLOCK — never send this. Escalate instead.
            return ESCALATE_TO_HUMAN
        
        if proposed_rate < campaign_config.budget_min:
            # Agent tried to undercut — fix to min
            return replace_rate(agent_response, campaign_config.budget_min)
        
        return agent_response  # Within band, safe to send

Why this matters: Claude research flagged prompt injection risk — a sophisticated influencer could theoretically manipulate the LLM into revealing budget ceilings or agreeing above max. The guardrail layer makes this impossible because the max budget value never enters the LLM context.

Addition 2: FAQ Knowledge Base via pgvector RAG

┌────────────────────────────┐
│ Campaign Setup              │
│ Admin uploads FAQ doc       │
│ → Chunked + embedded        │
│ → Stored in Supabase        │
│   pgvector table            │
└────────────┬───────────────┘
             │
┌────────────▼───────────────┐
│ Influencer asks question    │
│ → Embedded via OpenAI       │
│ → Similarity search pgvector│
│ → Top 3 chunks as context   │
│ → GPT-4o-mini generates     │
│   answer from chunks ONLY   │
│ → If confidence < 0.7:      │
│   "Let me check on that"    │
│   → queued for human        │
└────────────────────────────┘

Why pgvector over Pinecone: Supabase has native pgvector support. Zero extra infrastructure, zero extra cost, zero extra vendor. FAQ corpus for a campaign is tiny (~50-200 chunks) — pgvector handles this trivially.

4.4 Conversation Inbox: Chatwoot (Open-Source)

NEW in v2. Instead of building a custom conversation log viewer, we use Chatwoot (21K+ ⭐):

Integration pattern:

1. WhatsApp webhooks → Chatwoot (handles message display + history)
2. Chatwoot webhook → our LangGraph agent (handles AI response generation)
3. LangGraph response → Chatwoot API (sends response back through WhatsApp)
4. Human handoff: agent marks conversation in Chatwoot, human picks up in same inbox

Trade-off: Adds a dependency (Chatwoot self-hosted). But saves ~3 weeks of custom UI development and gives the operations team a professional inbox from Day 1.

4.5 Google Sheets Integration

Unchanged from v1 — bidirectional sync via Sheets API v4. n8n has a native Google Sheets node that handles this with zero custom code.

4.6 Human Handoff Flow

Unchanged from v1 — Slack notification with interactive buttons. n8n handles the Slack notification trigger.

4.7 LLM Selection

Revised based on both research reports:

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5. Module 2 — Instagram Engagement Dashboard

5.1 Data Collection: Dual-Layer (Unchanged)

Graph API (primary, free) + Phyllo (fallback, paid). See v1 for decision logic.

5.2 Sentiment Analysis: Tiered Approach (NEW in v2)

Claude research recommended cardiffnlp/twitter-roberta-base-sentiment. We adopt a tiered version:

                    ┌─────────────────────┐
                    │ Instagram Comments   │
                    │ (batch every 4h)     │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
            ┌───── │ Language Detection   │ ─────┐
            │      └──────────────────────┘      │
            │                                     │
     English/European                    Hindi/Hinglish/
     languages                           Regional/Emoji-heavy
            │                                     │
            ▼                                     ▼
┌──────────────────────┐            ┌───────────────────────┐
│ TIER 1: XLM-RoBERTa  │            │ TIER 2: GPT-4o-mini   │
│ (HF Inference API     │            │ (with structured       │
│  or self-hosted)      │            │  output JSON mode)     │
│                       │            │                        │
│ cardiffnlp/twitter-   │            │ Handles:               │
│ xlm-roberta-base-     │            │ - Code-mixed text      │
│ sentiment-multilingual│            │ - Sarcasm              │
│                       │            │ - Heavy emoji usage    │
│ 10.8M downloads       │            │ - Slang/abbreviations  │
│ Multilingual trained   │            │                        │
│ FREE on HF Inference  │            │ ~$0.15/1M tokens       │
│ ~50ms per comment     │            │ ~500ms per comment     │
│                       │            │                        │
│ Output: pos/neg/neu   │            │ Output: pos/neg/neu/   │
│ + confidence score    │            │ spam + topics +        │
│                       │            │ purchase_intent        │
└──────────┬────────────┘            └──────────┬────────────┘
           │                                     │
           │    ┌─────────────────────┐          │
           └───→│ IF confidence < 0.6 │──────────┘
                │ → route to Tier 2   │  (fallback)
                └─────────────────────┘
                           │
                           ▼
                ┌─────────────────────┐
                │ Store in Supabase   │
                │ [comments] table    │
                └─────────────────────┘

Why tiered:

• XLM-RoBERTa handles ~80-90% of comments (English, Spanish, Portuguese, French, etc.) at zero marginal cost
• Only truly ambiguous comments (low confidence) or code-mixed Hinglish route to GPT-4o-mini
• At 10K comments/campaign: ~9K free (Tier 1) + ~1K at $0.15/1M tokens (Tier 2) = ~$0.002 total. vs. $1.50 if all via GPT-4o-mini

5.3 Dashboard: Metabase (v1) → Next.js + Tremor (v2)

NEW in v2. Instead of building a custom dashboard from Day 1:

Phase 1 (v1): Metabase (open-source, 40K+ ⭐)

• Docker deploy on Railway (~5 min setup)
• Connect directly to Supabase PostgreSQL
• Built-in: chart builder, dashboard creator, filters, drill-downs, scheduled reports, embedding
• The ops team can build their own views without engineering help
• Time to ship: 2 days (vs. 2 weeks for custom)

Phase 2 (v2, Week 12+): Custom Next.js + Tremor

• Only if/when Metabase's limitations become clear (specific UX needs, real-time, custom anomaly views)
• By then, we know exactly what views the client uses most → build those specifically

5.4 Anomaly Detection

Unchanged from v1 — Modified Z-Score with rolling baseline. Integrated as an n8n workflow running daily at 2 AM.

5.5 Polling Architecture

Unchanged from v1 but now managed by n8n workflows instead of custom cron:

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6. Module 3 — Influencer Discovery Engine

Unchanged from v1. Modash API, every 3 days, deduplicate on IG handle. Now orchestrated by n8n workflow.

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7. Data Architecture

7.1 Database Schema

Unchanged from v1 (campaigns, influencers, conversations, campaign_posts, engagement_snapshots, comments, anomalies) with one addition:

-- NEW: FAQ knowledge base for RAG
CREATE TABLE faq_chunks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    campaign_id UUID REFERENCES campaigns(id),
    content TEXT NOT NULL,
    embedding VECTOR(1536),  -- OpenAI text-embedding-3-small
    source_document TEXT,
    chunk_index INTEGER,
    created_at TIMESTAMPTZ DEFAULT now()
);

-- pgvector index for similarity search
CREATE INDEX ON faq_chunks USING ivfflat (embedding vector_cosine_ops) WITH (lists = 10);

7.2 Message Bus: Redis

NEW in v2 (from Claude architecture). Redis Pub/Sub for decoupling:

• n8n publishes outreach trigger → Redis → Agent service consumes
• Agent publishes state change → Redis → n8n consumes → updates Sheet + Slack
• Prevents tight coupling between orchestration and agent service

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8. Infrastructure & DevOps

8.1 Revised Tech Stack

8.2 Service Map on Railway

Railway Project
├── Service: n8n (Docker)           — 1 GB RAM — $10/mo
├── Service: agent-service (Python) — 512 MB RAM — $5/mo
├── Service: chatwoot (Docker)      — 2 GB RAM — $15/mo
├── Service: metabase (Docker)      — 1 GB RAM — $10/mo
├── Service: redis                  — 256 MB RAM — $3/mo
└── Total Railway: ~$43/mo

External Managed:
├── Supabase Pro: $25/mo
└── Domain + DNS: $15/mo

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9. Security & Compliance

Unchanged from v1 with one addition:

9.4 Prompt Injection Prevention (NEW)

WhatsApp messages from influencers are untrusted input flowing into an LLM:

• Input sanitization: Strip any system-prompt-like patterns ("ignore previous instructions", "you are now", etc.)
• Budget isolation: Max budget value NEVER enters LLM context. Guardrail layer validates outputs.
• Output validation: Every agent response checked for: PII leakage, profanity, off-brand content, rates outside band
• Conversation length limit: Max 15 turns before auto-escalate to human (prevents adversarial probing)

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10. Cost Estimation

10.1 Monthly Cost — 100 Influencers

10.2 Monthly Cost — 500 Influencers

Key insight unchanged: Phyllo is the biggest cost driver. Push Graph API OAuth adoption aggressively.

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11. Risk Register & Mitigations

All 10 risks from v1 unchanged, plus:

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12. Development Timeline

Revised Timeline (incorporates open-source tooling — net schedule unchanged at 10 weeks but with more capability delivered)

PHASE 0 — Foundation (Week 1-2)
├── Week 1
│   ├── Day 1-2: Infrastructure setup
│   │   ├── Railway: deploy n8n, Redis, Chatwoot (Docker)
│   │   ├── Supabase: project + full schema migration (incl. pgvector)
│   │   ├── GitHub repo (monorepo: services/agent, services/monitor)
│   │   ├── CI/CD: GitHub Actions → Railway
│   │   └── ⚠️ START Meta Business Verification
│   │
│   ├── Day 3-4: Google Sheets + Slack
│   │   ├── n8n: Google Sheets sync workflow (bidirectional)
│   │   ├── n8n: Slack notification workflow (templated alerts)
│   │   └── Test: add row in Sheet → appears in Supabase → Slack ping
│   │
│   └── Day 5: WhatsApp Cloud API
│       ├── Meta Business Manager app setup
│       ├── Webhook endpoint (FastAPI)
│       ├── Template messages submitted for approval
│       ├── Evolution API / WAHA sandbox for dev testing
│       └── Chatwoot ↔ WhatsApp connection configured
│
├── Week 2
│   ├── Day 6-7: Auth + Chatwoot config
│   │   ├── Supabase Auth (Google OAuth)
│   │   ├── Chatwoot: team setup, canned responses, WhatsApp channel
│   │   └── Chatwoot webhook → FastAPI agent endpoint
│   │
│   ├── Day 8-9: Metabase dashboard (v1)
│   │   ├── Deploy Metabase Docker on Railway
│   │   ├── Connect to Supabase PostgreSQL
│   │   ├── Build: campaign overview, influencer table, engagement charts
│   │   └── Build: anomaly log view, conversation status board
│   │
│   └── Day 10: Integration testing
│       ├── Sheet → Supabase → WhatsApp → Chatwoot flow
│       ├── Slack notification delivery
│       └── Metabase reads from live Supabase data
│
│   ✅ DELIVERABLE: Full infra live. WhatsApp send/receive working.
│      Chatwoot inbox operational. Metabase dashboard connected.

PHASE 1 — Negotiation Agent (Week 3-5)
├── Week 3
│   ├── Day 11-13: LangGraph agent core
│   │   ├── State machine: INTRO → QUALIFY → FAQ → NEGOTIATE → CONFIRM → CLOSE/ESCALATE
│   │   ├── State persistence to Supabase (checkpoint after every transition)
│   │   ├── GPT-4o structured output integration
│   │   ├── Budget guardrail layer (hard-coded, outside LLM)
│   │   ├── System prompts: campaign brief injection, tone, Hinglish support
│   │   └── Unit tests for every state transition
│   │
│   └── Day 14-15: FAQ RAG pipeline
│       ├── pgvector table + embedding pipeline
│       ├── FAQ document chunker + uploader
│       ├── Semantic search endpoint
│       ├── GPT-4o-mini generates answer from retrieved chunks
│       └── Confidence threshold: < 0.7 → "Let me check" → human queue
│
├── Week 4
│   ├── Day 16-17: Negotiation logic + guardrails
│   │   ├── Counter-offer algorithm (midpoint, max 3 rounds, escalate)
│   │   ├── Output validation (rate extraction, profanity check, PII check)
│   │   ├── Input sanitization (prompt injection prevention)
│   │   └── Lead scoring: Cold / Warm / Hot / Rejected
│   │
│   ├── Day 18-19: Human handoff + Slack integration
│   │   ├── n8n workflow: agent → Slack interactive message
│   │   ├── Chatwoot: agent → human assignment
│   │   ├── Human takeover in Chatwoot inbox
│   │   └── Google Sheet status sync on every state change
│   │
│   └── Day 20: Voice fallback agent
│       ├── Twilio Voice + Deepgram STT + ElevenLabs TTS pipeline
│       ├── Same LangGraph FSM, voice-adapted (shorter responses)
│       ├── n8n trigger: 48h no WA response → schedule voice call
│       └── Warm transfer to human if needed
│
├── Week 5
│   ├── Day 21-23: End-to-end testing
│   │   ├── Full flow: Sheet → WA → Negotiate → Handoff → Close → Sheet update
│   │   ├── Edge cases: non-responsive, reject, > max budget, Hinglish, sarcasm
│   │   ├── Voice flow testing
│   │   ├── Load test: 50 concurrent conversations
│   │   └── Quality audit: review 20 AI conversations for tone + accuracy
│   │
│   └── Day 24-25: Polish
│       ├── Error handling, retries, exponential backoff
│       ├── Conversation timeout (auto-follow-up at 24h, 48h, then dormant)
│       └── Metabase: conversation management views
│
│   ✅ DELIVERABLE: Full outreach → negotiate → handoff → close pipeline.

PHASE 2 — IG Monitoring (Week 6-8)
├── Week 6: Instagram Graph API + Phyllo integration + polling n8n workflows
├── Week 7: Tiered sentiment pipeline + anomaly detection + ROI engine
├── Week 8: Metabase dashboard enhancements + testing with real data
│
│   ✅ DELIVERABLE: Live engagement dashboard with sentiment + anomalies.

PHASE 3 — Discovery + Hardening (Week 9-10)
├── Week 9: Modash discovery + dedup + enrichment (n8n workflow)
├── Week 10: Security hardening + documentation + UAT + go-live
│
│   ✅ DELIVERABLE: Production platform, hardened, documented.

Timeline Summary

WEEK:  1    2    3    4    5    6    7    8    9    10
       ├────┤────├────┤────┤────├────┤────┤────├────┤────┤
Phase 0 ████████
Phase 1           ████████████████
Phase 2                          ████████████████
Phase 3                                           ████████

META VERIFICATION ████████████░░░░  (parallel)

TOTAL: 10 weeks to production MVP

---

13. Team Structure

Revised for open-source leverage — smaller team needed:

Total: 3.5 FTE for 10 weeks (down from 4.25 in v1 — open-source tooling saves ~0.75 FTE)

---

14. Assumptions, Dependencies & Out of Scope

14.1 Client Responsibilities

• Provide Google Sheet with influencer data (name, WhatsApp, category, followers, IG handle) — Week 1
• Initiate Meta Business verification + WhatsApp Business API access — Week 1
• Provide campaign briefs, FAQ docs, product details, budget bands — before Phase 1
• Coordinate influencer consent for IG Graph API permissions
• Designate Slack channel + assign lead follow-up team

14.2 Assumptions

• ≥60% of influencers have WhatsApp numbers
• IG Graph API accessible for meaningful subset (Business/Creator accounts)
• Phyllo/Modash subscriptions approved ($200-500/mo)
• LLM API costs budgeted separately
• Cloud hosting approved (~$100-300/mo)

14.3 Out of Scope (v1)

• CRM integration (Salesforce, HubSpot) — Chatwoot + Sheets serves as v1 CRM
• YouTube, TikTok, Twitter monitoring — IG only
• Contract generation / e-signature
• Payment processing / invoicing
• Custom mobile app — web only
• Multi-language beyond English + Hindi/Hinglish

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15. Open-Source Tooling Map — Build vs. Reuse

This is the key section. For every component, we evaluate: should we build it, or use an existing open-source project?

15.1 Recommended Open-Source Repos

15.2 What We Build Custom (Core IP)

15.3 Decision Framework

For any new feature, ask:
1. Is there an OSS repo with >1K ⭐ that does 80%+ of what we need?
   YES → REUSE (fork if needed, contribute back)
   NO  → Continue

2. Is there a reference implementation we can study?
   YES → REFERENCE (copy patterns, adapt to our stack)
   NO  → Continue

3. Does this component constitute core IP / competitive advantage?
   YES → BUILD CUSTOM
   NO  → Use simplest possible glue code

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16. Competitive Landscape & Market Intel

(Synthesized from Grok research — X posts, Reddit, reviews, late 2025-early 2026)

16.1 Direct Competitors

16.2 Our Differentiation

16.3 Market Sentiment Summary

• "Future is agents, not more tools" — brands want AI that acts, not just dashboards to look at
• Human-in-the-loop is non-negotiable — pure automation without human oversight erodes relationships
• Personalization is king — reference specific posts/content in outreach, not just "Hi {name}"
• India market opportunity — WhatsApp dominant, regional language support is a gap in existing tools
• Price sensitivity — smaller teams reject annual contracts and enterprise pricing; our ~$329/mo at 100 influencers is highly competitive vs. Grin ($$$) or CreatorIQ ($$$$)

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17. Appendix — Tech Lead Review

✅ Agreements (unchanged from v1)

🔄 Divergences (expanded from v1)

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18. Appendix — API Rate Limits

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19. Appendix — Glossary

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20. Next Steps

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This document is intended for internal review and scoping. Estimates subject to revision based on client requirements, API access timelines, and agreed scope.

End of Technical Design Document v2.0