Konstruct

What This Is

Konstruct is an AI workforce platform where SMBs subscribe to AI employees that communicate through familiar messaging channels — Slack and WhatsApp for v1. Clients get an AI worker that shows up where their team already communicates, requiring zero behavior change. Think "hire an AI department" rather than "subscribe to another SaaS dashboard."

Core Value

An AI employee that works in the channels your team already uses — no new tools to learn, no dashboards to check, just a capable coworker in Slack or WhatsApp.

Requirements

Validated

(None yet — ship to validate)

Active

Channel Gateway that normalizes messages from Slack and WhatsApp into a unified internal format
Single AI employee per tenant with configurable role, persona, and tools
Multi-tenant architecture with proper isolation (PostgreSQL RLS for Starter tier)
LLM backend pool with Ollama (local) + commercial APIs (Anthropic/OpenAI) via LiteLLM
Full admin portal (Next.js) for tenant management, agent configuration, and monitoring
Tenant onboarding flow in the portal
Billing integration (Stripe) for subscription management
Conversational memory (conversation history + vector search)
Tool framework for agent capabilities (registry, execution)
Rate limiting per tenant and per channel

Out of Scope

Multi-agent teams and coordinator pattern — v2 (need single agent working first)
AI company hierarchy (teams of teams) — v2+
Microsoft Teams, Mattermost, Rocket.Chat, Signal, Telegram — v2 channel expansion
BYO API key support — moved to v1 Phase 3 (operator requested during scoping)
Self-hosted deployment (Helm chart) — v2+ (SaaS-first for beta)
Voice/telephony channels — v3+
Agent marketplace / pre-built templates — v3+
SOC 2 / HIPAA compliance — post-revenue
White-labeling for agencies — future consideration

Context

Market gap: Existing AI tools are dashboards or chatbots, not channel-native workers. No coordinated AI teams. No self-hosted options for enterprises. Konstruct addresses all three.
Target customer: SMBs that need additional staff capacity but lack resources, are overwhelmed with processes, or want to grow faster but can't find the right balance.
Inspiration: paperclip.ing — but differentiated by channel-native presence, tiered multi-tenancy, and eventual BYO-model support.
V1 goal: Beta-ready product that can accept early users. One AI employee per tenant on Slack + WhatsApp, managed through a full admin portal, with multi-tenancy and billing.
Tech foundation: Python (FastAPI) backend, Next.js portal, PostgreSQL + Redis, Docker Compose for dev, monorepo structure.

Constraints

Tech stack: Python 3.12+ (FastAPI, SQLAlchemy 2.0, Pydantic v2), Next.js 14+ (App Router, shadcn/ui), PostgreSQL 16, Redis — as specified in CLAUDE.md
V1 channels: Slack (slack-bolt) + WhatsApp (Business Cloud API) only
LLM providers: Ollama (local) + Anthropic/OpenAI (commercial) via LiteLLM — no BYO in v1
Multi-tenancy: PostgreSQL RLS for v1 (Starter tier), schema isolation deferred to v2
Deployment: Docker Compose for dev, single-server deployment for beta — Kubernetes deferred

Key Decisions

Decision	Rationale	Outcome
Slack + WhatsApp for v1 channels	Slack = where SMB teams work, WhatsApp = massive business communication reach	— Pending
Single agent per tenant for v1	Prove the channel-native thesis before adding team complexity	— Pending
Full portal from day one	Beta users need a proper UI, not config files — lowers barrier to adoption	— Pending
Local + commercial LLMs	Ollama for dev/cheap tasks, commercial APIs for quality — balances cost and capability	— Pending
PostgreSQL RLS multi-tenancy	Simplest to start, sufficient for Starter tier, upgrade path to schema isolation exists	— Pending
Beta-ready as v1 target	Multi-tenancy + billing = can accept real users, not just demos	— Pending

Last updated: 2026-03-22 after initialization

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