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Ventaw vs Pairly vs CV Coach

The problem, what was built, the stack and the outcome, next to each other.

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In common

  • Shared stack: Next.js, FastAPI
  • Both kinds of work: Web
  • Order: CV Coach (2025 – present) → Pairly (Jul 2026 – present)

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Ventaw ›

Secure, isolated environments for developer workloads

Pairly ›

An AI-native operating system for founder-led teams

CV Coach ›

AI CV analysis, job matching and tailored applications

At a glance

Role
Founder / Lead Engineer
When
In progress
Length
—
Status
In progress
Type
Platform, Web
Role
Founder / Lead Engineer
When
Jul 2026 – present
Length
4 mo so far
Status
In progress
Type
AI, Web, Platform
Role
Founder / Lead Engineer
When
2025 – present
Length
1 yr 9 mo so far
Status
MVP
Type
AI, Web

The problem

Environment provisioning, session management and resource monitoring, designed for security, multi-tenancy and concurrent workloads.
Small teams stitch together disconnected tools for websites, customer communication, content, analytics and operations, while generic AI assistants lack safe access to business context and actions.
A Chrome extension captures job ads; a LangGraph workflow parses the CV and the ad, scores the match and drafts tailored cover letters and CV revisions.

What was built

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Pairly gives each business one governed platform where specialised agents can work across those functions. It combines tenant-scoped AI agents, websites and CMS, CRM and communications, newsletters, analytics, integrations and a skills marketplace.
A Chrome extension captures advertisements, stores them in PostgreSQL and queues analysis. The platform compares a CV with a job description, produces a structured match score and generates tailored cover letters and CV revisions.

Stack

  • Next.js
  • FastAPI
  • Docker
  • Python
  • FastAPI
  • PostgreSQL
  • Redis
  • Next.js
  • React
  • Azure
  • LangChain
  • Docker
  • FastAPI
  • Next.js
  • Supabase
  • LangGraph
  • LangChain
  • Celery

Highlights

  • Next.js management interface
  • FastAPI orchestration APIs
  • Tenant-scoped agent runtime with durable memory, model routing, typed tools and permission ceilings
  • Streaming agent UX with human approval gates, audit events and token budgets
  • Installable skills marketplace with automated validation and human publication review
  • End-to-end features spanning API, CMS, renderer, email and PDF output
  • LangGraph workflow with custom tools to parse, compare evidence and score the match
  • Asynchronous analysis pipeline

Infrastructure choices

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  • PostgreSQL
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Outcome

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A broad working platform with multi-tenant permissions, real-time agent execution, publishing and revision workflows, provider integrations and installable skills, with approval gates, audit events and human review for consequential AI actions.
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