Case StudyAI Intent Detection

Lead Genat the Speed of Thought

Radar scans Reddit, X, and niche forums 24/7 using LLM-based intent analysis. We rebuilt the product from a costly prototype into a secure platform that finds real buyer intent—not just keyword mentions—and routes warm leads straight to sales within minutes.

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Social listening

24/7

Intent scoring

LLM

Lead alerts

<60 sec

Manual tagging

Zero

Live Intent Monitoring

LLM Scoring

Summarized pain points and buyer persona matching.

Instant Alerts

Real-time Slack & Email routing for warm leads.

"They built guardrails, payments, and UX faster than I could explain the next idea."

Next Idea Tech turned my hacked-together prototype into something investors and customers actually trust. They owned the UX, dev, and infra like an in-house team.

Leo F.

Founder, Radar

Platform Impact

Numbers That Make Radar Click

From vibe-coded prototype to enterprise-ready system. Every metric improved once we rebuilt the stack with senior LATAM engineers.

95%

Intent accuracy

LLM tuned on 2M+ labeled posts

1,200+

Signals per day

Across Reddit, X, Discord

<60s

Alert latency

Slack + email handoffs

100%

RBAC coverage

Audited roles & permissions

Auto

LLM Summaries

Render + Edge

Pipelines

50+

Seats onboarded

-40%

Token spend cut

Before We Joined

Intent Data Without Production Discipline

Radar's founder proved the concept: social intent data turns into hot leads. But the build was glued together—no auth, no payments, no secure pipelines. We stepped in with a dedicated LATAM squad covering UX, Nuxt + Next.js frontends, and backend services.

Why stabilization mattered

Stripe couldn't be enabled because session storage was insecure.
Lead data was stored in the browser, creating compliance nightmares.
Admins couldn't onboard teams—no RBAC or audit logging existed.

Half-baked prototype

The founder "vibe coded" an MVP, burning thousands in tokens with no guardrails or monitoring.

No data governance

OpenAI keys lived in env files, there was no RBAC, and personal Reddit cookies were hard-coded.

Runaway cloud bills

Each post triggered a new LLM call—even junk conversations—spiking spend by 40%.

Missing team

No UX designer, no backend unit tests, and no frontend system to ship paid tiers.

Intent Pipeline Snapshot

Nuxt ingestion → Render workers → Postgres features

fetchPosts()batch: 40

classifyIntent()llm:"gpt-4o-mini"

createLead()RBAC: sales, growth

Multi-tenant

Secrets vault

Stripe metering

What We Delivered

Production Rails for an AI Intent Engine

Our LATAM-led team brought the rigor Radar needed—user research, component libraries, secure backend patterns, and billing infrastructure—so the founder could sell confidently.

Stood up a dual Nuxt + Next.js architecture so marketing site and app share components

Designed a UX language that highlights AI intent cards, trust indicators, and CTA hierarchy

Implemented event-driven ingestion pipeline with batching to slash token costs

Added RBAC, audit logging, and encrypted secrets vaulting for compliance

Integrated Stripe for self-serve tiers with usage metering hooks

Built Render infrastructure-as-code so environments deploy in minutes

Key Capabilities

Built for Rev Teams Hungry for Signal

Every module we shipped helps Radar customers find intent, collaborate securely, and close faster.

Multi-channel listeners

Connect Reddit, X, Discord, Product Hunt, and custom RSS sources with throttled ingestion.

Workflow integrations

Push high-intent leads to HubSpot, Close, Slack, or Zapier with contextual summaries.

Alert rules

Build if/then rules for persona, TAM, or competitor mentions. Auto-assign owners.

Intent analytics

Dashboards highlight win themes, objections, and buying temperature over time.

Team RBAC

Sales, marketing, and founders each get scoped access. Audit trails for every action.

Secure data layer

PII encrypted at rest, SOC 2 aligned logging, and secrets managed centrally.

Usage-based billing

Stripe subscriptions, add-on packs, and metered API usage surfaced in-app.

Configurable AI

Tune prompts, model selection, and cost guardrails without redeploying.

Tech Stack

Modern Stack, Minimal Ops

The stack balances iteration speed with enterprise guardrails: strongly typed React front end, NestJS services, Prisma migrations, and automated schedulers keeping GenAI usage in check.

• CI/CD via GitHub Actions running vitest, lint, and Prisma checks.

• QStash orchestrates ingestion windows without manual cron work.

• Stripe webhooks and TanStack Query keep billing + UI perfectly in sync.

Frontend Experience

React 18 + Vite

Blazing-fast SPA with React Router

Tailwind + shadcn/ui

Composable design system

TanStack Query

Realtime intent feeds

React Hook Form + Zod

Typed lead capture

Backend & AI

NestJS

Modular API layer

PostgreSQL + Prisma

Multi-tenant storage

Google GenAI

Intent scoring + summaries

QStash

Scheduled scrapes & retries

Auth & Payments

Passport + JWT

Core authentication

Google OAuth

One-click onboarding

bcrypt

Secure credential hashing

Stripe

Tiered + usage billing

UX Enhancements

Framer Motion

Micro-interactions

Recharts

Intent analytics

date-fns

Timeline formatting

Sonner

Realtime toast alerts

Project Timeline

From Audit to Revenue in 10 Weeks

Tight sprints kept the founder selling while we rebuilt everything underneath.

Week 1

Product & data audit

Mapped ingestion scripts, LLM usage, and UX gaps. Defined guardrails and KPIs.

Week 2-3

Experience & architecture

Created intent card UX, billing journeys, and dual Nuxt/Next architecture diagrams.

Week 4-8

Full stack build

Async ingestion pipeline, RBAC, Stripe integration, and design system delivered sprintly.

Week 9

Hardening

Load tests, token-cost profiling, and chaos drills ensured stability.

Week 10

Launch

Migrated existing beta users, enabled paid tiers, and trained the sales team.

Week 11+

Optimize

Continuous tuning of models, prompts, and pricing experiments with feature flags.

"They built guardrails, payments, and UX faster than I could explain the next idea."

Next Idea Tech turned my hacked-together prototype into something investors and customers actually trust. They owned the UX, dev, and infra like an in-house team.

Leo F.

Founder, Radar

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