How We Build

Our Technology

We're opinionated about how to build. TypeScript everywhere. Edge-first deployment. AI as a first-class citizen.

Engineering Principles

TypeScript-First

End-to-end type safety from database schema to UI. We use TypeScript in every layer — no "we'll add types later" technical debt.

const engine: AIEngine = new AisoEngine({
  model: "claude-opus-4",
  tools: [scout, closer, strategist]
});

Edge-Native Deployment

Cloudflare Workers and Pages for latency that's invisible. Static where possible, edge functions where dynamic behavior is needed.

export const onRequest: PagesFunction = async (ctx) => {
  const data = await ctx.env.DB.prepare(...)
  return Response.json(data);
};

AI as Infrastructure

We treat LLMs like any other infrastructure component — with proper error handling, retry logic, cost management, and observability.

const result = await withRetry(
  () => claude.messages.create({...}),
  { maxAttempts: 3, backoff: "exponential" }
);

Observable by Default

Structured logging, error tracking, and performance monitoring baked in from day one. We know when things break before clients do.

logger.info("lead.scored", {
  leadId, score, signals,
  latency: Date.now() - start
});

Platform Capabilities

Multi-Agent Orchestration

Specialized agent workers (scout, closer, strategist)
Human-in-the-loop approval flows
Persistent action queues with retry
Agent-to-agent delegation and handoffs

AI Integration Layer

Anthropic Claude (Opus, Sonnet, Haiku)
OpenAI GPT-4 and embeddings
Structured output extraction
Cost tracking and rate limiting

Data & Storage

PostgreSQL with Drizzle ORM
Cloudflare D1 for edge SQLite
Redis for caching and queues
Sanity CMS for structured content

External Integrations

Stripe (billing, subscriptions, dunning)
SendGrid / Resend (transactional email)
LinkedIn and Apollo (lead enrichment)
Cloudflare (DNS, pages, workers, R2)

Want this kind of engineering on your project?

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