This is the reference our team works from. The same one we'd hand to a new senior engineer on day one. It's how we scope work, run tight build loops, review every line, and ship to production without standups. If you're evaluating Asyncdot, this is the honest version of what working with us looks like.
Frontend, backend, AI, infrastructure. All of it. Full applications, not demos. We pick the work where AI-native delivery has a clear advantage: anything net-new, anything pattern-heavy, anything where the hard part is getting the spec right and shipping it fast.
Full applications with auth, billing, dashboards, APIs, and an admin panel. Next.js, TypeScript, clean architecture, deployed to your infrastructure. Launch-ready, not just demo-ready. Average delivery: 5 days.
Cross-platform via React Native. Push notifications, offline cache, app-store-ready builds. We pair with a React-Native-shaped backend so your web and mobile share the same data layer.
Custom agents with RAG pipelines, tool calling, vector stores, and eval harnesses. We don't ship agents that haven't been tested against a representative dataset. Average delivery: 48 hours.
Internal automations that connect your systems and run on autopilot. We pick between custom scripts and LLM-orchestrated workflows, self-hosted on your infrastructure, based on what's actually maintainable for your team.
Custom admin panels, ops dashboards, and internal tools. We build these from components rather than reaching for Retool, easier to extend, no per-seat tax, and the source code lives in your repo.
New features for products you already have in production. We slot in async, ship behind feature flags, and don't touch anything we weren't asked to.
We don't run multi-month discovery phases. Most engagements go from "send us a paragraph" to "deployed to production" in under two weeks. Here's what actually happens at each step.
You write a paragraph or a Loom. We don't need a spec doc. A rough idea is enough. If you have wireframes or screenshots, send them. If you don't, we'll generate option sketches in the next step.
A senior engineer reads what you sent, asks the questions that matter (and only those), and writes a tight spec back to you. The spec is short: it names the surface area, the data model, the flows we'll ship, and the parts we're explicitly skipping. You approve or redirect in a Loom or a thread.
We work in 4-hour design-build-review cycles, not 2-week sprints. You see progress every day, not on demo Friday. Each loop ships a piece you can poke at on a preview URL: an auth flow, a dashboard screen, an agent endpoint. We learn what's wrong fast and redirect before the wrong thing compounds.
Every change is reviewed line-by-line by a senior engineer before it merges. Not a stamp; an actual review with comments, rejections, and rewrites. We test what's testable, eyeball what isn't, and don't ship code that hasn't been read by a human who understands the consequences.
Production deploys go through CI: type-check, lint, tests, build, deploy to your infra. We deploy to staging first, run a smoke check, then promote. We never deploy on a Friday afternoon. You get a release note with the URL, the changes, and the rollback command, not a status report.
On a Build subscription, the next request starts the same day the previous one ships. There's no "wrap-up" phase, no handover, no second SOW. You submit; we build; we deploy; you submit again. The system you bought is the loop, not a single deliverable.
Most teams treat AI as autocomplete. We treat it as a substrate. We've redesigned how specs get written, how decisions get made, how reviews close, and how validation happens, so the work that used to take a sprint takes an afternoon. Same senior judgment. Tighter loops.
Every shipped feature is also training data for the next one. We run a four-step loop on every build: plan, delegate, assess, codify. The first three are obvious. The last is where the gain compounds. After a build closes, we capture what went well and what didn't as artifacts the team (and the tools) can use on the next pass. A sub-agent for a recurring review pattern. A slash command for a repeated scaffold. A note in the project's memory for a constraint we keep rediscovering.
In traditional engineering, each feature makes the next one harder to build. Context piles up, the model in everyone's head gets more brittle, and the cost of a small change creeps. We invert that. Each feature we ship makes the next one easier, because the artifact left behind: a sub-agent, a scaffold, a memory note, absorbs the friction. The second feature ships faster than the first. The tenth ships faster than the second. The system isn't getting bigger; it's getting more legible to the people and tools building on top of it.
The most common AI-native fail mode is letting the model own the spec. We don't. A senior engineer reads what you sent, decides what's actually being built, and writes a spec tight enough that the next steps are mechanical. Drafts come back fast, but the shape of the build is owned by a human, every time.
We run it like a software factory: humans write the specs and the tests, and iterate on the bar until the work passes. The model compresses the path from intent to a reviewable draft, fast. The judgment about what the draft should be never leaves the engineer's desk.
When the cost of producing a working prototype drops to near-zero, decks die. We don't pitch options: we ship the option as a preview URL and let you poke at it. If a direction is wrong, you know within a day, not at a status meeting two weeks later.
Inside the team, this changes how decisions get made. New product directions get a 4-hour spike, not a 4-week debate. Shoot, fail, learn, redirect, much faster than thinking-by-Slack-thread.
A team's velocity is only as fast as its information flow. For a small team, that means everything load-bearing has to live in an artifact someone (or some process) can read. We default to written communication on the request board because written artifacts are the substrate the system can actually use. Meeting transcripts and Slack DMs aren't.
This isn't an accident of being remote. It's the architecture. If a decision lives only in someone's head, the loop can't compound on it. So we write it down, not for documentation, but for execution.
When something slows us down, we build a small tool to remove it: usually in days, sometimes hours. A review pile getting long? We build the agent that reads it first. A repeating spec pattern? A slash command that scaffolds it. The operating system improves as a side effect of running it, not as a separate "internal tooling roadmap."
The trade we're not making: we don't sell you those tools. The substrate stays internal. What you buy is the throughput it produces.
Early on we let each project dictate its own stack: a Vue app here, a Django site there, a separate React SPA with a Go API. Every one took longer than it should have. The problem wasn't the technologies. It was context-switching. So we standardized. One stack, every pattern dialed in. The result: faster builds, higher code quality, fewer surprises.
Next.js is our default for every SaaS product. Server components, file-based routing, edge runtime when it helps. TypeScript on everything. We don't ship JS without types. Tailwind for styling, no CSS-in-JS, no design-system framework on top. For mobile: React Native via Expo.
Node for most APIs. Python when there's heavy ML/data work. Postgres is the default database: proven, predictable, fast. For projects that need real-time data and a tight client/server contract, we reach for Convex. We avoid bespoke ORMs; Prisma or Drizzle handle it.
BetterAuth for authentication: the schema lives in your DB, the sessions are yours, no per-MAU vendor tax. Stripe for billing, wired the same way every time: subscriptions, customer portal, webhook handling, dunning. The recipe is consistent project-to-project.
Mastra is our agent framework: workflows, tools, memory, eval hooks. For RAG: pgvector or Pinecone depending on scale, and we're opinionated about chunking and retrieval evaluation. LiveKit when there's voice or video. Every agent ships with tracing turned on from day one.
Vercel for frontends and edge functions. AWS for heavier backend/data workloads. Cloudflare for DNS, R2, and Workers when they fit. Docker for anything we need to run anywhere. We don't configure servers by hand: managed services where they exist.
Sentry for errors. PostHog for product analytics. Logtail/BetterStack for log aggregation. Langfuse for agent traces and evals. Every project ships with these wired in: observability is not a post-launch upgrade.
Where it makes sense, we'll deviate: your existing stack always wins if you have one. We adapt; we don't insist. But for a greenfield project, this is what we reach for, and we know it cold.
We use GitHub for every project. The repo lives in your organization from day one. Never in ours. You own the source code outright, the commits, the issues, and the deploy keys. We add ourselves as collaborators and can be removed in one click when an engagement ends.
Trunk-based with short-lived feature branches. Pattern:
<engineer>/<ticket>-<short-title>. A
branch lives long enough to ship one piece of work: usually hours,
not days. PRs are small and focused, not omnibus.
Every PR is reviewed by a senior engineer who didn't write it. Required: a clear PR description, screenshots or a Loom for anything UI, type-checks passing, tests passing. Required for the reviewer: actually read the diff. Stamps don't count. We treat review as where quality is created, not certified.
Every push triggers CI: type-check, lint, unit tests, build. Every merge to main triggers a preview deploy you can hit on a URL. Failed CI blocks merge: no overrides, no "we'll fix it after." If CI is flaky, fixing CI is the next ticket, not "live with it."
We test where the cost of being wrong is high: auth, billing, data mutations, anything that touches money or sensitive data. We don't chase 100% coverage; we aim for "would this regression be caught?" and write the test that answers yes. For agents, we maintain an eval dataset and treat regressions in retrieval/grounding as first-class bugs.
Readable code is more important than clever code. We delete more than we add. We don't write comments that explain what the code does: only why a non-obvious decision was made. We don't introduce abstractions until we have three concrete uses. We don't add configuration for hypothetical future cases.
Secrets in environment variables, never in the repo. Rotated when engineers rotate. Dependencies kept current: Dependabot on, and we actually merge the PRs. Auth, sessions, and CSRF wired the same way every project. We don't roll our own crypto.
We're a fully async team. No daily standups, no recurring status meetings, no "got 15 minutes?" pings. The default is written communication on a request board you can read end-to-end at any time. Sync calls happen, but only when a decision is faster spoken than typed, and we keep them short.
Every engagement gets a request board (Linear or your choice) with clear states: requested → clarifying → in progress → in review → shipped. You see exactly what's active, what's queued, and what's waiting on you. No status emails.
New requests get a first response within 4 business hours. Active builds update on the board daily. If we ever need a clarification, we ask once with options, not three rounds of "any thoughts?".
Monday: you submit a request or two. Tuesday: clarification thread and a spec back to you. Wednesday: preview deploy of a working first slice. Thursday: review, redirect, polish. Friday: shipped to production, or staged for Monday if the change is risky. Repeat.
Three cases: kickoff (one 30-min call to align on goals); when a decision needs nuance that's hard to type ("which of these two product directions?"); and post-launch reviews, if you want one. Otherwise we stay async.
Plain language, no jargon. Loom for anything visual. We name tradeoffs explicitly: when we pick A over B, we say what B would have given you. We flag risks before they become problems. We don't say "we'll get back to you" without a date.
AI-native delivery is good for a specific shape of work. It's bad for others. Telling you up front saves both of us time.
We don't sell developer hours, embed engineers full-time on your team, or augment headcount by the seat. Our engineers stay on our operating system; that's where the speed comes from.
Multi-year migrations, legacy modernizations with deep organizational politics, anything that's primarily a change-management problem rather than an engineering one. Not our shape. There are excellent firms for this; we'll refer you.
If your scope is "rewrite this 2-million-line monolith," we'll decline. If it's "ship the next ten things on top of that monolith," we'll happily start tomorrow.
No billable hours. No timesheets. The fixed-price Launch tier and the monthly Build subscription are the entire pricing model. If you want hourly billing, we're a bad fit.
Build is month-to-month. Pause anytime; cancel anytime. If we're not earning the next month, you shouldn't be paying for it.
If your project requires something we don't know cold, we'll say that, and either learn it on our dime or refer you to someone who knows it cold already. We don't wing core technology choices on a client's bill.
No exceptions. No "this is a small change." Every line is read by a senior engineer before it lands in your repo. If we can't staff review on a build, we don't take it.