Spec to MVP in 6 weeks

Because we've already done it 16+ times for ourselves. Every product on AntigravityApps started as a problem we encountered, became an MVP in weeks, and got open-sourced or productized. We've turned that pattern into reusable substrate — auth scaffolding, IaC templates, AI integration patterns, observability defaults — that makes the build economical at a fixed price. We're not figuring it out on your budget. We're applying reps.

It also keeps us honest. Fixed price forces clarity on both sides: clear scope, clear deliverable, clear definition of done. Hourly billing rewards drift. We'd rather have the harder upfront conversation than the messier midway one.

Four weeks isn't enough to ship something production-grade with three real features. We've tried. You end up with a demo, not a system.

Twelve weeks is enough time for a project to lose focus, change scope twice, and forget what it was originally for. The constraint of six weeks is the point — it forces decisions, kills nice-to-haves, and produces something real.

If your idea genuinely needs more than six weeks, we'll tell you in the scoping call and either reshape the scope or recommend a different engagement model.

Freelancers are cheaper per hour but you're the integrator, the architect, the QA, and the PM. Most founders underestimate that cost.

Dev shops give you a team but typically run on time-and-materials, charge for ramp-up on every project, and don't have a reusable substrate, so week 1 is closer to zero than to 30%.

We're a small team of senior builders running a productized service. Same person who scopes it is the person building it. The substrate exists. The patterns are battle-tested. You get senior execution without the layers.

Vibe coding is great for prototypes. It struggles at production. The hard parts of a real system aren't the parts an LLM one-shots — they're the data model that won't paint you into a corner in month 3, the error handling that catches what you didn't think of, the security defaults, the deploy pipeline, the observability when something breaks at 2am.

There's also agentic technical debt — the cost-shape architectural choices an LLM has no opinion on because it sees the code, not the bill. We cover the specific failure modes earlier on this page (Lambda vs container, S3 vs EBS, event-driven vs polling, etc.), each typically a 10–100× cost decision.

We use the same AI tools you do — Claude, Google Antigravity, Codex, Cursor — applied where each is strongest, not all at once on every project. That's how we ship a production MVP in six weeks instead of six months. But we drive them with reps. You're not paying us to type. You're paying us to make the calls AI can't.

If you'd rather try it yourself first, do it. Some founders get a prototype that's "good enough" and never need us. The ones who hire us are the ones who tried and found the gap between "works in dev" and "works with real users" wider than they expected.

Because the AI doesn't know what to build, which trade-offs to make, what to skip, or how to wire the failure modes that matter. We do. The model is a tool, not a colleague.

AI made us faster, not free. We bring patterns about systems that don't fall over, the substrate (auth, IaC, AI, observability) behind 16+ live products, and judgment about what to build and what to leave out. AI compresses the typing. It doesn't compress the deciding.

The price reflects the outcome — a system that takes real traffic on day one — not the hours worked. If AI made the work zero-effort and the outcome reliable, the price would be lower. It isn't yet.

It means the MVP can take real traffic from real users on day one without falling over. Specifically:

• Authentication that handles real auth flows, not a hardcoded login
• A database with proper schema, migrations, and backups configured
• APIs with input validation, error handling, and rate limiting
• Infrastructure defined in code, deployed reproducibly
• Observability — logs, metrics, error tracking — so you can debug live issues
• Security defaults — HTTPS, secrets management, least-privilege IAM, no exposed credentials
• A CI/CD pipeline so you can ship changes without us

It does not mean SOC 2 certified, HIPAA audited, or load-tested for a million concurrent users. Those are separate engagements with separate budgets.

Honest answer: it depends — on what traffic patterns look like, what each request actually does, where the bottleneck lands first (database, AI calls, third-party APIs), and how concurrent users behave. There's no clean yes-or-no, and anyone who gives you one is selling something.

What the MVP gives you out of the box is a foundation that won't paint you into a corner: serverless compute (Lambda) that auto-scales horizontally without you touching anything, PostgreSQL on RDS that handles tens of millions of rows without architectural rewrites, stateless APIs, IaC, and observability — so when scale problems appear, you can see them and fix them surgically.

What's not in the six-week scope (this is where scaling work begins):

• Read replicas and database sharding
• Caching layers (Redis, CDN edge caching, query-level caching)
• Queue-based async processing for heavy workloads
• Multi-region failover and disaster recovery
• AI cost optimization at scale (token budgeting, batching, model routing)
• Query optimization and indexing strategy for hot paths
• Load testing and capacity planning

Those are scaling problems — the kind you want to have, because they mean you have real users. If you get there, we'd love to help. Scaling is one of the most satisfying engineering problems to solve. The right answer is almost never "pre-build for a million users on day one." It's "build a clean foundation, ship, watch what actually breaks, and fix that."

A feature is one primary user job-to-be-done with its own primary screen or flow and its own data model.

Examples that count as features: "user can upload a document and get an AI-extracted summary," "user can invite teammates and assign roles," "user can schedule a recurring report and receive it by email."

Examples that don't count as separate features (they're part of the foundation): authentication, user profile, basic settings, password reset, transactional emails.

We'll write down exactly what your three features are in the scoping call, with examples of what's in and what's out. That document is the contract.

Small changes — copy tweaks, a new field on a form, a different button placement — we absorb without fuss. That's normal.

Material scope changes — a new feature, a different data model, a pivot in direction — we'll pause, talk honestly about cost and timeline impact, and either re-scope formally or defer to a follow-up engagement. We won't quietly absorb scope and burn out by week 5, and we won't surprise you with an invoice.

Because doing both well in six weeks at a fixed price isn't honest. Mobile adds testing surface, build pipelines, and app store review cycles that double the operational overhead.

PWA covers most early-stage mobile needs — installable from a browser, works offline, push notifications, no app store gatekeeping. For most MVPs that's the right call. If you specifically need native (deep OS integration, app store presence, native performance), we'll quote it as an add-on or a follow-up engagement.

Default stack: TypeScript, Next.js, Node.js, PostgreSQL, SST on AWS, Bedrock or OpenRouter for AI. It's the stack we know cold and the one our substrate is built on.

You can request alternatives, but it'll change the price or the timeline (or both), because we'll be moving slower outside our defaults. Be honest with yourself about whether the stack preference is worth that trade-off. Most founders find that "boring, proven, fast to ship" beats "the framework I read about last week."

You can. Supabase + Vercel + Clerk + Resend + Posthog + a cron service — that stack ships fast and we'd recommend it if it's the right fit for your situation.

We default to AWS because:

• The substrate we've built — auth, IaC, AI integration, observability — lives on it. That's where the 6-week speed comes from.
• You own everything from day one. No vendor migrations later. No "we hit the free tier limit and our bill jumped 40x overnight."
• It scales without rewrites — same Lambda, RDS, and S3 from MVP to a million users. Tool-stitched stacks often force a rebuild when you outgrow them.
• Compliance and data-residency stories are cleaner — single account, single security boundary, all controlled by you.

For a pre-PMF tinkerer, free-tier stitching is fine and we'll tell you so in the scoping call. For something you intend to scale and own, AWS pays back the slightly higher floor cost ($20–$200/month) within the first year.

Yes to both. The GitHub repository is transferred to your organization on day one — you have full commit history and can audit progress live. The infrastructure runs in your AWS account, defined in code, fully yours. We don't host your MVP on our infrastructure and we don't gate access. If you fire us on day 30, you have everything.

One AI capability is included — RAG, document extraction, classification, agent loop, or voice. Built on Bedrock for sensitive data or OpenRouter for everything else, with proper guardrails, cost controls, and observability.

Model selection is part of the work — and it matters. The landscape has dozens of viable models now (Claude Opus/Sonnet/Haiku, GPT, Gemini, Qwen, Kimi, DeepSeek, Grok, Mistral, Nova, plus embedding, voice, and vision models) with very different cost, latency, context-window, and quality trade-offs. There's no one-size-fits-all. We pick per-feature, not per-project. A few examples of how the choice plays out:

• Classification at scale → small, cheap, fast models (Haiku, Nova Lite) — sometimes 50–100× cheaper than frontier models with comparable accuracy on narrow tasks
• Complex reasoning or agent loops → frontier models (Opus/Sonnet, GPT-5, Gemini 3) where judgment quality justifies the cost
• Document extraction → vision-capable models with structured output (Claude Sonnet, GPT) tuned for JSON-schema reliability
• RAG pipelines → a good embedding model (Titan, Cohere, or OpenAI) paired with a generation model that handles retrieved context well (not to forget the vector database and retrieval pipeline that goes with it)
• Voice → low-latency stacks (Deepgram for STT, ElevenLabs/Cartesia for TTS) when sub-second response matters
• Cost-sensitive workloads → smaller open-source models routed through Bedrock or OpenRouter, with quality monitored against a frontier baseline

You're not locked into one provider. Bedrock for anything sensitive — data stays in your AWS account, no third-party data sharing. OpenRouter for everything else, which keeps the door open to swap models as better or cheaper ones ship (and they will — the landscape moves every few weeks). We document the choice and the swap path, so if Claude 5 Haiku launches in month 4 at half the cost, you can route to it without a rewrite.

If you need multiple AI capabilities or something complex (multi-agent orchestration, custom model fine-tuning, real-time voice with sub-second latency), we'll scope it honestly in the call. Some of it fits in six weeks, some of it doesn't.

You get two weeks of bug-fix support — real bugs we introduced, fixed at no extra cost. Not new features, not changes you want, not "can we tweak this." Bugs.

After that, you have three options:

1. 1Take it in-house. You have the code, the runbook, and the handover doc. Your team takes it forward.
2. 2Hire us on retainer. Monthly engagement for continued development, on a separate agreement.
3. 3Walk away. No lock-in, no ongoing fees, no obligation. The MVP is yours.

Most clients pick option 1 or 2. Either works for us.

Led by Dhaval Nagar — AWS Serverless HERO, and long-time serverless developer — with senior builders from AppGambit and AntigravityApps. The same people who built the projects on the portfolio page. Not a junior team with senior oversight, not an offshored sub-team, not interchangeable resources. You'll know who's working on your project and you'll talk to them directly.

We're a small team by design. That's how we keep the substrate consistent and the quality high. You can audit our open-source work at github.com/AppGambitStudio before ever talking to us.

We'll tell you in the intro call. Six weeks at a fixed price doesn't work for everything, and we'd rather have a 15-minute conversation that ends with "this isn't for us" than a six-week engagement that ends with a bad MVP.

If we're not the right fit, we'll usually have a suggestion for who is — other builders, different engagement models, or a phased approach that gets you closer to ready.

The portfolio is the receipt. 16+ live products — most of them open-source, all of them inspectable. You can read the code, audit the architecture, and judge the quality before you ever talk to us. We've built this stuff. We've shipped it. We're not asking you to take it on faith.

Book a free 15-min intro call. We'll talk through what you want to build and whether 6 weeks is realistic for it. If we're a fit, we'll set up a 60-min scoping call as the first work of the engagement.