Two weeks. That's our target from first call to production deploy.
Not a demo. Not a "proof of concept." Production. Users hitting it. Money flowing.
Most agencies quote 3-6 months for AI projects. We think that's insane. In AI, if you're not shipping in days, you're already behind.
Here's exactly how we do it.
Days 1-2: Discovery and Architecture
Day 1: Understand the actual problem
We don't build what clients ask for. We build what they need.
Most founders come to us with a solution: "I want a chatbot that does X." We dig deeper. What's the business outcome? What's the user pain? What happens if we solve this problem 80% of the way?
That last question kills scope creep. Perfect is the enemy of shipped.
Day 2: Technical architecture
We decide on three things fast:
- Model strategy — Which LLM? Fine-tuned or prompted? One model or a router?
- Data pipeline — Where's the knowledge? How do we keep it fresh?
- Integration surface — API only? UI? Slack bot? Voice?
We document this in a 2-page architecture doc. Not a 50-page spec. If it takes more than 2 pages to explain, we're overbuilding.
Days 3-7: Core AI Pipeline + Integration
This is where the magic happens—and where most teams fail.
The trap: Developers spend weeks perfecting prompts for edge cases that represent 5% of usage.
Our approach: Ship the 80% case first. Hard.
We build the core pipeline:
- Retrieval system (if RAG)
- Agent orchestration (if multi-step)
- Base prompts with version control
- Eval framework to measure quality
The eval framework is non-negotiable. You can't improve what you can't measure. We test against real user scenarios, not synthetic benchmarks.
Cost controls from day one
AI features can blow budgets fast. We bake in:
- Token budgets per request
- Caching for repeated queries
- Model fallbacks (cheaper models for simple queries)
- Usage analytics dashboards
We've seen startups blow $10K/month on API costs because they didn't think about this upfront. Don't be that startup.
UI/Integration runs parallel
While the AI pipeline is being built, we're simultaneously building:
- React/Vue components with streaming
- Well-documented API endpoints
- User-facing interface with guided prompts
No waterfall. Everything moves together.
Days 8-10: Testing and Hardening
Red team your AI
Before launch, we try to break it:
- Prompt injection attempts
- Edge case queries
- High-volume stress tests
- Adversarial inputs
We find the failure modes and decide: fix, guard, or accept.
Some edge cases aren't worth fixing. If 0.1% of queries hit a weird edge case, a "I'm not sure about that" response is fine. Ship it.
Production observability
We set up:
- Request/response logging (anonymized)
- Error tracking with context
- Performance metrics
- Cost tracking per feature
When something breaks in production (and it will), we need to debug fast.
Days 11-14: Launch and Iterate
Soft launch to real users
Not beta testers. Real users with real problems.
We watch metrics obsessively:
- Task completion rates
- User retry patterns (sign of confusion)
- Error rates by category
- Cost per successful interaction
Iteration velocity
Post-launch, we can ship improvements in hours, not days. Because we built:
- Prompt versioning with A/B testing
- Feature flags for new capabilities
- Feedback loops to capture user corrections
Why This Works (And Why Others Take Longer)
We skip the meetings
No weekly status updates. No steering committees. Daily async updates, daily demos, direct Slack access to engineers.
We use boring technology
Our stack is deliberately unsexy:
- TypeScript everywhere
- PostgreSQL with pgvector
- Redis for caching
- Standard cloud infrastructure
No custom ML ops platforms. No Kubernetes clusters for a MVP. We'll add complexity when scale demands it.
We hire T-shaped engineers
Everyone on our team can:
- Write production code
- Understand LLM behavior
- Debug a production incident at 2am
We don't have "prompt engineers" separate from "backend engineers." That handoff kills velocity.
When This Doesn't Work
We'll be honest—fast AI development isn't for everyone.
Don't hire us if:
- You need regulatory compliance from day one (healthcare, finance)
- Your use case requires 99.9% accuracy
- You don't have clear success metrics
- Your org can't make decisions fast
For these cases, you need a slower, more methodical approach. That's fine. Just don't pretend you can move fast if you can't.
The Real Secret
Speed comes from saying no.
No to features that aren't core. No to perfect before shipped. No to stakeholder reviews that add weeks. No to building when buying works.
Every "no" is a day saved.
Let's Build Something
If you're raising in the next 6 months and need AI in your product—you don't have time to figure this out yourself.
We've shipped 11 AI products in the last year. We know what works.
Book 20 minutes with us. We'll tell you if we're a fit, and if we're not, we'll point you in the right direction.
No sales deck. Just real talk about what you need.
Next week: How we evaluate AI vendors (and why most of them are garbage)
Written by Dandelion Labs