Why 30 Days Is the Right Timeline
Six months is the default timeline for AI features. It's wrong. In six months, models improve, competitors ship, and your requirements change. The features that get shipped are the ones with aggressive timelines.
Thirty days is tight enough to force scoping discipline but long enough to build something production-worthy. We've used this framework across 15 SaaS engagements. It works — if you follow it strictly.
Week 0: Scoping — What to Build and What to Cut
Before the 30 days start, spend 2-3 days on scoping. This is the highest-leverage activity in the entire project.
The Scoping Framework
Answer these four questions:
1. What's the single most valuable AI feature for your users? Not the three most valuable. Not the platform. One feature. The one that would make users say "finally."
2. What's the minimum viable AI quality? Define "good enough" in concrete terms. For a support copilot: "Answers 70% of common questions correctly, with sources." For document extraction: "Extracts 90% of fields with 95% accuracy."
3. What can the AI feature NOT do (and that's OK)? Explicit limitations prevent scope creep. "The copilot doesn't handle billing questions — those go to a human." "The extraction doesn't handle handwritten forms."
4. What's the evaluation plan? Before building anything, define how you'll measure success. 50 test cases minimum. Automated evaluation where possible.
What to Cut
Cut everything that doesn't directly serve the core feature:
- •Custom training/fine-tuning: Use base models with good prompts. Fine-tuning is an optimization for later.
- •Multi-model evaluation: Pick one model (Claude Sonnet or GPT-4o). Optimize model selection after the feature ships.
- •Admin dashboards: Build monitoring, not dashboards. Dashboards are a week 5-8 project.
- •Multi-language support: Launch in English. Add languages after validating the feature works.
- •Advanced personalization: Start with one-size-fits-all. Personalize in v2.
Week 1: Architecture and Foundation
Day 1-2: Architecture Decision
Choose your integration pattern (Sidecar, Middleware, Embedded, or Orchestrator — see our integration patterns guide). For most first AI features, the Sidecar pattern is the right choice: lowest risk, fastest to build, easiest to modify.
Set up the LLM gateway with:
- •Model routing (even if you're using one model — you'll add a fallback later)
- •Basic rate limiting
- •Request/response logging
- •Cost tracking per request
Day 3-4: Data Pipeline
If your feature needs RAG (most do), set up the data pipeline:
- •Document ingestion for your content sources
- •Chunking with recursive strategy (512 tokens, 50 overlap)
- •Embedding with text-embedding-3-small
- •Vector storage with pgvector (or your existing Postgres)
Index your initial content. Run basic retrieval tests to verify relevance.
Day 5: Prompt Engineering V1
Write your initial system prompt and test it against 10-20 representative queries. Don't over-engineer — this prompt will change 5+ times before launch.
Focus on:
- •Clear role definition
- •Output format specification
- •Explicit constraints (what the AI should NOT do)
- •One good example of expected behavior
Week 2: Core AI Integration
Day 6-8: Feature Implementation
Build the core feature. This is the actual integration into your product:
- •API endpoint(s) for the AI feature
- •Frontend UI for the feature
- •Streaming response handling (if applicable)
- •Basic error states
Focus on the happy path first. Error handling comes in week 3.
Day 9-10: First Internal Testing
Deploy to a staging environment. Get 3-5 team members to use the feature for real tasks. Collect:
- •Screenshot/recording of every interaction
- •Quality rating (1-5) for each response
- •Notes on what surprised them (good or bad)
This feedback is gold. It reveals assumptions your prompts make that don't match real usage.
Week 3: Evaluation and Hardening
Day 11-13: Evaluation Pipeline
Build the automated evaluation:
- •Create a test dataset of 50+ query-expected answer pairs
- •Implement automated scoring (LLM-as-judge or custom metrics)
- •Run the evaluation and establish a baseline score
- •Set quality gates: the feature doesn't ship if the score drops below baseline
Day 14-15: Prompt Optimization
Using evaluation data from week 2 testing and the automated evaluation, optimize prompts:
- •Fix failure modes revealed by testing
- •Adjust tone and format based on user feedback
- •Add edge case handling for the most common errors
- •Re-run evaluation to verify improvement (not regression)
Day 16-17: Production Hardening
Implement the non-negotiable production requirements:
- •Fallback behavior (when the LLM is down or slow)
- •Timeout configuration (5 seconds for real-time, 30 for background)
- •Circuit breaker (trip after 5 consecutive failures)
- •Per-user rate limiting
- •Cost per request tracking
- •Input/output logging (sampled, PII-redacted)
Day 18-19: Security Review
- •Prompt injection testing (try to override the system prompt via user input)
- •Output filtering (ensure the AI doesn't return harmful content)
- •Permission checks (AI respects existing access controls)
- •PII handling (no unnecessary data sent to LLM providers)
Week 4: Production Deployment and Monitoring
Day 20-21: Feature Flag and Staging Deploy
Deploy behind a feature flag set to 0% (off). Verify:
- •The feature works end-to-end in production (not just staging)
- •Feature flag toggles correctly
- •Kill switch works (can disable instantly)
- •Fallback activates when LLM connection is killed
Day 22-23: Internal Rollout
Enable the feature flag for internal users (team only). Use it for real work for 2 days. Track:
- •Error rate
- •Latency distribution
- •Cost per interaction
- •Quality (internal team gives feedback)
Fix any issues found during internal rollout.
Day 24-25: Gradual External Rollout
- •Day 24: 5% of users. Monitor all metrics for 8 hours.
- •Day 25: 25% of users. Monitor for 12 hours.
Day 26-27: Full Rollout
- •Day 26: 50% of users. Monitor.
- •Day 27: 100% of users.
Day 28-30: Monitoring and Iteration
First three days at 100% are critical. Monitor:
- •Error rate (should be < 1%)
- •User feedback (thumbs up/down ratio)
- •Cost trajectory (is it within budget?)
- •Support tickets mentioning the AI feature
Fix any issues immediately. Start a backlog of v2 improvements based on user feedback.
Production Readiness Gates
The feature doesn't ship unless ALL of these pass:
- •Automated evaluation score ≥ baseline
- •p95 latency < 5 seconds
- •Error rate < 2%
- •Fallback tested and working
- •Circuit breaker tested and working
- •Feature flag and kill switch tested
- •Security review complete
- •Cost per request within budget
- •Monitoring dashboards live
- •On-call runbook documented
Post-Launch: The First 30 Days in Production
The feature is live, but you're not done. The first 30 days in production reveal everything your testing missed.
Week 1 post-launch: Monitor daily. Respond to every piece of negative user feedback personally. Track the top 5 failure modes.
Week 2 post-launch: Address the top 3 failure modes with prompt improvements. Re-run evaluation to verify improvements.
Week 3 post-launch: Review cost trajectory. Implement any needed cost optimizations (caching, model routing).
Week 4 post-launch: Compile a post-launch report: quality metrics, cost metrics, user feedback summary, and the v2 roadmap.
Conclusion
Thirty days is enough to ship a production AI feature — if you scope ruthlessly, build the right infrastructure in week 1, and don't skip the hardening in weeks 3-4.
The teams that fail at 30-day timelines always fail for the same reason: they spend 3 weeks building the happy path and 1 week scrambling on production concerns. Flip it: spend 1 week on the happy path and 3 weeks making it production-ready.