StoAI

Methodology

30 days. Seven stages. Zero guesswork.

Every AI consulting engagement follows our battle-tested SHIP-7 framework. You know exactly what happens on each day, what you'll receive, and what risks we're mitigating — before we write a single line of code.

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Why a repeatable framework matters.

Most AI projects fail not because the technology doesn't work — but because the process is ad-hoc. Teams skip discovery, jump into implementation, and spend months fixing problems that a proper architecture phase would have prevented.

Our 7-stage framework eliminates this. Every stage has clear objectives, concrete deliverables, and built-in risk mitigation. It's the same process whether we're building an AI copilot, hardening an existing system, or automating document processing.

The 30-day timeline

D
TA
A
Implementation
Testing
De
H
Day 1Day 10Day 22Day 30
Stage 1
Days 1-3

Discovery

10% of engagement

Objectives

  • ✓Understand the business problem, not just the technical request
  • ✓Map the current workflow and identify where AI creates the highest impact
  • ✓Define measurable success criteria and acceptance benchmarks
  • ✓Align all stakeholders on scope, timeline, and expected outcomes

Activities

  • ›Stakeholder interviews (CTO, product, engineering, end users)
  • ›Current workflow analysis and pain point mapping
  • ›Data availability and quality assessment
  • ›Success metrics definition with baseline measurement
  • ›Competitive and prior art analysis

Deliverables

  • ■Discovery Report with business context and technical requirements
  • ■Success Criteria Document with measurable KPIs
  • ■Data Readiness Assessment
  • ■Stakeholder Alignment Summary (signed off)

Tools

Loom, Notion, Linear, custom intake questionnaire

Risks & mitigation

  • Unclear business objectives → Structured intake questionnaire completed before Day 1
  • Missing stakeholder buy-in → Require CTO sign-off on discovery report before proceeding
  • Insufficient data → Data readiness gate: if data quality is below threshold, we pause and advise
Stage 2
Days 4-6

Technical Audit

10% of engagement

Objectives

  • ✓Evaluate the existing codebase, infrastructure, and integration points
  • ✓Identify technical constraints, security requirements, and compliance needs
  • ✓Benchmark current system performance as a baseline for improvement
  • ✓Surface hidden technical debt that could block AI integration

Activities

  • ›Codebase review (architecture, patterns, tech debt)
  • ›Infrastructure audit (cloud, CI/CD, monitoring, security)
  • ›API and data pipeline assessment
  • ›Performance benchmarking (latency, throughput, error rates)
  • ›Security and compliance review (SOC2, GDPR, HIPAA as applicable)

Deliverables

  • ■Technical Audit Report (architecture, gaps, recommendations)
  • ■Infrastructure Readiness Scorecard
  • ■Performance Baseline Document
  • ■Security and Compliance Checklist

Tools

GitHub, SonarQube, Datadog/Grafana, AWS Well-Architected Tool, custom audit scripts

Risks & mitigation

  • Codebase too large to audit in 3 days → Focus on integration-relevant modules only, flag rest for future audit
  • No existing monitoring → Deploy lightweight observability in implementation phase
  • Compliance blockers discovered → Escalate immediately with mitigation plan, adjust scope if needed
Stage 3
Days 7-9

Architecture

10% of engagement

Objectives

  • ✓Design the AI integration architecture with production constraints in mind
  • ✓Select models, frameworks, and infrastructure based on requirements — not hype
  • ✓Define the data pipeline, prompt strategy, and evaluation approach
  • ✓Get architectural sign-off before writing any implementation code

Activities

  • ›Architecture design (system diagrams, data flow, integration points)
  • ›Model selection and evaluation (Claude, GPT, open-source, cost/performance trade-offs)
  • ›Prompt engineering strategy and template design
  • ›Fallback chain and error handling design
  • ›Cost modeling at 1x, 5x, and 10x scale
  • ›Architecture Decision Record (ADR) documentation

Deliverables

  • ■Architecture Decision Record (ADR) with rationale for every choice
  • ■System Architecture Diagram (C4 model)
  • ■Model Selection Report with benchmarks
  • ■Cost Projection Model (1x, 5x, 10x)
  • ■Prompt Strategy Document

Tools

Excalidraw, LangSmith/Braintrust for model eval, custom cost calculator, Notion ADR templates

Risks & mitigation

  • Wrong model selection → Run structured evaluation with 50+ test cases before committing
  • Over-engineering → Apply YAGNI principle: design for current requirements, document future extensibility
  • Cost estimates miss reality → Include 40% buffer in cost projections, validate with spike during implementation
Stage 4
Days 10-22

Implementation

43% of engagement

Objectives

  • ✓Build the AI feature in the client's codebase, not in isolation
  • ✓Follow the client's PR process, coding standards, and deployment pipeline
  • ✓Implement with production hardening from day one — not as an afterthought
  • ✓Maintain daily progress visibility through async updates

Activities

  • ›Core AI feature development (in client's repository)
  • ›Prompt engineering, iteration, and optimization
  • ›Integration with existing APIs, databases, and authentication
  • ›Error handling, fallback chains, and circuit breakers
  • ›Streaming response implementation (where applicable)
  • ›Daily async progress updates (Slack/Loom)
  • ›Mid-project check-in call (Day 16)

Deliverables

  • ■Production code in client repository (via PRs)
  • ■Prompt library with version control
  • ■Integration layer with error handling
  • ■Mid-project status report

Tools

Client's stack (Java/Python/Node.js/Go), OpenAI/Anthropic SDKs, pgvector, Redis, client's CI/CD pipeline

Risks & mitigation

  • Scope creep during implementation → Strict adherence to signed architecture document, change requests go through formal process
  • API rate limits or model degradation → Build multi-provider fallback chain from day one
  • Integration conflicts with existing code → Daily PRs with small, reviewable changes instead of large merges
  • Client team unavailable for reviews → Define review SLA in kickoff, escalate blockers within 24 hours
Stage 5
Days 23-26

Testing

13% of engagement

Objectives

  • ✓Validate AI quality with a structured evaluation suite, not manual spot-checking
  • ✓Load test under realistic conditions to verify performance at scale
  • ✓Run adversarial testing to find edge cases before users do
  • ✓Verify all acceptance criteria from the discovery phase are met

Activities

  • ›Evaluation suite creation (50-100+ test cases across categories)
  • ›Automated regression test pipeline
  • ›Load testing and latency profiling under production-like traffic
  • ›Adversarial and edge case testing (prompt injection, unexpected inputs)
  • ›Acceptance criteria validation against discovery document
  • ›User acceptance testing with client stakeholders

Deliverables

  • ■Evaluation Suite (50-100+ test cases with expected outputs)
  • ■Test Results Report with pass/fail rates per category
  • ■Load Test Report (throughput, latency at p50/p95/p99)
  • ■Adversarial Test Results with mitigations applied
  • ■Acceptance Criteria Sign-Off Document

Tools

Braintrust/LangSmith for eval, k6/Locust for load testing, custom adversarial test harness, pytest/Jest

Risks & mitigation

  • Evaluation shows quality below threshold → Built-in buffer days for prompt iteration and fixes
  • Performance degrades under load → Implement caching, streaming, and request queuing before deployment
  • Edge cases discovered late → Adversarial testing runs in parallel with functional testing from Day 23
Stage 6
Days 27-28

Deployment

7% of engagement

Objectives

  • ✓Deploy to production with full monitoring and observability from minute one
  • ✓Configure alerting rules for cost, latency, error rate, and quality drift
  • ✓Validate production behavior matches staging environment results
  • ✓Establish rollback procedures and incident response protocols

Activities

  • ›Production deployment via client's CI/CD pipeline
  • ›Monitoring dashboard setup (latency, cost, error rate, usage, quality metrics)
  • ›Alerting configuration (PagerDuty/Slack/email thresholds)
  • ›Feature flag or gradual rollout configuration
  • ›Rollback procedure verification
  • ›Production smoke tests

Deliverables

  • ■Production-deployed feature with monitoring
  • ■Monitoring Dashboards (4+ dashboards: performance, cost, quality, usage)
  • ■Alerting Configuration Document
  • ■Rollback Procedure Playbook
  • ■Incident Response Playbook for AI-specific failures

Tools

Datadog/Grafana/CloudWatch, PagerDuty/OpsGenie, LaunchDarkly/custom feature flags, client's CI/CD

Risks & mitigation

  • Production environment differs from staging → Deploy to staging-prod first, run full test suite before user traffic
  • Unexpected cost spike at scale → Per-request cost tracking with automated alerts at 80% of projected budget
  • Silent quality degradation → Automated quality sampling (5% of requests) with drift detection alerts
Stage 7
Days 29-30

Handoff

7% of engagement

Objectives

  • ✓Transfer complete ownership and operational knowledge to the client's team
  • ✓Ensure the client's engineers can maintain, modify, and extend the system independently
  • ✓Document everything — architecture decisions, operational procedures, and troubleshooting guides
  • ✓Establish the 30-day async support window for post-handoff questions

Activities

  • ›90-minute recorded knowledge transfer session with engineering team
  • ›Complete documentation review and walkthrough
  • ›Operational playbook review (monitoring, alerting, incident response)
  • ›Q&A session with engineering team
  • ›30-day async support kickoff (Slack channel or email)

Deliverables

  • ■Recorded Handoff Session (90 minutes, searchable, timestamped)
  • ■Complete Technical Documentation (architecture, code, prompts, evaluation)
  • ■Operational Runbook (monitoring, alerting, incident response, cost management)
  • ■Maintenance Guide (how to update prompts, retrain evaluations, scale infrastructure)
  • ■30-Day Async Support Agreement

Tools

Loom for recording, Notion/Confluence for docs, Slack for async support, GitHub for code documentation

Risks & mitigation

  • Knowledge gaps in client team → Recorded session enables async re-learning, documentation covers all operational scenarios
  • Issues discovered after handoff → 30-day async support included, critical issues addressed within 24 hours
  • Team turnover post-handoff → All documentation is self-contained and doesn't require tribal knowledge

Our guarantee.

Every engagement follows this exact framework. No shortcuts. No skipped stages. If we can't meet the 30-day timeline for your project, we'll tell you before we start — not after.

Fixed scope. Fixed price. Fixed timeline. The risk is on us, not on you.

Ready to see how this framework applies to your project?

Book a 30-minute technical assessment. We'll walk through your architecture, identify where AI fits, and show you exactly how the 30-day framework maps to your specific requirements.

Book a Technical Assessment

No commitment. You'll talk directly to the engineer who'll run the engagement.