AIPMO Package Offerings — Dual Lens

Same capabilities. Two framings. Left: how consultancies buy (project phases). Right: how product companies buy (continuous capabilities).

"Coding is commoditized. The 80% before build is where the value moved." — This thread, Jun 9 2026

SDLC Phase Gates

Linear phases → project outcome
Buyer: Consultancies, SIs, Enterprise PMOs

Product Development

Continuous loops → recurring capability
Buyer: SaaS, Platform Companies, Internal Product Orgs
0
Discovery & Business Case
✓ AIPMO Covered
PKG: Rapid Business Case Builder

One-time discovery engagement

Feed: exec transcripts, market data → Deliver: structured business case
  • EBITDA impact modeling (we did: 40%→80% margin shift)
  • Headcount leverage analysis (we did: 2,800 analyst baseline)
  • Revenue opportunity sizing with go/no-go recommendation
  • Competitive landscape positioning
📄 Deliverable: Business case with financial projections + EBITDA execution framework — delivered in hours
PKG: Product Strategy & Market Sizing

Always-on market intelligence

Recurring: product council inputs → continuous market fit analysis
  • Continuous TAM/SAM refinement as market data changes
  • Feature-to-revenue attribution modeling
  • Competitive positioning updates per release cycle
  • Portfolio opportunity scoring across product lines
🔄 Deliverable: Living product strategy dashboard — updates every cycle
1
Requirements & Analysis
✓ AIPMO Covered
PKG: AI Agent Requirements Engineering

Extract specs from stakeholders

Feed: recordings, docs, interviews → Deliver: agent-ready specs
  • Transcript-to-requirements extraction (we did: May 7 Krishna session)
  • Pre-filled questionnaires from existing context (we did: A.6 Vitals)
  • Gap analysis against target capabilities
  • 30-min validation replaces weeks of BA workshops
📄 Deliverable: Structured requirements spec + domain questionnaire — 30-min validation replaces weeks of workshops
PKG: Product Definition & Prioritization

Decide what to build next and why

Recurring: user feedback, usage data, strategy → prioritized backlog
  • Feature-level revenue impact scoring (we did: A.6-A.12 ranking)
  • Requirements extraction from customer interviews at scale
  • Cross-product dependency mapping for sequencing
  • Prioritization framework that compounds — not one-time ranking
🔄 Deliverable: Prioritized feature backlog with revenue justification — per sprint
2
Architecture & Technical Design
✓ AIPMO Covered
PKG: Platform Architecture Assessment

Evaluate reuse, estimate effort

Feed: codebase + target requirements → Deliver: reuse matrix + LOE
  • Code reuse analysis by layer (we did: Mythos 80% arch / 10% data / 0% domain = 40%)
  • Integration gap mapping against status (we did: TEK/APO inventory)
  • LOE breakdown by resource type (we did: AI vs. human SME vs. platform eng)
  • Risk-rated pre-build blocker analysis per deliverable
📄 Deliverable: Architecture assessment + reuse matrix + effort estimate
PKG: Platform Economics & Build/Buy Analysis

Shape platform vs. bespoke decisions

Per decision: platform state + target → build/buy/extend recommendation
  • Platform-vs-bespoke economics per feature (not just "can we reuse code?")
  • Integration investment analysis — which connectors unlock the most value?
  • Technical debt quantification tied to product velocity
  • Resource model that informs hiring, not just project staffing
🔄 Deliverable: Platform investment thesis — updated per product decision
3
Planning & Execution Readiness
✓ AIPMO Covered
PKG: Sprint Planning & Roadmap Generation

From backlog to execution plan

Feed: backlog + constraints → Deliver: release roadmap + sprint backlogs
  • Multi-track GANTT with dependencies (we did: 28 tasks, 4 teams, live dashboard)
  • Critical path analysis per sprint (we did: TEK-154 → TEK-95 ∥ TEK-106)
  • Resource constraint identification + mitigation plans
  • Cross-team dependency mapping with escalation triggers
📄 Deliverable: Release plan + sprint backlogs + live GANTT dashboard — in minutes
PKG: Capacity Optimization & Sprint Intelligence

Maximize value per engineering sprint

Continuous: capacity + priorities → optimal allocation each cycle
  • Sprint-over-sprint capacity allocation optimization
  • "Are we building the right thing?" analysis — not just "will we hit the date?"
  • Velocity trending + bottleneck detection across teams
  • Scenario modeling: what ships if we add/remove a resource?
🔄 Deliverable: Sprint intelligence brief — auto-generated every cycle
4
Delivery Management & Program Intelligence
✓ AIPMO Covered
PKG: Managed Delivery Intelligence

Continuous program tracking to completion

Ongoing: status updates, tickets, comms → Deliver: program view with change detection
  • Multi-source status aggregation (we did: PDF + Slack + GitHub + screenshots)
  • Change detection: "3 new items, 1 blocker resolved, critical path shifted"
  • Stakeholder briefing prep with positioning guidance
  • Blocker escalation with mitigation options
📄 Deliverable: Continuously updated program dashboard + weekly executive brief
PKG: Product Operations & Portfolio Intelligence

Run the product office, not just the project

Continuous: all product signals → portfolio health + trajectory
  • Cross-product portfolio health monitoring
  • Release readiness scoring — not just "tests pass" but "market-ready?"
  • Stakeholder communication automation for recurring updates
  • Product lifecycle intelligence: when to invest, sunset, or pivot
🔄 Deliverable: Product operations dashboard + automated stakeholder updates

Phases 5–6: Build, Test & Deploy — Not in AIPMO scope (yet)

~20% of SDLC effort. This is where coding agents, QA automation, and CI/CD live. AIPMO deliberately stops before build — because that's the commodity layer now. The 80% before build is where humans were spending months and millions. That's what we compress to days.

In a Product Development lens, this maps to Engineering Execution (build sprints) and Release & GTM (deployment, launch, adoption). Both are increasingly automated — and increasingly decoupled from the planning intelligence above.

SDLC Pitch
"We deliver Phases 0–4 in days. You only staff Phase 5 (build) and Phase 6 (deploy). That's 80% of the SDLC compressed by 100x."
Sells to: Deloitte, Accenture, KPMG, enterprise PMOs, SIs
Product Dev Pitch
"We make your product team 10x faster at deciding what to build, how to resource it, and whether it's working. Every sprint. Not a one-time engagement."
Sells to: SaaS companies, platform companies, internal product orgs, VCs evaluating portfolios

AIPMO Customer Engagement Process

How Warren guides a new customer through each phase. Deterministic steps always happen in order — they're the gates and guaranteed outputs. Probabilistic methods are how Warren adapts to the customer's specific context.

Design Principle: Deterministic Outcomes + Probabilistic Methods

Set deterministic triggers for OUTCOMES — what must be produced, what gates must be passed, what the customer receives. Let probabilistic capabilities figure out HOW — which questions to ask, how to extract insights, what patterns to surface from the customer's unique data. Not 100% scripted, not 100% freeform — the right mix.

Every phase has a required intake (what the customer must provide), deterministic steps (always execute, same sequence), probabilistic methods (Warren adapts to context), and a phase gate (customer sign-off before advancing).

0

Discovery & Business Case

⏱ 2–4 hours

🔒 Deterministic Steps (always happen)

  1. Intake checklist: Collect company overview, industry vertical, current pain points, strategic goals, existing tech stack, org chart of decision-makers
  2. Stakeholder recording: Record a 30–60 min discovery session with the executive sponsor. Transcribe.
  3. Business opportunity extraction: From transcript, produce a structured list of ≥3 business opportunities with revenue/cost impact estimates
  4. EBITDA impact model: For each opportunity, model the financial impact: headcount leverage, margin shift, time-to-value
  5. Competitive landscape scan: Position opportunities against industry benchmarks
  6. Business case document: Synthesize into a single deliverable with executive summary, financial projections, and prioritized opportunity ranking
  7. Customer review: Walk customer through the business case; capture corrections and refinements

🎯 Probabilistic Methods (Warren adapts)

  1. Question selection: Warren selects the highest-signal questions based on the customer's industry, size, and stated pain points — no two discovery sessions ask the same questions
  2. Pattern matching: Cross-reference the customer's situation against prior engagements (Deloitte, Kindo) to surface relevant analogies without leaking specifics
  3. Financial model calibration: Choose the right margin, headcount, and timeline assumptions based on the customer's vertical and scale
  4. Opportunity prioritization: Apply the three-filter lens — Exposed (gap that changes the dynamic), Unguarded (path of least resistance), Disproportionate (small input → large outcome)
  5. Missing data inference: When the customer can't provide a data point, Warren identifies the best proxy and flags it transparently as an assumption
Phase Gate
✅ Customer confirms or corrects business case → opportunity ranking agreed → top 1–3 opportunities selected for Phase 1
1

Requirements & Analysis

⏱ 4–8 hours

🔒 Deterministic Steps (always happen)

  1. Opportunity-to-capability mapping: For each selected opportunity, decompose into specific capabilities/features needed
  2. Existing asset audit: Customer provides access to existing docs, transcripts, runbooks, or recorded sessions. Warren ingests and indexes.
  3. Pre-filled requirements questionnaire: From ingested materials, Warren generates a questionnaire that's 60–80% pre-filled — customer validates rather than builds from scratch
  4. SME validation session: 30-min session per domain area. Warren leads with pre-filled answers; SME corrects/extends.
  5. Structured requirements spec: Produce a formal spec per capability: functional requirements, acceptance criteria, data sources, integration points, constraints
  6. Gap analysis: For each requirement, classify as: exists today, needs modification, needs net-new build, needs external dependency
  7. Requirements sign-off: Customer reviews and approves spec

🎯 Probabilistic Methods (Warren adapts)

  1. Transcript intelligence: Warren extracts implicit requirements from natural conversation — the things the SME said but didn't realize were requirements
  2. Domain knowledge application: Warren adjusts the questionnaire template based on the customer's domain (cybersecurity vs. finance vs. supply chain produce very different question trees)
  3. Ambiguity detection: Automatically flag requirements that are vague, contradictory, or missing measurable acceptance criteria — prompt the customer for clarification
  4. Cross-requirement dependency discovery: Surface hidden dependencies between requirements that the customer may not have identified
  5. Scope risk assessment: Identify requirements that are 5x harder than the customer thinks — flag before they become surprises in Phase 3
Phase Gate
✅ Requirements spec approved → gap analysis reviewed → scope agreed (what's in, what's deferred) → ready for architecture
2

Architecture & Technical Design

⏱ 4–8 hours

🔒 Deterministic Steps (always happen)

  1. Platform/codebase access: Customer provides access to existing repos, platforms, or architecture docs. Warren performs code/config analysis.
  2. Reuse matrix: For each requirement, evaluate existing assets by layer: architecture (%), data sources (%), domain logic (%), UI (%). Produce a weighted reuse score.
  3. Integration inventory: Map every external system/API/connector needed. For each: does it exist? In progress? Needs to be built? Blocked?
  4. LOE estimation: Per requirement, estimate effort in days broken down by resource type: AI-automatable, human SME, platform engineering, external dependency
  5. Risk assessment: Rate each component across 4 dimensions: technical complexity, integration risk, data access risk, timeline risk
  6. Architecture recommendation: Produce a technical design document with component diagram, integration architecture, and recommended tech stack
  7. Customer review: Walk through with customer's technical stakeholders; capture constraints and preferences

🎯 Probabilistic Methods (Warren adapts)

  1. Codebase pattern recognition: Warren identifies reusable patterns, anti-patterns, and technical debt in the customer's existing code that affect the build plan
  2. Integration complexity calibration: Based on the specific APIs/systems involved, Warren adjusts LOE estimates — a SailPoint integration isn't the same as a Jira integration
  3. Build vs. buy recommendations: For each component, Warren assesses whether building custom, extending existing, or buying off-the-shelf is optimal — using the customer's specific cost structure
  4. Risk pattern matching: Surface risks from analogous projects (e.g., "this is the same pattern as TEK-139 — blocked dependencies will stall your timeline unless you get evidence early")
  5. Resource model optimization: Given the customer's available team composition, suggest the most efficient allocation — not the ideal staffing plan
Phase Gate
✅ Architecture approved → LOE accepted → resource constraints identified → customer confirms available resources/timeline → ready for planning
3

Planning & Execution Readiness

⏱ 2–4 hours

🔒 Deterministic Steps (always happen)

  1. Release structure: Organize requirements into releases based on priority, dependencies, and customer timeline. Define release goals.
  2. Sprint backlog generation: Break each release into sprints (2-3 day cycles). Each sprint has: user-driven goal, task list, resource assignments, definition of done.
  3. Dependency GANTT: Produce a multi-track timeline with predecessor/successor relationships, parallel tracks, and milestones. Deploy as a live interactive dashboard.
  4. Critical path analysis: Identify the longest dependency chain per sprint. Flag single-thread blockers that jeopardize the release date.
  5. Blocker pre-assessment: For each blocker identified in Phase 2, produce a mitigation plan with owner, deadline, and escalation trigger.
  6. Resource allocation matrix: Map every sprint task to a named resource type with utilization percentages.
  7. Execution kickoff brief: One-page summary: what's being built, by whom, by when, what could go wrong, and who decides.

🎯 Probabilistic Methods (Warren adapts)

  1. Sprint sequencing optimization: Warren arranges sprint content to minimize idle time and maximize parallel execution based on actual resource availability
  2. Buffer calibration: Based on the risk profile from Phase 2, Warren adds appropriate buffer per sprint — high-risk items get more slack, proven patterns get less
  3. Scenario modeling: "What if this dependency slips 3 days?" Warren models cascade effects and presents alternatives
  4. Communication cadence recommendation: Based on team size and distribution, Warren recommends the right standup/review cadence — daily for high-risk sprints, async for stable ones
  5. Historical pattern application: Sprint velocity estimates calibrated against similar project profiles
Phase Gate
✅ Sprint plan approved → resource commitments confirmed → blocker owners assigned → live dashboard deployed → execution begins
4

Delivery Management & Program Intelligence

⏱ Continuous (ongoing)

🔒 Deterministic Steps (recurring cycle)

  1. Daily status ingestion: Warren monitors all configured sources (Slack, tickets, email, status docs) for status changes
  2. Change detection report: Every cycle: what's new, what moved, what's blocked, what resolved. With deltas from last cycle.
  3. Critical path update: Re-calculate critical path based on actual progress. Flag when the path shifts.
  4. Blocker escalation: Any blocker past its mitigation deadline triggers an automatic escalation with recommended action
  5. Stakeholder brief generation: Weekly executive brief: progress vs. plan, risks, decisions needed, next week outlook
  6. Sprint retrospective data: At sprint close: planned vs. actual, velocity metrics, blocker categories, improvement candidates
  7. Release readiness assessment: At release gate: all acceptance criteria checked, P1/P2 bugs enumerated, go/no-go recommendation with evidence

🎯 Probabilistic Methods (Warren adapts)

  1. Signal prioritization: From dozens of daily updates, Warren surfaces the 2–3 that actually matter to the customer's critical path
  2. Stakeholder communication tuning: Warren adjusts the brief format and detail level based on the audience — exec sponsor gets BLUF, tech lead gets specifics
  3. Risk trajectory modeling: Warren identifies when a "green" item is trending toward "red" before it officially changes status
  4. Cross-sprint learning: Warren applies lessons from Sprint N to improve accuracy in Sprint N+1 estimates and risk predictions
  5. Proactive re-planning: When progress data suggests the current plan won't land, Warren proposes re-scoping or re-sequencing before the customer asks
Phase Gate (per release)
✅ Release acceptance criteria met → customer sign-off → retrospective completed → learnings captured for next cycle
AIPMO Package Framework + Engagement Process — T&C Collaboration • Generated from live Kindo × Deloitte delivery thread, Jun 9 2026
Process designed for deterministic customer outcomes with probabilistic AI methods — ready for Codazen onboarding