The Decision Graph
Why engineering decisions disappear, and how we're building the missing layer between human intent and machine execution
Patent Pending · USPTO Application Filed 2025
The Problem Engineers Live With Daily
If you've worked at a scaling engineering organization - say, 100+ developers - you've seen this pattern:
Architectural decisions get made, documented, and then lost. A team debates using a particular technology stack, weighs the tradeoffs in Slack, documents the decision in a GitHub issue, and moves on. Six months later, someone on a different team proposes the exact same thing, unaware it was already decided against. The cycle repeats.
Research confirms what engineers experience daily: developers spend 35% of their time navigating and understanding existing code [1], and lose 17.3 hours per week to technical debt and maintenance [2]. Much of this isn't fixing bad code. It's reconstructing decisions that were already made.
The tools we use for software development track tasks and artifacts, not intent. Slack preserves conversations but can't structure them. Jira/Linear track tickets but not the reasoning behind them. GitHub shows what changed but not why it should have changed that way.
Then AI coding assistants arrived. Suddenly, code was being generated faster than humans could track the reasoning behind it. Without a system to capture organizational memory, AI became a force multiplier for mistakes.
This is Decision Amnesia: the structural loss of decision context that happens when engineering decisions scatter across tools designed for tasks, not intent.
Align™ is the infrastructure layer that solves this.
💡 The Core Insight
AI without organizational memory doesn't accelerate teams. It multiplies mistakes.
By 2027, AI will generate 50%+ of code [3]. Without a decision layer, engineering becomes chaos. AI suggests patterns you explicitly rejected. It violates constraints no one documented. It generates code faster than you can remember why that code shouldn't exist.
The Problem: Decision Amnesia at Scale
How Decisions Disappear
Consider a typical 100-engineer organization:
- 500+ decisions per week across architecture, features, and operations
- 15-25+ different tools across the SDLC where these decisions are made
- 0 systems that connect them into a coherent record
Critical engineering conversations happen in:
- Chat platforms (Slack, Teams, Discord)
- Issue trackers (Jira, Linear, GitHub Issues)
- Code review tools (GitHub, GitLab, Bitbucket)
- Design tools (Figma, Storybook, Miro)
- Data tools (dbt, Airflow, Looker)
- Incident management (PagerDuty, Opsgenie)
- Documentation (Notion, Confluence, Google Docs)
- Meetings with no written outcome
Once scattered, this knowledge becomes impossible to reconstruct. A single architectural decision might touch 5+ tools over weeks, with no single source of truth.
The Cost
For a 100-engineer organization, Decision Amnesia creates massive hidden costs:
- Duplicated reasoning: Same architectural debates repeated every 6-12 months
- Constraint violations: Engineers unknowingly building solutions that contradict past decisions
- Extended onboarding: New hires taking 6+ months to understand "why we do things this way"
- Audit friction: Compliance reviews taking weeks to reconstruct decision trails
- Production incidents: Features breaking because context was lost in translation
- AI misalignment: Generated code violating undocumented organizational constraints
25-35% of development time is spent searching for context that should already exist [1,2]
Not writing code. Not solving problems. Just reconstructing decisions that were already made.
What Needs to Exist
Engineering teams need a system that:
- Captures decisions where they happen (Slack, GitHub, Jira, meetings)
- Structures them automatically (what was decided, why, by whom, under what constraints)
- Links them bidirectionally (to tickets, PRs, incidents, deployments, other decisions)
- Surfaces them when relevant (during code review, incident response, planning)
- Detects conflicts in real-time (when new decisions contradict existing ones)
Not another wiki. Not another doc tool. A system built specifically for how engineering teams actually make decisions.
This is the Decision Graph.
How Align™ Works
1. Capture Decisions at the Source
Type /align in Slack during an architectural discussion. Mention @align in a GitHub PR where you're debating an approach. The system extracts the decision, context, participants, and outcome.
No context switching. No separate documentation step. Less than 5 seconds.
2. Structure with AI
LLMs extract structured Decision Snapshots from unstructured conversations:
- What was decided
- Why (goals, constraints, tradeoffs)
- Who decided (and who was affected)
- What alternatives were considered
- Links to related tickets, PRs, incidents, or other decisions
- Version history (how the decision evolved)
Engineers review and confirm. Align™ does the heavy lifting of structuring institutional knowledge.
3. Build the Graph
Decisions aren't isolated documents. They form a graph connected by:
- Semantic similarity (vector embeddings link conceptually related decisions)
- Temporal sequence (decisions ordered by creation and modification time)
- Hierarchical relationships (parent/child for decision decomposition)
- Artifact links (connections to tickets, PRs, incidents, deployments)
- Actor relationships (who made, approved, or was affected by decisions)
- Conflict edges (explicit links between contradictory decisions)
4. Surface at Decision Time
When a new decision is captured, the system:
- Checks for conflicts with existing decisions
- Surfaces related historical precedents
- Shows which teams will be affected
- Identifies constraint violations before they ship
When an incident happens, it links back to the architectural choices that contributed. When a new hire asks "why do we do X?", the answer is one search away.
Technical Architecture
System Architecture
┌─────────────────────────────────────────────────────────────┐
│ CAPTURE LAYER │
├─────────────────────────────────────────────────────────────┤
│ Slack GitHub Jira Linear PagerDuty Notion + growing │
│ Real-time event streaming · OAuth 2.0 · Webhook ingestion │
│ Bidirectional sync · Context-aware capture triggers │
└────────────────────┬────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ DECISION INTELLIGENCE ENGINE │
├─────────────────────────────────────────────────────────────┤
│ LLM-Powered Extraction · Semantic Analysis · Entity Graph │
│ Conflict Detection · Pattern Recognition · Impact Analysis │
│ Multi-modal embeddings · Temporal reasoning · Auto-linking │
│ │
│ [Patent-pending decision graph construction algorithms] │
└────────────────────┬────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ THE DECISION GRAPH (Core IP) │
├─────────────────────────────────────────────────────────────┤
│ Vector-Native Storage · Temporal Versioning · Event Store │
│ Graph Indexing (patent-pending) · Semantic Search Engine │
│ Bidirectional Traceability · Multi-tenant Isolation │
│ Sub-50ms similarity queries at 100K+ decision scale │
└────────────────────┬────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ SURFACE & INTEGRATE │
├─────────────────────────────────────────────────────────────┤
│ Interactive Web Dashboard · Timeline · Graph Explorer │
│ REST + GraphQL APIs · Real-time feeds · Search │
│ Programmable exports · Compliance reporting · Integrations │
│ Future: AI coding assistant context injection (roadmap) │
└─────────────────────────────────────────────────────────────┘
The Decision Snapshot
Each decision is captured in a structured Decision Snapshot:
// Core Decision
title, summary, decision_type, status
// Context
goals, constraints, alternatives_considered
risks, acceptance_criteria
// Actors & Teams
decided_by, stakeholders, affected_teams
// Traceability
source_tool, source_url
linked_artifacts (tickets, PRs, incidents, deployments)
parent_decisions, child_decisions
// Semantic Layer
embeddings (for similarity matching)
tags (auto-generated)
The complete system - including LLM extraction methods, graph indexing algorithms, conflict detection logic, and semantic analysis - is covered by our patent-pending architecture.
Deployment Models
Self-Hosted (Enterprise)
For organizations with strict security and compliance requirements (typically 100+ engineers):
- Kubernetes Helm chart for any cluster (EKS, GKE, AKS, on-prem)
- Your cloud, your data: All decision data stays in customer environment
- Model flexibility: OpenAI, Anthropic, Azure OpenAI, BYOK (bring your own key), or self-hosted OSS models
- Air-gapped support: Fully functional without internet access (OSS models)
- Zero data egress: Align Tech Ltd has no access to customer decision data - ever
- Your decisions train your models, not ours
Cloud SaaS (Team & Growth)
For smaller teams that need faster deployment and lower operational overhead (typically 5-100 engineers):
- Managed infrastructure: Fully managed by Align Tech Ltd
- Multi-tenant architecture: Logical isolation with row-level security
- Rapid deployment: Up and running in hours, not weeks
- Automatic updates: Always running the latest version
- Flexible pricing: Start small, scale as you grow
- Enterprise-grade security: Encrypted at rest and in transit, SOC 2 certification planned
Not an LLM wrapper. The Decision Graph's value is in the semantic indexing, conflict detection, and bidirectional traceability infrastructure - not the LLM choice. Models are swappable components.
Security & Compliance
- Authentication: OIDC/SAML SSO (Okta, Azure AD, Google)
- Authorization: RBAC with team-level and decision-type permissions
- Encryption: TLS 1.3 in transit, AES-256 at rest
- Audit logging: Immutable logs of all access and modifications
- Compliance roadmap: Architecture designed for SOC 2 Type II, ISO 27001, GDPR, HIPAA compliance
Scale Characteristics
- Vector similarity queries: Designed for sub-second response times at 100K+ decisions
- Write throughput: Architecture supports thousands of decisions per day
- Storage efficiency: Optimized for high-volume decision capture
- Horizontal scalability: Stateless services, autoscaling based on load
What This Solves
For Engineering Teams
- Stop re-debating the same decisions every quarter
- Onboard new engineers in days instead of months
- Prevent teams from building conflicting solutions
- Surface architectural context during code review
For Engineering Leaders
- See where decisions are being made and who's making them
- Detect architectural drift before it becomes technical debt
- Understand which constraints are slowing teams down
- Make better decisions with access to historical context
For Compliance & Audits
- Complete decision-to-implementation traceability (SOC 2, ISO 27001, FedRAMP)
- Immutable audit logs of who decided what and when
- Automated compliance report generation
- Reduce audit prep from weeks to hours
For AI-Assisted Development
- Feed Decision Graph context into AI coding assistants (roadmap)
- Prevent AI from suggesting rejected approaches
- Reduce review burden from constraint violations
- Adopt AI with confidence that it respects your decisions
Competitive Landscape
Why No One Has Built This Yet
- LLM extraction quality only viable in last 18 months. GPT-4 and Claude can reliably extract structured decisions from unstructured conversations. Earlier models couldn't.
- Vector databases became production-ready recently. Modern vector search technology enables sub-50ms similarity queries at scale. This wasn't possible 3 years ago.
- AI coding assistants created the governance crisis. Teams are deploying AI without organizational context. The pain is acute now.
- Integration complexity. Building bidirectional connectors to 10+ tools is hard. Most don't attempt it.
Adjacent Categories (Not Direct Competitors)
Documentation Tools (Notion, Confluence)
What they do: Store documents and wiki pages
What they don't do:
- Capture decisions at source (require manual documentation)
- Structure decisions into queryable formats
- Link decisions across tools and artifacts
- Detect conflicts or duplications
Why we win: We capture organically, structure automatically, link bidirectionally.
Issue Trackers (Jira, Linear)
What they do: Track tasks and tickets
What they don't do:
- Track the why behind tasks
- Relate decisions across different work items
- Surface historical context for current work
Why we win: We complement issue trackers by capturing the intent layer they miss.
AI Coding Tools (GitHub Copilot, Cursor)
What they do: Generate code from prompts
What they don't do:
- Understand organizational decision history
- Respect documented constraints and patterns
- Provide governance over generated code
Why we win: We're the governance layer AI coding tools desperately need (partnership opportunity).
Architecture Decision Records (ADRs)
What they are: Manual markdown files for documenting decisions
What they don't do:
- Automate capture (require manual writing)
- Provide semantic search
- Detect conflicts
- Link to SDLC artifacts
Why we win: We auto-generate ADRs PLUS provide semantic search, conflict detection, and traceability.
No existing tool creates a unified Decision Graph.
Align™ is category-defining. The Decision Graph is a new infrastructure layer, not a feature.
Market Opportunity
Total Addressable Market
Align™ sits at the intersection of multiple rapidly expanding categories:
- AI-assisted software development: $80B TAM with 30% CAGR [5]
- Developer productivity tools: $50B TAM [6]
- SDLC governance and compliance: $25B TAM [7]
Combined addressable market: $150B+
Target Customer Profile
Engineering organizations with:
- 50+ developers across multiple teams
- Using 6+ development tools (Slack, GitHub, Jira, etc.)
- Facing compliance requirements (SOC 2, ISO 27001, FedRAMP) OR scaling rapidly
- Deploying AI coding assistants
Estimated 25,000+ companies worldwide match this profile.
Market Drivers
- AI explosion: 50%+ code generation by 2027 requires governance [3]
- Remote work: Distributed teams lose context faster
- Compliance mandates: SOC 2, ISO 27001, FedRAMP require decision traceability
- Engineering scale: Teams growing 15-20% annually
- Platform engineering: Need for discoverable golden paths
Why Now?
Three technologies converged in the last 18 months:
- LLM extraction quality crossed the threshold for production use.
- Vector databases became production-ready.
- AI coding assistants created the governance crisis.
The Decision Graph was impossible to build well three years ago. Today, it's critical.
Business Model & Go-To-Market
Deployment Models
Align™ offers two deployment options to serve the full market:
- Cloud SaaS: Multi-tenant managed service for teams (5-100 engineers)
- Self-Hosted: Customer-controlled infrastructure for enterprise (100+ engineers)
Pricing Model
Cloud SaaS: Usage-based pricing scaled with engineering headcount.
Self-Hosted: Enterprise licensing based on team size, with tiered support and feature access.
Revenue Streams
- SaaS subscriptions: Monthly/annual recurring revenue from cloud customers
- Enterprise licenses: Multi-year contracts for self-hosted deployments
- Professional services: Implementation, custom connectors, training
- Premium support: SLA-backed support with dedicated success teams
Target Market
Primary customer segment: Fast-growing engineering organizations (50-500 developers) experiencing Decision Amnesia as they scale.
Ideal customer profile: Teams deploying AI coding assistants, scaling rapidly, or with distributed/remote engineering teams where context loss is acute.
Dual go-to-market approach enables both bottom-up adoption (SaaS) and top-down enterprise sales (self-hosted).
Go-To-Market Strategy
Phase 1: Design Partners & Dogfooding
- Internal dogfooding at Align Tech Ltd engineering team
- 5-10 early adopters for product validation (self-hosted deployments)
- Focus: Fast-growing engineering orgs (100+ engineers) experiencing Decision Amnesia pain
- YC portfolio companies as initial customers (shared context, network effects)
- Co-develop integrations and workflows
- Generate case studies and proof of value for enterprise sales
Phase 2: Cloud SaaS Launch
- Launch multi-tenant SaaS offering for teams (5-100 engineers)
- Self-serve deployment with credit card signup
- Marketplace presence (Slack App Directory, GitHub Marketplace)
- Freemium or trial model for product-led growth
- Developer evangelism (conference talks, technical content)
Phase 3: Scale Enterprise Sales
- Dedicated sales team for $100K+ self-hosted deals
- Target: Fortune 1000 engineering orgs
- Compliance and audit-focused messaging
- SaaS-to-Enterprise upgrade path for growing teams
- Strategic partnerships (Slack, GitHub, Atlassian)
Customer Acquisition Channels
- Developer evangelism: Conference talks, technical blog posts
- Content marketing: "Decision Amnesia" thought leadership
- Partnerships: Integrate with Slack, Jira, GitHub marketplaces
- Community: Decision architecture best practices community
- Direct outreach: VPs of Engineering at ICP companies
Competitive Moats
- Data moat: Decision Graphs become organizational memory, switching cost increases over time
- Network effects: More decisions leads to better similarity matching and higher value
- Integration moat: Deep bidirectional connectors are hard to replicate
- Patent protection: System and method of automated decision extraction, semantic indexing, and conflict detection
- Enterprise trust: Self-hosted model appeals to regulated industries
Product Roadmap & Vision
MVP (Current Focus)
- Decision capture via Slack, GitHub, Jira, Linear
- LLM-powered extraction and structuring
- Vector similarity search and conflict detection
- Web dashboard with timeline and graph explorer
- Kubernetes deployment (Helm chart)
Phase 2: AI Integration (6-12 months)
- AI context injection: Feed Decision Graph into Copilot/Cursor prompts
- Policy enforcement: Block PRs that violate documented constraints
- Smart recommendations: Surface relevant precedents during planning
- Automated ADR generation: Export to standard ADR format
Phase 3: Platform Expansion (12-24 months)
- Decision workflows: Approval chains for architectural changes
- Analytics layer: Decision velocity, conflict rates, team alignment metrics
- Connector SDK: Enable customers to build custom integrations
- Marketplace: Third-party connectors and extensions
- Mobile app: Decision capture on the go
The Vision
The Decision Graph will become essential infrastructure for engineering organizations.
Just like version control became essential for code, and observability became essential for production systems, the Decision Graph will become essential for engineering teams operating in the AI era.
We're not building a tool. We're building a new layer of the SDLC: the connective tissue between human decisions and machine execution.
In 5 years, every engineering organization with 20+ developers will have a Decision Graph. It will be as fundamental as GitHub, Jira, or Slack.
Align™ is building that future.
Current Status
- Product stage: Building MVP with focus on core decision capture and graph functionality
- Patent filed: USPTO application for the system and method of automated decision extraction, semantic indexing, and conflict detection using LLM-powered analysis and vector similarity search (2025)
- Trademark pending: Align™ registration in process
- Founder domain expertise: Direct experience with Decision Amnesia across multiple engineering organizations
Immediate focus: Complete MVP and validate with initial design partners. Seeking accelerator program (YC) to scale go-to-market and build connector ecosystem.
References
- Parnin, C. & Rugaber, S. (2011). "Programmer Information Needs After Memory Failure." Empirical study showing developers spend 35% of time navigating and understanding existing code.
- Stripe & Harris Poll (2018). "The Developer Coefficient." Reports developers spend 17.3 hours per week on technical debt and maintenance.
- McKinsey & Company (2023). "The Economic Potential of Generative AI." Projects 50%+ of code will be AI-generated by 2027.
- GitHub (2024). "GitHub Innovation Graph: AI-Powered Developer Productivity." 40%+ of code on GitHub now written with AI assistance.
- Gartner (2024). "Market Guide for AI-Assisted Software Engineering." $80B TAM projection with 30% CAGR.
- IDC (2023). "Worldwide Developer and DevOps Software Tools Forecast." $50B developer productivity tools market.
- MarketsandMarkets (2024). "GRC Platform Market by Component." $25B SDLC governance and compliance market.