Constructor Fabric Vision

NOTE: This document is a draft in terms of completeness - but can be used already to validate the rest of the artifacts

1. Open Design

Constructor Fabric is model-agnostic, tool-agnostic, and open.

Organizations retain ownership of their specifications, architecture, code, and operational knowledge while benefiting from AI-native software construction.

2. Why Fabric

A fabric is created by weaving many independent threads into one coherent structure.

Constructor Fabric weaves together:

  • Intent and strategy
  • Product discovery and requirements
  • Architecture and decisions
  • Code and reusable components
  • Validation and governance
  • Deployment and operations
  • Learning and optimization

Every artifact remains connected to its origin.
Every decision remains traceable.
Every improvement compounds.

3. Target Audience

3.1 Target Organizations/Departments/Functions

Constructor Fabric is designed to serve the following organizational units:

  1. Product Management
  2. R&D
  3. DevOps / Datacenter Operations (DCO)
  4. GTM (Sales & Marketing)
  5. Customer Success / Support

3.2 Target Roles

Constructor Fabric is intended to be used by the following groups of people:

  1. Product Management
    • VP of Product Management - who defines the product portfolio vision of the company
    • Product Owner / Product Manager - who is responsible for maximizing the value of a product by defining its vision, strategy, priorities, and outcomes.
    • Program Manager
    • UX/UI Designers - responsible for designing the user experience and user interface of software products to ensure they are intuitive, efficient, accessible, and aligned with user needs and business goals.
  2. R&D
    • Software Architects - responsible for defining and guiding the overall structure, design principles, and technical direction of a software system to ensure it meets functional, non-functional, and long-term scalability
    • Engineering Managers - responsible for delivery quality, traceability, and review readiness across teams.
      and maintainability requirements.
    • Developers who use AI coding tools daily and need a reliable way to keep requirements, design, and code aligned.
    • Quality Assurance (QA) engineers who oversee requirements, design, implementation, and tests to ensure changes remain verifiable, consistent, and review-ready.
  3. DevOps / DCO
    • DevOps Engineers /SRE who integrate validation and traceability checks into CI pipelines.
  4. GTM
    • CRO / CMO / VP of Sales / REgional GMs - track the performance of people
  5. External
    • Kit and extension authors who build reusable workflow packages, artifact kinds, and project rules for teams.

3.3 Target Companies

3.3.1 Product Type

Constructor Fabric is designed to serve the XaaS (Everything as a Service) space, including:

  • SaaS
  • PaaS
  • IaaS
  • AIaaS

3.3.2 Company Size

  • Medium-to large Service Providers with 2-5 developers
  • Larger software organizations with 20+ developers - Service Providers (SaaS, PaaS, IaaS, AIaaS), ISVs

3.4 Maturity Level

  • Greenfield - creation of a new software product or system from scratch without constraints imposed by existing code, architecture, data, or operational environments.
  • Brownfield - development, modernization, or extension of software within an existing system landscape while preserving compatibility with existing code, architecture, data, users, and business operations.

3.5 Codebase Size

Constructor Fabric must be affective for a wide variety of codebase sizes - from a single repository with dozens of files for one product and few thousands lines of code to a product portfolio spanning across hundreds of repositories, dozens of products and dozens of millions line of code.

3.6 Target Ecosystems

  1. XaaS startups and scale-ups building AI-native and cloud-native platforms
  2. Mature SaaS and XaaS providers scaling engineering, operations, and platform governance
  3. ISVs modernizing legacy software into operationally automated subscription platforms
  4. Open-source ecosystems extending commercial adoption and operational capabilities
  5. IDE, developer tooling, and AI-agent platforms embedding structured AI-native SDLC workflows and benchmarking

4. Product Development Lifecycle

Constructor Fabric must cover all phases of the software product development lifecycle:

4.1 Level 1: Three Major Phases

  1. Plan - Decide what should be built and why.
  2. Build - Design, implement, validate, and release the solution.
  3. Operate - Run the software, support users, learn from outcomes, and improve.

4.2 Level 2: Detailed Lifecycle

14 phases of software product lifecycle in one line:

Intent → Vision → Discovery → Strategy → Definition → Design → Construction → Validation → Release → Operation → Support → Intelligence → Optimization → Evolvution

4.2.1 PLAN

  1. Intent - Why does this product exist?
- Activities:

  - Opportunity identification
  - Market research
  - Customer segmentation
  - Business case
  - Intent formulation

- Outputs:

  - Strategic goals
  - Success metrics
  - Investment thesis
  1. Vision - What future are we trying to create?
- Outputs:

  - Product vision
  - Value proposition
  - Long-term direction
  1. Discovery - What problems should we solve?
- Activities:

  - Customer interviews
  - Problem validation
  - Competitive analysis
  - Solution exploration
  - User research

- Outputs:

  - Opportunities
  - Problem statements
  - Customer needs
  1. Strategy - How will we win?
- Activities:

  - Positioning
  - Packaging
  - Pricing
  - Roadmap themes
  - Business model
  - Competition
  - Go-to-market planning

- Outputs:

  - Product strategy
  - Roadmap themes
  1. Definition - What exactly should be built?
- Activities:

  - Requirements
  - User stories
  - Acceptance criteria
  - Prioritization

- Outputs:

  - Backlog
  - Product specifications

4.2.2 BUILD

  1. Design - How should the solution be structured?
- Activities:

  - System design & Architecture
  - Technology selection
  - Security design

- Outputs:

  - Architecture specifications
  1. Construction - Build the product.
- Activities:

  - Coding
  - AI-assisted development
  - Infrastructure definition

- Outputs:

  - Source code
  - Infrastructure code
  1. Validation - Does it work?
- Activities:
  - Testing
  - Security validation
  - Performance testing
  - Compliance checks

- Outputs:

  - Verified release candidates
  1. Release - Deliver to customers.
- Activities:

  - Deployment
  - Launch management
  - Rollout planning

- Outputs:

  - Production release

4.2.3 OPERATE

  1. Operations - Keep the system running efficiently
- Activities:

  - Monitoring
  - Incident response
  - Capacity management
  - SLO & error-budget management
  - Deployment & release ops (canary / blue-green / rollback)
  - Automated root-cause analysis

- Outputs:

  - Reliable service
  - Reliability dashboards (SLO burn, DORA)
  - Met SLAs
  1. Support - Help users succeed.
- Activities:

  - Customer support
  - Knowledge management
  - Case handling

- Outputs:

  - Resolved issues
  - Customer satisfaction
  1. Intelligence - Understand what is happening.
- Activities:

  - Analytics
  - Telemetry
  - Usage analysis

- Outputs:

  - Product insights
  - Business insights
  1. Optimization - Improve the product.
- Activities:

  - Feature enhancement
  - Performance tuning
  - Cost optimization

- Outputs:

  - Updated roadmap
  - Improvement initiatives
  1. Evolution - Determine what comes next.
- Activities:

  - Strategic review
  - New opportunities
  - Portfolio planning

- Outputs:

  - New vision
  - New discovery cycle

5. High-Level Scenarios

This section describes the high-level scenarios enabled by Constructor Fabric along with the deliverables for each of the ROles and Stages.

5.1 Constructor Fabric for Product Management

  1. Plan

    • Vision, requirements & UX design in one consistent workflow
    • Opportunity assessment - problem, target user, success metric before commit
    • Annual planning – GEO, segment, App - competitive research, pricing sensitivity, localized functionality, integrations, channel structure & margins, conversion funnel, payment methods
    • Competitive intelligence - product development trends, channel trends, pricing trends
    • Roadmap planning - desired functionality and resources

  2. Build

    • Vision traceability - resources committed per week/month against each vision pillar
    • Plan vs. actual - track R&D resources actually spent against the plan
    • Turn PRDs into (animated) backlog-ready stories & acceptance criteria
    • End-to-end traceability - spec → feature → delivery
    • Catch requirement gaps before engineering or market does
    • Instrument analytics at build time - every key flow tracked before launch

  3. Operate

    • Win/loss & funnel-conversion analysis - find the bottlenecks
    • Live product dashboards & experimentation from launch onward
    • Customer feedback – prioritized, traceable into requirements
    • Retention & expansion support - health scores, upsell paths
    • Flag product & scope drift across the portfolio
    • Outcome vs. spend - did the resources committed to each vision pillar pay off (ARR, adoption)

5.2 Constructor Fabric for Architects

  1. Plan

    • Architecture traceability - every requirement mapped to the design decision & component that satisfies it.
    • Encode senior-architect judgment into every design decision - even without a senior architect on the team.
    • Design the system to meet its requirements & constraints - (Architecture Decision Records) ADRs & service boundaries.
    • Design in non-functional requirements - security, scalability, performance, compliance.
    • Validate the design against requirements & constraints - catch gaps before build.
    • Technology selection - build vs. buy vs. reuse, with documented trade-offs.

  2. Build

    • Conformance - track what's actually built against the intended architecture, live
    • End-to-end traceability - requirement → architecture → code → test
    • Implementation review - prove the build hits its non-functional requirement targets in practice (throughput, latency, failover/failback, security), with recurring checks wired into CI
    • Enforce architecture rules as build-time guardrails
    • Assemble systems from proven, reusable modules (Gears)
    • Auto-generate the BOM - dependencies, licenses, provenance in one view
    • Instrument observability at build time - every service emits health & perf signals

  3. Operate

    • System health dashboards - uptime, latency, error rates against SLA
    • Enforce NFRs in production - security, scalability, perf budgets
    • Tech-debt surfacing & refactor prioritization from live signals
    • Flag cross-system architectural drift - keep one governed layer across teams
    • Planned vs. actual resource consumption - track each service's real compute/infra usage against plan, flag overruns
    • Incident → architecture feedback loop - production signals & postmortems feed design

5.3 Constructor Fabric for Engineering Management

  1. Plan

    • Scope decomposition - break down product requirement into features, user stories, technical tasks, dependencies, acceptance criteria
    • Effort estimation - given historical velocity, team composition, architecture complexity – provide the estimations and explain the assumptions behind each estimate and identify hidden work that teams often forget
    • Dependency & risk discovery - analyze requirements and identify hidden cross-team dependencies, security implications, compliance requirements, infrastructure changes, migration risks, ops impacts
    • Capacity & resource planning - whether the organization has sufficient resources and expertise, forecasting future needs, and evaluating delivery tradeoffs across multiple scenarios.

  2. Build

    • Risk and bottleneck predictions – analyze commit histories and PR cycle times to predict delayed milestone and flag developers experiencing fatigue or burnout
    • Insight - productivity measurement and improvements – constantly evaluate developers performance holistically – to identify process bottlenecks and propose the most efficient solution
    • Automatic status updates – through integration with all relevant systems – objectively analyze risks, delays, critical paths, release readiness

  3. Operate

    • Automated root-cause analysis – when a deployment fails or performance degrades – instantly and automatically cross-reference the incident with the recent code changes to locate the cause
    • Trend detection and anomaly surfacing - analyze ticket streams to detect emerging issues early, flag abnormal increases in specific categories, services, or customer segments before they become major incidents - shift from reactive to early-warning-driven.
    • Performance & cost optimization - identifies inefficiencies in production systems such as slow endpoints, over-provisioned resources, or expensive queries, and suggest optimizations to improve latency and reduce cloud spend.

5.4 Constructor Fabric for DevOps/SRE

  1. Plan

    • Define SLOs and error budgets up front – from product requirements
    • Capacity and cost planning - forecast infra needs and flag overruns before they're provisioned
    • Design the deployment topology - environments, regions, failover/DR, traceable to NFRs (availability, latency, RTO/RPO)
    • Golden paths - approved templates and platform self-service so product teams ship without deep ops knowledge
    • Security and compliance by design - secrets, network policy, threat surface, SBOM requirements decided before build
    • Encode senior-SRE judgment into every plan – even w/o a senior SRE on the team

  2. Build

    • Infrastructure as code from the spec - from the same model the app is built against
    • CI/CD guardrails - reliability, security, and policy checks wired in as pipeline gates
    • Assemble from proven SRE and Dev Kits and platform engines (Gears) - observability, deployment - out of the box
    • Instrument observability at build time – built-in metrics: usage patterns, health, costs and more
    • Safe deployments - canary/blue-green with automated rollback triggers tied to SLOs
    • End-to-end traceability - requirement to infra to deployment, so any change can be traced and verified

  3. Operate

    • Live reliability dashboards - SLO burn, error budgets, and DORA
    • Automated incident management
    • Automated root-cause analysis - cross-reference incidents with recent code and config changes to locate the cause instantly
    • Anomaly and early-warning detection
    • Performance and cost optimization
    • Incident to architecture feedback loop - postmortems and production signals feed back into the model
    • Planned vs actual - real compute/infra usage against plan, flag drift and overruns

5.5 Constructor Fabric for QA & Benchmarking

  1. Plan

    • Define quality bars and acceptance criteria
    • Test strategy from the spec - what to test and at which level (unit/integration/e2e) and on what configurations, risk-based prioritization
    • Define the benchmarks - which quality, delivery, and productivity metrics matter, how to measure them, and the baseline and competitors to compare against
    • Coverage and traceability targets - every requirement maps to the tests that verify it
    • Compliance and NFR test planning - security, performance, accessibility gates decided before build
    • Encode senior-QA judgment into the plan
    • Learn from production signals

  2. Build

    • Generate tests from specs - unit, integration, performance and other types of tests produced from the same model as the code
    • Quality gates in CI - block merges that miss the bar
    • Requirement to test to result traceability - prove each requirement is actually verified, both ways
    • Track AI-authored vs human code and its quality - flag where AI output needs more review
    • Regression testing wired in - catch fragile or under-tested code before it ships
    • Benchmark each build - quality and delivery metrics captured per build to compare over time and with competitors

  3. Operate

    • Quality dashboards - defect and escape rates, pass/fail, coverage trends over time and more
    • Define release readiness based on data not feelings
    • Run tests and analyze results and suggest fixes – fully automatically
    • Anomaly detection in quality signals - rising defect categories or flaky tests surfaced early
    • Production to test feedback loop - real incidents and escaped defects and production configurations feed back into the test suite
    • Optimize utilization and costs of environment used in tests (hardware, cloud resources and more)

6. Elements

Constructor Fabric consists today of 3 core elements:

  1. Constructor Studio - AI-native SDLC automation, traceability, and development governance. Transforms idea → specification → architecture → implementation → production.
  2. Constructor Gears - reusable OSS/BSS modules, platform engines, developer and SRE tooling
  3. Constructor Insight - operational intelligence, benchmarking, measurement, and analytics

It should be possible for any of these elements to be used independently or in combination.

When used in a combination - each of the elements should benefit from other elements and create a synergistic effect.

All 3 elements must work not only with Constructor Fabric stack - but with the other clouds as well - listed in the Integrations section below.

7. Integrations

Constructor Fabric's open philosophy assumes that one is not restricted by the selection of tools and therefore Constructor Fabric must have good integrations with a wide selection of software development tools and cloud service providers.

7.1 AI Authoring & Design

  • Anthropic (Claude / Claude Code) - registered in the gateway; agentic codegen in Studio
  • JetBrains (Junie) - golden-path codegen in IntelliJ; pluggable Studio agent
  • Microsoft (Copilot) - Studio surface in VS Code; Copilot via the gateway
  • Anysphere (Cursor) - AI editor; output wrapped in golden paths
  • OpenAI (Codex) - codegen agent, routed via the gateway
  • Zencoder (Zenflow and IDE Agents) - AI coding agent, pluggable in Studio
  • Cognition (Windsurf/Devin) - AI editor; pluggable agent via the gateway

7.2 Plan & Collaborate

  • Atlassian (Jira / Confluence) - plans to Jira, ADRs to Confluence; PRD→CODE traceability

7.3 Source & CI/CD

  • GitLab (+ CI + Runners) - CF drives MRs, pipelines & runners via API/webhooks; emits CI config
  • GitHub - CF drives the same flow via Actions + the Checks API
  • Jenkins - CF triggers & reads existing jobs; wraps pipelines, doesn't replace

7.4 Quality & Security Gates

  • Snyk - CF wires SCA into its NFR gates; findings feed the auto-SBOM
  • Also gates: SonarQube, Semgrep, Trivy, TruffleHog (SAST, image & secret scans)

7.5 Artifacts & Registry

  • JFrog - CF pushes artifacts to Artifactory; Xray scans feed BOM/SBOM
  • Also publishes to: Harbor, Nexus

7.6 GitOps & Runtime

  • Kubernetes - primary runtime; Gears via operators/Helm
  • ArgoCD - Studio emits manifests; GitOps delivery

7.7 IaC, Secrets & Signals

  • HashiCorp (Terraform + Vault) - IaC + secrets/PKI, as Gears modules
  • Ansible - VM provisioning & infra hardening
  • Observability (Prometheus++)→ Insight - metrics, logs & traces via OTLP

7.8 Clouds we run on

  • Hyperscalers (AWS·Azure·GCP) - any cloud; endpoints via gateway
  • NeoClouds (Nebius, CoreWeave, Crusoe, Lambda, FluidStack, Nscale) - GPU, cost-routed
  • Digital Ocean - lighter-weight target

8. Constructor Fabric Improves Total Cost of Ownership

  1. AI Cost budgeting - allow to set limits for token spending per user, per model, per task type, per agent
  2. Multi-model routing - send each task to the cheapest model that can do it; domain specialists, light-to-heavy cascades
    • Constructor Fabric must provide the default configuration for model routing - but allow a user to customize the routing (statically/dynamically) for herself, her team or for the whole organization.
    • Cheaper / local / open models + fallback-on-retry
  3. Prompt / prefix caching - cache the static prefix and codebase (cache reads ~0.1× input; 41–80% savings on agentic tasks)
  4. Context compression / compaction - shrink the window (20–40% fewer tokens)
  5. Batching, output caps, structured output - rein in output tokens (3–10× the cost of input)
  6. **Cost observability - track spend per user, per model, per task type, per agent. Alert on overruns.

9. Participation in Constructor Fabric

9.1 Use Constructor Fabric Start elements in your project share your feedback.

You can start using any element or any combination of elements of Constructor Fabric.

1. Install Constructor tool to your repositories
2. Use Constructor Gears as building blocks for your projects
3. Deploy Constructor Insight and see how AI affects your organization's productivity

9.2 Contribute to Constructor Fabric

1. Provide new skills, kits, and plugins for Constructor Studio tools
2. Improve Constructor Studio tools
3. Improve existing Constructor Gears
4. Develop new Constructor Gears from the Constructor Fabric roadmap
5. Propose and develop new Constructor Gears outside the roadmap
6. Implement integrations between Constructor Fabric and third-party systems

99. Unsorted Requirements

  • Ingress existing repository
  • Participating companies
  • Operational environment hosted by partners