ADDOM
ADDOM is a local-first desktop workspace that bundles multi-provider LLM chat, an integrated code editor, terminal sessions, source-control inspection, and delegated agent roles into a single project-anchored interface. Developers select which LLM provider and model to use per thread, eliminating the need to switch between separate chat applications. The workspace stores all projects, credentials, and conversation history locally on the device, avoiding cloud lock-in. Agent roles let teams define bounded tasks with specific permissions and working scope, which ADDOM routes autonomously and returns results to the parent workflow. File-change review and execution evidence surfaces provide audit trails for code generated by agents or LLMs.
ADDOM is a local-first desktop workspace, integrating with OpenAI, Anthropic, Google and OpenRouter. InnovaAI rates it 4.2 of 10 for agency adoption, best for Development Team Lead, Senior Developer and Project Manager roles.
Agency Audit
ADDOM is a local-first desktop workspace that consolidates AI-assisted coding, multi-provider LLM access, and delegated agent roles into a single project-aware environment. Development teams at software agencies benefit most, since they can iterate on client code without cloud lock-in, switch between OpenAI, Anthropic, Google, and OpenRouter models per task, and maintain full audit trails of agent decisions. The tool trades off breadth for control: it's built for teams that write code regularly and need transparency over convenience.
5recommended
25/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Development Team Lead handling multi-model code iteration and comparison
- Senior Developer handling delegated coding task execution and review
- Project Manager handling agent decision audit and transparency
- Your agency does not write code in-house or outsources all development to external contractors. ADDOM's value is internal to coding workflows; non-technical roles gain no direct benefit.
- Your team works exclusively with a single LLM provider and has no need to compare model outputs. ADDOM's multi-provider architecture adds complexity without payoff.
- Your developers are already embedded in an IDE-native AI coding assistant (GitHub Copilot, JetBrains AI) and rarely need to switch models. ADDOM is a separate workspace, not an IDE plugin.
Internal Adoption Path
No paid plan published
25 hr/mo
5 seats × 5 hr each
$1,875/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of ADDOM
Multi-provider chat in one workspace
Switch between OpenAI, Anthropic, Google, and OpenRouter models per thread without leaving the application. Development teams eliminate tab-switching overhead when comparing model outputs on the same code problem.
Project-aware integrated editor
Edit code, run terminal sessions, and inspect source control without moving context outside the workspace. Developers keep implementation evidence and conversation history attached to the same project thread.
Delegated agent roles with bounded scope
Define specialist agent roles with their own provider, model, and working permissions. Project Managers route specific tasks (test generation, refactoring, documentation) to agents and review results in the parent workflow, reducing manual code review cycles.
Permission modes and tool approvals
Configure which actions agents can execute autonomously and which require human review. Founders and Tech Leads maintain audit trails of consequential decisions without sacrificing iteration speed.
Local-first project storage
All projects, threads, and credentials remain on-device. Development teams avoid cloud lock-in and retain full control over code and LLM request history.
File-change review and artifact history
Inspect diffs and trace which conversation thread produced which code changes. Developers and code reviewers reduce time spent reconstructing the reasoning behind agent-generated code.
What Makes ADDOM Different
Unique advantages vs similar tools in this niche
Local-first architecture keeps all project data on-device
vs Cloud-based AI coding tools like GitHub Copilot or CursorProjects, settings, and continuity remain anchored to the device, with no telemetry or developer data collection.
Multi-provider support without vendor lock-in
vs Single-provider tools like GitHub Copilot (OpenAI-only)Use OpenAI, Anthropic, Google, OpenRouter, and other providers from a common workspace.
Guarded tool execution with approvals and evidence
vs Unrestricted AI coding agentsPermission modes and tool approvals keep machine-side work inspectable.
Value Equation
Outcome-likelihood-time-effort assessment for ADDOM
Value math requires real pricing
The Value Equation (dream outcome × likelihood ÷ time × effort) feeds directly into ROI math. ADDOM has no published pricing, so we hold this section until real numbers are available.
Contact ADDOMPricing
Pricing data not yet available for ADDOM.
Reality Check
ADDOM is alpha-stage software (v0.1.0) maintained by a single developer, so production stability and feature velocity are not guaranteed. Adoption requires team members to adopt a new desktop workspace and learn agent-delegation patterns; there is no web version or mobile access. Best ROI emerges only if your agency's development workflow centers on iterative coding with multiple LLM providers.
Moderate effort: standard configuration with some customization needed
How This Accelerates White-Label Services
Who It's For
- ✓software-development-agencies
- ✓freelance-developers
- ✓ai-assisted-coding-teams
Acceleration Steps
- 1Create your account and complete setup wizard
- 2Configure chat with multiple llm providers in one desktop workspace
- 3Connect OpenAI
- 4Launch your first client project
Academy for ADDOM
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
ADDOM Agency Implementation, Delivering AI-Assisted Code Projects at Scale
Learn how to structure client projects around ADDOM's multi-provider chat, integrated editor, and delegated agent roles to deliver faster code work and audit-ready artifacts. This course covers workspace setup, agent role configuration, permission scoping, and productizing code delivery as retainers with measurable output tracking.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Core concepts
The mental model you need to price and scope the work.
- Scaffold Versus Steward SplitConcept
AI code tools compress the cost of producing a first working version, but they do not compress the cost of owning it. The Scaffold Versus Steward Split separates two jobs agencies have historically billed as one: scaffolding (generating a full-stack app, wiring auth and payments, standing up a prototype) and stewardship (architecture decisions, review, and keeping consumer code in sync when contracts change). Scaffolding is where the speed advantage lives. Stewardship is where retainer value and liability live. Verdent illustrates the first half, turning a plain-English description into a working product with authentication, payments, and an admin dashboard. Ripple illustrates the second, detecting a breaking API change and opening pull requests across every affected repository. Agencies that price both halves at the same rate give away the margin on scaffolding and undercharge for stewardship. Split the two in your statements of work, and staff them differently.
- Agent Output Liability LineConcept
The Agent Output Liability Line is the boundary between code an agency can ship under its own name and code that still needs a human signature. Agentic tools now generate full-stack applications from prompts and auto-fix breaking API changes across repositories, which moves the liability question from 'did we write it' to 'who reviewed it.' Agencies that blur this line absorb client risk without pricing it. A practical test: if an agent opens a pull request at 2am and no human reads the diff before merge, that code sits above the line and belongs in a maintenance retainer, not a fixed-scope build. Tools like Agent Monitor and Agent Desk exist specifically to make that review boundary visible by surfacing per-file risk levels and session histories. The framework matters because delivery speed without a defined liability line converts margin into unpaid warranty work.
- Verification Debt CompoundingConcept
Verification Debt Compounding treats every unreviewed agent-generated commit as a small loan against future delivery capacity. The interest rate is set by how far the code drifts from the architecture a human would have chosen. Agencies that ship agent output straight to client repos accumulate this debt quietly, then pay it back in emergency debugging during a retainer sprint. The category description warns that over-reliance risks code quality inconsistency, and the operational evidence supports it: Agent Desk launched as an open-source macOS app specifically so teams can browse and review sessions across Claude Code, Cursor, Codex, and GitHub Copilot, which only matters if review is a scheduled step rather than an afterthought. A 10 MB in-browser model from GenClass now flags silent CSS and race-condition bugs that never throw errors, the exact class of defect that passes agent self-tests and lands in production. Budget review time as a line item, not a contingency.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- When Agent Output Ships Unreviewed, Cap the Blast Radius FirstEvaluation Rule
Set a hard blast-radius limit per agent run, then require a named human reviewer before any change merges into a client branch.
- AI Code Tools Rule: Price the Maintenance Tail Before the ScaffoldEvaluation Rule
Before committing a client retainer to AI-generated code, estimate the 12-month maintenance cost of that code and confirm the tooling or team can absorb it.
- AI Code Tools Decision: Scaffold-and-Review vs Autonomous Agent DeliveryDecision Framework
IF your delivery model bills fixed-scope builds on 4 to 8 week timelines and your senior engineers can absorb review load, THEN use AI code tools for scaffolding and maintenance while keeping architectural sign-off human. IF your margin depends on autonomous agents shipping unattended across client repos, THEN you need session-level audit tooling and a named fallback model before you take the retainer.
- The Silent Regression Trap: Why AI Code Tools Pass Review and Fail in ProductionFailure Pattern
- The Scaffold-Without-Owner Trap: Why AI Code Tools Stall After the First SprintFailure Pattern
Delivery system
Blueprints and procedures for running it as a service.
- Agentic Code Delivery Pilot (10-15 days)Implementation Blueprint
A fixed-scope pilot that puts AI code tools behind human architectural review on one live client codebase, so an agency can quote faster delivery without betting a retainer on unreviewed output.
- Agent Output Review Gate (QA)Operating Procedure
- Agent Session Audit and Cost Reconciliation (Retention)Operating Procedure
- Scaffold-to-Production Promotion Gate (Delivery)Operating Procedure
13 modules selected for ADDOM
Frequently Asked Questions
Answers about pricing, setup
ADDOM is a desktop workspace that combines chat with multiple LLM providers, an integrated code editor, terminal sessions, and delegated agent roles into one project-anchored environment. Development teams use it to iterate on code with full control over which model to use per task, review agent decisions with execution evidence, and avoid cloud lock-in. All projects and credentials remain local to the device.
ADDOM is open-source and free to download. The current release (v0.1.0-alpha) is available as a packaged Windows executable or as source code on GitHub under the MIT license.
Development team leads and senior developers benefit most, since they can audit agent work and switch models per task. Project Managers gain value from delegated agent roles, which reduce code-review overhead by defining bounded scopes upfront. Founders and Tech Leads benefit from permission modes and execution evidence, which provide transparency without sacrificing iteration speed.
Conservative estimate: 4-6 hours per developer per month on context-switching and manual code-snippet management, assuming the team currently uses 2+ LLM providers and switches between them 3+ times per week. Agent-delegation workflows (test generation, refactoring) may add 2-4 hours per month of review-cycle compression per Project Manager, depending on task volume. Actual savings depend on team size and coding workflow intensity.
ADDOM is in alpha (v0.1.0) and maintained by a single developer. Core features (multi-provider chat, integrated editor, agent roles) are functional, but stability and feature velocity are not guaranteed. Suitable for internal development workflows; not recommended for client-facing or mission-critical systems until a stable release is published.
ADDOM is a standalone desktop workspace, not an IDE plugin. It supports OpenAI, Anthropic, Google, and OpenRouter as LLM providers. It does not natively integrate with GitHub, GitLab, Slack, or other agency tools; developers must manually copy results or use terminal sessions within ADDOM to interact with external systems.