The Platform
INTENT
The first fully integrated control plane for governing organizational intent and agentic orchestration.
INTENT keeps purpose, authority, context, human and agent execution, evidence, judgment, and learning connected in one governed loop. Teams can keep working in native or external tools without creating a second source of truth for why the work exists or who may authorize it.
What Makes This Different
Human direction and agentic execution share one structure, one authority model, and one evidence record.
Other tools begin with tasks, prompts, models, or automation. INTENT begins with organizational purpose and keeps the full control loop connected: what should become true, who has authority, what context is admissible, what humans and agents may do, what evidence returns, and who judges the result. Its formally specified, patent-pending core makes the intent graph and governance cycle structural parts of the system, not reporting conventions added after execution.
Specs and harnesses make selected boundaries testable. They are important mechanisms inside this larger control plane. INTENT also governs the purpose, authority, context, orchestration, evidence lineage, and accountable judgment that those mechanisms alone cannot carry.
The Problem
Strategy lives in slides. Execution lives in tickets. AI agents answer to whoever prompted them last.
Your organization has outcomes it cares about. Somewhere between the board deck and the team's backlog, those outcomes get translated, diluted, and lost. AI agents make this worse, not because they are ungoverned, but because they execute so fast that misalignment compounds before anyone notices. The person prompting the agent may be doing exactly what they think is right. They just have no way to verify that it connects to what the organization actually intends.
One System, Three Postures
Same state. Same truth. Different ways in.
Everything in INTENT is a projection of a single canonical state. Switch postures without losing context. Change something in one view, every other view updates instantly.
INTENT
Navigate and operateThe Constellation view renders your intent graph spatially: strategic outcomes as anchoring bodies, outputs orbiting them, work flowing toward delivery. Right-click any node to act on it. Drill into the Workbench to edit, review, or deploy without losing your place in the graph. This is the primary surface. Everything else serves it.
Northstar
The steady point in the constellationNorthstar is the AI copilot that lives inside INTENT. It helps you articulate what you actually mean, tightens scope before vagueness becomes vague execution, and coaches teams toward intent that AI can deliver against. When you define an outcome, Northstar pressure-tests it. When an agent proposes work, Northstar checks alignment. It does not decide. It keeps the constellation oriented.
Signal Board
Coordinate and judgeKanban organized by the governance cycle: Intent Defined, Proposed, Judged, Executing, Verified. Every card shows its connection to the outcome it serves. Drill into any card to review, approve, or deploy an agent. The board surfaces what needs human attention right now.
Operations
Agents, tasks, runtimeWhat are the agents doing right now? Operations shows every active execution, every pending proposal, every running container. This is the transparency surface: the complete audit trail of AI activity, live. The view your CISO will ask about.
Intent Graph
Purpose and Work, Connected
The model says
One roadmap, three horizons. Now is owned and funded. Next is sponsored, waiting. Later is directional. The No Owner No Now gate guards the boundary.
The platform does
A canonical graph connecting outcomes, work, ownership, constraints, authority, and evidence. The horizon board is one projection of that graph. Items in Later cannot move to Now until a Decision Owner is assigned.
Assign a Decision Owner to move to Now
Intent Council + Flow Council
Portfolio Direction
The model says
The Intent Council sets direction and standards. The Flow Council runs the portfolio day to day -- triaging intake, allotting capacity, forming teams, and clearing blockers within hours.
The platform does
A direction dashboard with portfolio health at a glance, a demand intake queue showing every sponsored request, and a capacity pool view showing people and fleet allocation against the Now horizon.
Specify → Build → Judge → Land
The Daily Cycle
The model says
Every day: a spec is written, the fleet builds against it, the harness proves it, the Decision Owner accepts or names the blocker, and daily planning decides Confirm, Amend, or Replace.
The platform does
A split view. Left: the spec with its acceptance criteria as a checkable list. Right: live harness results mapped to each criterion. Below: the acceptance decision panel and the daily planning Confirm / Amend / Replace selector.
Domain Knowledge Network
Knowledge, Wired to the Work
The model says
Domain experts inject knowledge ahead of the work. Their criteria are encoded into the harness. Their context joins the knowledge graph. From that moment their judgment runs on every push.
The platform does
An expert roster showing who is available, who is injecting, and which Effort they are connected to. An injection log tracks every contribution and whether it has been encoded into the harness.
Switchboard
Governed Orchestration
The model says
Spec agents draft, build agents implement, test agents maintain the harness. Fleet capacity is provisioned by the Flow Council in the same pool as human capacity.
The platform does
A governed working surface for human and agent capacity. It shows each actor's role, authorized Effort, execution state, and required judgment gates while preserving one trace back to purpose.
Evidence Ledger + Governance Heartbeat
Evidence & Governance
The model says
Status is read, not written. Every increment carries its spec, harness results, acceptance decision, and who made it. Governance answers three questions continuously.
The platform does
An evidence ledger showing every increment across the portfolio with its proof chain. A governance heartbeat tracking cadence health. The Three Questions dashboard surfaces whether the model is working.
Singularics Metrics
Measurement
The model says
The model defines specific metrics: Decision Owner presence rate, the Confirm / Amend / Replace ratio, blocker resolution time. It also defines failure modes and how to detect them.
The platform does
A metrics dashboard showing every metric with sparkline trends. A failure mode monitor that watches for the specific anti-patterns the model warns about and surfaces them the moment they appear.
Core
One control plane across the full intent-to-evidence loop
Intent Graph
Outcomes cascade to outputs through weighted contributions. Conditions define success. Every piece of work traces back to the strategic intent it serves. Alignment is not a quarterly conversation. It is a continuously computed system property.
Continuous Governance
The same governance cycle runs at every level of the organization. Intent defined, AI proposes, humans judge, AI executes, outcomes verified. The board runs it monthly. Practitioners run it continuously. AI supports the cycle by surfacing conflicts, checking alignment, and routing decisions to the right human. Governance is not a ceremony. It is every moment a human in the loop exercises judgment.
Conservation Engine
Patented technology that replaces retrieval-based context (RAG) entirely. RAG recovers 29% of what agents need. The Conservation Engine recovers 100%. Token cost bounded by structural relevance, independent of corpus size. The longer you use INTENT, the less each operation costs.
Northstar Copilot
Built-in AI coach that helps teams articulate what they actually mean. Tightens scope. Strengthens acceptance criteria. Catches vague intent before it becomes vague execution. Supports OpenAI and Anthropic. Bring your own key.
Live Deployment
Agents don't just write code. They deploy it to a live preview so the human can verify against a running system, not a diff. INTENT manages containers, allocates resources, and scales infrastructure automatically. The verification step has evidence you can see and interact with.
INTENT Editor
A code editor that connects to your intent graph. Every file shows which work item it serves, what acceptance criteria it needs to meet, and where it sits in the cycle. Commit and the work item advances automatically. Northstar built in with both code context and intent context. Starts fast. Stays out of your way. Free.
Execution Ecosystem
Use INTENT natively or connect Jira, Azure DevOps, GitHub, and GitLab as execution adapters. Those tools can manage and perform work while the intent graph remains the authority for purpose, permission, and evidence.
Continuous Governance
Governance is not a ceremony. It is every moment a human in the loop exercises judgment.
Every enterprise is about to have hundreds of people, and soon thousands, each with dozens of AI agents, all building simultaneously with no shared model of what matters. The result is fast, confident, well-built work going in every direction at once. Periodic planning cannot govern continuous execution. The governance cycle has to run at the speed the work runs.
INTENT makes governance continuous and agentically supported. The system surfaces what needs human judgment, routes it to the right person at the right organizational level, and records every decision. AI supports the governance cycle by checking alignment, detecting drift, and flagging conflicts so humans can focus on the judgments that matter. The cycle is the same at every level. The cadence scales with the work.
INTENT is the organizational intent and agentic orchestration control plane. The intent graph preserves purpose. The command spine carries authorized action. The evidence record shows what happened and what it proved. Human judgment gates decide what may proceed and what counts as accepted.
Enterprise Governance
When the auditor asks what happened, the answer is already in the graph.
The governed audit spine is the foundation INTENT is built on. Every event captured, hash-chained, actor-attributed, and immutable. The operational system and the compliance record are architecturally separate.
The compliance record is a structural byproduct of the work itself. Who authorized it, what intent allowed it, what changed, and whether the result was accepted. INTENT records all of it as the work happens.
Architecture
Your data. Your infrastructure. Your rules.
Every customer runs on completely isolated infrastructure. Your own server, your own database, your own encryption. No shared tenancy. No data mixing. Bring your own LLM keys. We never see your prompts. Your intent graph is your competitive strategy in structured form. We protect it accordingly.
Isolated instances
Dedicated server and database per customer. Provisioned in under two minutes.
Bring your own keys
OpenAI, Anthropic, or any compatible provider. Your prompts never touch shared infrastructure.
SSO / OIDC
Your identity provider. Your policies. Your audit requirements.
Real-time
WebSocket event fabric. All connected clients see graph changes instantly. No polling.
Custom domains
Run INTENT on your own domain. Team tier and above.
Self-hosted option
Enterprise customers can deploy on their own infrastructure. Full control.
Model-agnostic
Cloud APIs, on-prem inference clusters, local inference hardware. INTENT is the governance layer, not the intelligence.
Formal Foundation
Built on a formally specified core. Not a feature list.
INTENT is not assembled from product intuitions. It is built on a canonical formal model with mathematically defined properties. The organizational state is a structured object with nodes, assignments, typed relations, an append-only event ledger, governance policies, and deterministic projections. Every view you see in the product is a pure projection of that state.
Most enterprise software is built by adding features until the demo looks good. INTENT is built by defining the mathematical object first and implementing it faithfully. The difference shows up when your organization scales, when your auditor asks for proof, and when the system needs to explain why something happened.
Bring Your Own Tools
Your team already has Cursor, Copilot, or Claude Code. Keep them. INTENT provides the why.
Not every team will use INTENT for code generation and deployment. Many will use it for what happens before and after the code: defining intent, decomposing work, setting acceptance criteria, and verifying that the outcome was achieved. The repo stays where it is. INTENT provides the structured context that makes every AI tool in the org smarter.
The real integration: INTENT exposes an MCP server that any AI coding tool can read. Cursor, Copilot, Claude Code, Codex, your custom agents. They all get structured context about what to build, why, and what done looks like. Connect your repo for full context packets. Your tools get the intent graph. Your agents get the execution contract.
Or use ours: INTENT Editor is a native code editor built on the same intent graph. Every file maps to a work item. Acceptance criteria live next to the code. Commit and the cycle advances. For teams that want the full loop in one surface.
What Changes
Your day before and after INTENT
VP of Engineering
You open Jira. 400 tickets across 8 teams. You have no idea which ones connect to the objectives you committed to last quarter. Three teams are using AI agents, but you cannot tell what those agents built or whether it was authorized. Your board asks if AI is delivering ROI. You do not know.
You open the Constellation. Every piece of work traces to a declared outcome. Agent activity is visible in Operations with cost attribution. The Signal Board shows what needs your judgment right now. Your board report writes itself from the graph.
Product Owner
You write user stories and hope developers interpret them correctly. With AI agents, interpretation happens at machine speed, in directions you did not anticipate. You review output that technically matches what you wrote but is not what you meant. Three rounds of revision per feature.
You define intent with Northstar, which pressure-tests your acceptance criteria before anything starts. AI proposes a decomposition. You approve or adjust. The agent executes and deploys a live preview. You verify against a running system, not a diff.
CISO / Compliance
Your organization deployed AI agents across four teams last quarter. You have no authorization records, no audit trail, no way to know what those agents accessed or produced. When the auditor asks, you reconstruct from git logs and Slack messages.
Every agent proposal, every human approval, every execution event is recorded in an append-only event ledger. The audit trail is structural. You do not reconstruct it. You query it.
Implement INTENT
Bring INTENT to your enterprise.
INTENT is deployed as a dedicated, isolated instance inside your organization. Your own infrastructure, your own database, your own encryption keys. We work with your team to configure the intent graph around your portfolio structure, integrate with your existing tools, and establish the governance cycle that fits how your organization makes decisions.
Isolated infrastructure
Dedicated server and database per customer. No shared tenancy. No data mixing.
Your tools, connected
Jira, Azure DevOps, GitHub, GitLab. INTENT remains the control plane across your existing execution stack.
Bring your own keys
OpenAI, Anthropic, or any compatible provider. Your prompts never touch shared infrastructure.
SSO and compliance
Your identity provider, your policies, your audit requirements. SOC 2 support available.
Self-hosted option
Deploy on your own infrastructure for full control. Nothing leaves your perimeter.
Engagement-led
We help you configure the graph, train your teams, and establish governance. Not a login and figure it out.
The first fully integrated control plane for governing organizational intent and agentic orchestration. Three patents pending.