HIGHEST DEGREE PRIORITIES

Command Center

SUCCESS LOOP

Turn everything we build into possibilities.

Observe the outside world, mine projects for reusable capabilities, discover non-obvious combinations, test market pull, and feed evidence back into development.

CAPTURE → DISCOVER → TEST → DEPLOY → LEARN
TOP OPPORTUNITIES

Where Success OS wants attention

LATEST DISCOVERIES

What Universal Ingest found

OPPORTUNITY RADAR

What just became more valuable?

LIVE SIGNALS

External change becomes fuel

ACTIVITY

The commercialization memory

ASSET PORTFOLIO

Stable products + exploration branches

UNIVERSAL CAPTURE · SUCCESS INBOX

Throw it in.
Don’t lose what it means.

Paste it, upload it, or let a connector deliver it. Success OS preserves the original input, provenance and attachments, then classifies it into one or more destinations—Signal, Opportunity, Commitment, Deal/RFQ, Experiment Evidence, Universal Ingest, or Surprise Ledger.

CAPTURE

One inbox for the real-world stream

ROUTING DOCTRINE

Classification is multi-label

SignalSomething outside changed and may alter opportunity value.
OpportunityA buyer/problem/application hypothesis worth testing.
CommitmentUnfinished business that must wake later.
Deal / RFQCommercial terms or contract work worth underwriting.
EvidenceReality changed an experiment’s belief; experiment link required.
IngestMaterial may contain reusable capabilities/products.
SurprisePreserve a potential false negative or framing/category lesson.
Original preservedPromotions create downstream records without destroying the capture.
FILE + IMAGE INTAKE

Preserve the artifact, then interpret it

Text/source files are added to classification context. Images and other binaries are stored locally with provenance. Images remain VISION PENDING until a configured vision worker—or you—describes them.

No files selected.
CAPTURE CONNECTORS

Let outside streams feed the Inbox

Inbound connectors require a token and create captures only. They do not silently promote Opportunities, Commitments, Deals, or Evidence. Vision connectors are outbound analysis hooks for attached images.

INBOX

Analyze once. Route many ways.

Recommended routes are suggestions; promote only what should become organizational state.
CAPTURE MEMORY

Recent classification runs

CONNECTOR HUB · HDP SUCCESS EVENT FABRIC

Many systems report.
One Success language.

v1.8 normalizes Gmail, Lucy/Twilio, Lead Wizard, GitHub, CRM, payments, website/demo activity, deployments and future systems into hdp-success-event/v1.8. Events are deduplicated, provenance-aware, trust-scored and can become preserved Success Inbox captures without silently promoting organizational state.

EVENT SOURCE

Add another nervous-system pathway

EVENT DOCTRINE

Observe broadly. Act narrowly.

NormalizeConnector-specific payloads become one stable Success Event envelope.
DedupeExternal event IDs/fingerprints suppress repeated deliveries without losing duplicate counts.
CaptureTrusted sources may auto-preserve events in Success Inbox; this is observation, not promotion.
WakeOnly an explicitly enabled source may wake an existing event-key commitment.
RelationshipsMission, Opportunity and Experiment links survive through the event into downstream capture.
Protocol stabilityNew connectors translate at the edge instead of teaching the whole OS a new schema.
SOURCE HUB

Named pathways into Success OS

All templates begin disconnected except Generic Event API. Enabling a source does not connect an outside service by itself; it opens an authenticated intake endpoint.
EVENT STREAM

Normalized organizational observations

EVENT MEMORY

Recent deliveries + dedupe outcomes

SUCCESS PATTERN INTELLIGENCE · CAUSAL MEMORY

Remember what tends to happen.
Act earlier next time.

v1.9 mines recurring event sequences across Experiment, Opportunity, customer/subject, thread/session, and other durable journey identities. It measures support, confidence and lag, then surfaces active reflexes. Every machine-found rule is explicitly an observed association until stronger evidence supports a causal claim.

PATTERN POLICY

Bound the memory before mining

CAUSAL DOCTRINE

Correlation is a clue, not a verdict.

Journey-awareSequences are grouped by experiment, opportunity, subject/customer, then durable thread/session/actor identity; otherwise an unlinked event stays isolated—not mashed into one global timeline.
Lag-awareMedian time between precursor and outcome becomes part of the reflex window.
Outcome-awareCustomer, revenue, validated and failed experiment markers can be synthesized from Success evidence.
No causal bluffingMachine rules remain “observed association” until Causal Memory records stronger proof.
ReflexesWhen a recent event matches a learned precursor, Success OS can surface what historically tended to happen next.
DisconfirmableEvery causal hypothesis should record confounders and the next test that could prove it wrong.
ACTIVE REFLEXES

Signals happening now that resemble learned sequences

PATTERN MEMORY

Recurring transitions ranked by evidence

Support = distinct journeys. Confidence = journeys containing the precursor that also reached the next event inside the configured window.
CAUSAL MEMORY

Preserve the hypothesis without overstating it

HYPOTHESIS LEDGER

What we think might be causal—and why

PATTERN RUN MEMORY

Learning rounds

INTERVENTION LEARNING · REFLEX ENGINE

Don't just predict the next state.
Learn which action changes it.

v2.0 converts learned patterns into controlled Reflex Programs. Journeys are assigned to a holdout or intervention arm, outcomes are measured inside a bounded window, and Success OS reports effect size plus uncertainty. A small-sample winner remains a signal—not a causal victory lap.

EVIDENCE POLICY

Define what counts before looking at results

CAUSAL GUARDRAILS

Randomization earns the right to learn.

Holdout requiredEvery randomized program retains a business-as-usual control arm.
Stable assignmentThe same journey deterministically receives the same arm; weights freeze once enrollment begins.
Exposure-awareTreatment outcomes count only after the intervention is actually recorded as applied.
Uncertainty shownLift is reported with a 95% difference interval and comparison p-value, not only a winner badge.
Harm stopsIf the randomized interval indicates likely harm, the program pauses instead of scaling itself.
Replication mattersEven evidence of effect is phrased as evidence consistent with causality and should be replicated before broad rollout.
REFLEX PROGRAMS

Controlled actions attached to learned precursors

MANUAL ENROLLMENT

Assign a journey deterministically

Live Event Fabric precursors can enroll linked journeys automatically. Manual enrollment is useful for testing and offline channels.

CREATE PROGRAM

Start without a mined pattern

ASSIGNMENT LEDGER

Who was assigned, exposed, and observed

INTERVENTION MEMORY

Evaluation rounds

ADAPTIVE REFLEX POLICY · CONTEXT-AWARE LEARNING

Don't use a winning intervention everywhere.
Learn where it earns the right to act.

v2.1 searches randomized intervention evidence for operating contexts where effect differs, then graduates promising contexts into policies with a persistent randomized holdout. Machine-discovered segments are exploratory—not a license to overfit.

POLICY EVIDENCE RULES

Keep subgroup discovery skeptical

SAFE CONTEXT POLICY

Context can improve decisions without profiling people.

Operational contextVertical, channel, stage, customer type, company size, intent tier, campaign, market and similar workflow context can be tested.
Sensitive attributes blockedRace, ethnicity, religion, sex/gender, sexual orientation, health/disability, politics, union status and similar protected/sensitive personal traits are excluded from policy context.
Post-hoc ≠ provenA machine-found subgroup remains exploratory even if the parent experiment was randomized.
Holdout survives graduationActive policies keep a randomized control slice so effect drift and harm remain measurable.
SEGMENT DISCOVERY

Where did the intervention appear to work differently?

The analysis only uses context already attached to intervention assignments. Results are exploratory subgroup associations and should be replicated.

MANUAL POLICY

Prespecify a context rule before rollout

SEGMENT MEMORY

Exploratory context differences

A strong subgroup can seed a policy, but policy evidence must be earned again with its own holdout.
ADAPTIVE POLICIES

Context-aware reflexes with persistent exploration

POLICY DECISION LEDGER

Eligible, held out, recommended, applied, observed

POLICY MEMORY

Evaluation rounds

INTERVENTION PORTFOLIO · POLICY COMPETITION

Don't just learn whether an action works.
Learn which action is worth taking now.

v2.2 compares multiple possible next actions under a permanent holdout, explicit exploration, cost/friction penalties, route availability, and randomized outcome evidence. Preference is earned and remains reversible.

LEARNING POLICY

Holdout + exploration + expected value

DECISION DOCTRINE

Preference is not permanent truth.

Holdout never disappearsA control slice stays untouched so the system can detect drift and false improvement.
Exploration stays aliveUnder-sampled and alternative actions keep receiving bounded trials instead of being permanently starved.
Economics matterOutcome probability is weighed against actual cost, customer friction, and current execution availability.
No silent actionAn action recommendation is still only a recommendation until exposure is confirmed through governed execution.
SEED FROM ADAPTIVE POLICY

Make the current reflex compete

MANUAL ACTION PORTFOLIO

Start with several plausible next moves

ACTION PORTFOLIOS

Competing interventions under one decision policy

ACTION DECISION LEDGER

Holdout, explore, exploit, apply, observe

COMPETITION MEMORY

Evaluation rounds and reversibility

POLICY EXECUTION BRIDGE · CLOSED-LOOP ACTION ROUTING

Recommendation is not execution.
Route it, prove it happened, learn from the result.

v2.3 connects Action Competition to the governed Execution Router. Holdouts remain protected; external actions respect approval gates; exposure is recorded only when confirmed; worker observations can return through Event Fabric and update the learning loop.

BRIDGE POLICY

How far may the loop close automatically?

EXECUTION DOCTRINE

Learning never gets to forge reality.

Holdout is protectedControl journeys never receive a bridge execution.
Approval survives optimizationA high-utility action still respects its approval and external-action gates.
Exposure requires confirmationAssignment or webhook success does not count as treatment unless the action actually occurred.
Results return as eventsWorkers may report observed events through Event Fabric; they cannot fabricate outcomes or bypass experiment relationships.
ACTION ROUTING QUEUE

Recommended actions waiting to become governed executions

BRIDGE EXECUTION MEMORY

Recommendation → route → execution → exposure → event feedback

OUTCOME VERIFICATION · EVIDENCE ARBITRATION

Don’t reward a claim.
Verify what actually happened.

v2.4 separates worker claims from independent corroboration. Outcomes can remain pending, become verified, or be disputed/rejected when credible systems disagree. Disputed outcomes are withdrawn from adaptive-learning statistics.

ARBITRATION POLICY

How much evidence earns credit?

ARBITRATION DOCTRINE

Evidence has roles, not just volume.

ClaimThe system that performed the action may say what it believes happened, but it cannot independently verify its own success.
SupportAn independent source such as CRM, Calendar, Gmail or Payments can corroborate the outcome.
ContradictionA credible reversal, cancellation, refund, missing appointment or explicit negative observation can dispute prior credit.
Verified ≠ permanentLater contradictory evidence can withdraw a previously credited outcome from policy-learning statistics.
MANUAL CASE

Open an outcome question

WHY THIS MATTERS

Claims become auditable organizational truth.

A worker can prove that it executed an action. A source-of-record can prove the external result. When they disagree, Success OS preserves both and withholds causal credit until the dispute is resolved.

Lucy says bookedClaim only until Calendar/CRM confirms.
HDP Pay says paidA high-trust transaction source can strongly corroborate revenue.
Refund arrives laterContradictory evidence can reverse prior learning credit.
Human review remains possibleAmbiguous cases can be explicitly resolved with an audit note.
VERIFICATION CASES

Claim → support / contradiction → verified truth

ARBITRATION MEMORY

When evidence changed organizational belief

UNIVERSAL INGEST

Give it our stuff.
Find what is hiding inside.

Import a project folder, ZIP, public GitHub repository/URL, or paste source/notes. Universal Ingest extracts reusable capability primitives, preserves evidence, and mines possible standalone products and recombinations.

LOCAL PROJECT

Folder or ZIP

Nothing is uploaded anywhere except this local Success OS process. Folder mode reads text/source files in your browser; ZIP mode is unpacked locally by the Node server.

No project selected.
REMOTE / RAW

URL, GitHub, or pasted material

OR
PRODUCT MINE

Recent ingest analyses

Promote evidence-backed discoveries into the global Capability Graph.
CAPABILITY GRAPH

The product is not the unit. The capability is.

Manual capabilities and Universal Ingest discoveries converge here. Success OS searches the graph for hidden standalone products, bundles, overlays, and vertical applications.

CAPABILITY INVENTORY

Reusable economic primitives

SIGNAL INBOX

Drop in anything that might matter

Competitor funding, a customer complaint, a new law, a job posting, an incumbent workflow, a strange headline—Success OS treats it as opportunity intelligence.

RECENT SIGNALS

Turn threat into leverage

OPPORTUNITY RADAR

Don't watch the market.
Watch what changes for us.

Continuous Discovery turns feeds, pages, market observations and the Signal Inbox into capability deltas: which HDP capabilities became more valuable, where, why now, and the cheapest useful test.

RADAR PULSE

Scan internal + external signals

Repeated items are fingerprinted and deduplicated. A pulse refreshes confidence and matches without erasing promoted or dismissed decisions.

RADAR SOURCE

Add RSS, Atom, JSON, or webpage

WATCH SOURCES

Where Radar is listening

CAPABILITY DELTAS

Ranked changes worth our attention

RADAR MEMORY

Pulse history

OPPORTUNITY GENOME

Products are combinations.
Search the combinations.

Opportunity Genome recombines capabilities across different HDP assets, then mutates the commercial genes around them: vertical, buyer, channel, business model, proof strategy and market signal. Cheap scoring kills weak combinations before deeper Success Swarm effort.

COMBINATION SEARCH

How wide should the swarm look?

The local engine evaluates capability bundles first, then only mutates the strongest bundles into commercial forms. This avoids exploding every possible Cartesian combination.

GENES

What gets recombined?

CapabilitiesReusable technical/economic primitives from every asset.
Market triggerSignal Inbox + live Opportunity Radar candidates.
VerticalPain, budget, access, competition, expansion and founder fit.
BuyerOperator, operations leader, technical buyer, investor/acquirer and more.
DistributionDirect, local, partners, content hooks or incumbent ecosystems.
Business modelManaged service, overlay, assessment, outcome fee, licensing or marketplace.
GENOME CANDIDATES

Products hiding between products

GENOME MEMORY

Combination-search history

SURPRISE LEDGER

Don't let a sensible filter
erase an unreasonable upside.

A conventional score answers “does this look commercially coherent now?” The Surprise Ledger separately records false-negative risk: framing leverage, asymmetry, ordinary utilities that could become categories, and hypotheses whose story is ahead of their proof.

LOG A SURPRISE

Capture the thing we might laugh off today

Historical near-misses, oddly simple utilities, strange valuations, naming/framing observations, or anything that teaches Success OS where conventional reasoning can create false negatives.

TWO-LANE FILTER

Coherence is a gate. Surprise is a reserve.

Conventional laneTechnical relevance, buyer fit, vertical fit, proof and distribution still matter.
Surprise laneNear-filter candidates survive when framing, timing, recombination or asymmetric upside could make the filter wrong.
Narrative riskA strong story with weak proof is tracked explicitly—not worshipped and not automatically discarded.
Cheap falsificationThe answer to uncertainty is a bounded test, not endless debate or automatic engineering.
FALSE-NEGATIVE MEMORY

Ideas the filter is not allowed to silently forget

CATEGORY CREATION · MARKET EXPANSION LAB

Separate where we built it
from who actually needs it.

Category Lab audits Context Lock, abstracts the underlying job-to-be-done, searches broader market boundaries, and maps the commercial infrastructure required to turn an invention into a transaction. A missing payment, distribution, onboarding, trust, or integration layer can become its own opportunity.

RUN MARKET-BOUNDARY AUDIT

What if the birthplace is lying to us?

HISTORICAL LESSONS

Teach Success OS what hindsight exposed

These are not predictions. They are anti-blind-spot memory: why an opportunity looked small, what the real market was, and which missing commercial link prevented capture.

CATEGORY AUDITS

Utilities, categories, and missing links

Run the same asset more than once as new capabilities or evidence appear.
SUCCESS MISSION CONTROL · OBJECTIVE ENGINE

Good opportunities are everywhere.
Choose what matters now.

v1.3 combines active company objectives, Opportunity scores, Resource Genome fit, available cash, available attention, deadlines and Track-2 reserve into one bounded company priority queue. Unselected ideas are deferred with reasons—not erased.

COMPANY OBJECTIVES

Tell Success OS what winning means right now

MISSION CAPACITY

Bound the company before ranking the work

Objective firstAn exciting idea can still be the wrong thing this week.
Best ownerEvery selected priority inherits the strongest Resource Genome match.
Finite attentionCash and hours are allocated once; the same scarce capacity cannot be promised everywhere.
Protected frontierA bounded Track-2 reserve keeps strange asymmetric work alive without letting it consume Track 1.
Deferred ≠ rejectedHigh-potential ideas remain visible with an explicit reason they are not “now.”
Evidence loopValidated/failed experiments change future objective progress and mission ranking.
ACTIVE OBJECTIVES

What HDP is trying to accomplish

COMPANY PRIORITY QUEUE

What deserves attention now

Ranked across objectives, resources, cash, attention and evidence.
DEFERRED — NOT DELETED

Good ideas that are not the mission right now

MISSION MEMORY

Company-level allocation rounds

MISSION OPERATING CADENCE · AI CHIEF OF STAFF

Mission Control chose the work.
Now move it.

v1.4 converts the current company mission into a bounded operating rhythm. It does not invent side quests: every item traces back to a Mission priority, a real Success Experiment, a dependency, a review gate, or a blocker that prevents the selected work from moving.

OPERATING POLICY

Bound today before filling it

CHIEF-OF-STAFF RULES

Move decisions, not busywork

Mission-boundNo work appears unless it traces to the current Mission plan.
Next dependencyExpose the next thing that actually unlocks motion—not a giant to-do dump.
Human gates visibleApprovals, judgment, evidence, and external human handoffs become Needs Harrison.
Agents are honestDry Run is Blocked, not “agent-executable.” Live workers are labeled only when routing really exists.
Stale means staleIf the mission, evidence, routes, or tasks change, rebuild the cadence.
Waiting is work stateWaiting/review/blocked items stay visible without stealing the Now queue.
OPERATING BOARD

Now → Today → Next

Human attention is budgeted; agent work and blockers remain visible without flooding the day.

NOW

TODAY

NEXT

WAITING / BLOCKED

What cannot move yet

HUMAN + EVIDENCE GATES

What needs judgment rather than more automation

CADENCE MEMORY

Operating snapshots

COMMITMENT LEDGER · FOLLOW-UP BRAIN

Unfinished business should sleep.
Not disappear.

v1.5 remembers promises, replies, deadlines, credentials, proposals, pilots, approvals, and future checkpoints. A commitment stays out of the way while it is waiting, then wakes when its date or event becomes actionable. Mission-linked commitments automatically invalidate Chief of Staff cadence when they wake.

ADD COMMITMENT

Tell Success OS what must not vanish

FOLLOW-UP POLICY

Wake at the right time

Date wakeSleep until the follow-up/deadline time becomes actionable.
No-response wakeWait politely, then surface the next action if nothing arrived by the deadline.
Event wakeAn API/manual event such as “reply arrived” can wake the exact commitment early.
Mission-awareOnly commitments tied to current Mission work enter Chief of Staff cadence.
No amnesiaSnoozes, completions, cancellations and wake events remain in commitment memory.
Local runtimeAutomatic wake checks run only while this Success OS server is running.
OPEN COMMITMENTS

Waiting → actionable → overdue

Due work wakes; future work stays quiet.
EVENT BUS

Wake commitments from outside events

FOLLOW-UP MEMORY

Wake/sweep history

COMPLETED / CANCELLED

Closed loops remain searchable memory

RESOURCE GENOME + OPPORTUNITY MATCHER

Don't just find opportunities.
Know what should pursue them.

Resource Genome pulls the shop-fit lesson back into the whole Success OS. Model a team, agent stack, partner, facility, channel, or capital bundle once; then rank every Success Opportunity against its real capability coverage, market fit, bandwidth, cash envelope, channels, and missing genes.

CREATE RESOURCE GENOME

Define an execution organism

WHY THIS MATTERS

Manufacturing was a special case of a general problem

Opportunity ≠ universal valueThe same opportunity can be excellent for one resource configuration and wrong for another.
Capability coverageDoes this team actually contain the technical/commercial genes the opportunity requires?
AssemblyIf a gene is missing, does another HDP asset already own it?
CapacityDo we have enough time and attention to pursue it without derailing higher-value work?
CashCan the resource afford the cheapest useful experiment or deal structure?
RoutingThe answer can be pursue, assemble, partner, test narrowly, watch, or pass.
RESOURCE GENOMES

Track 1, Track 2, specialist cells, partners and future teams

OPPORTUNITY × RESOURCE BOARD

Who should own what—and what must be assembled first?

Ranks all current Success Opportunities across all active Resource Genomes.
ALLOCATION MEMORY

Resource-matching rounds

SHOP GENOME + DEAL MATCHER

Don't ask whether an RFQ is good.
Ask whether it is good for this shop.

Model machines, axes, travel, materials, quality systems, inspection, available hours, cash constraints and preferred work. Then rank every Deal Architect opportunity against the facility that would actually execute it.

CREATE SHOP GENOME

Define the operating reality once

ADD MACHINE / CELL

Turn equipment into matchable capacity

SHOP GENOMES

Facilities are economic organisms, not equipment lists

DEAL MATCH BOARD

Which RFQs deserve this shop's scarce attention?

MATCH MEMORY

Recent shop-specific underwriting rounds

DEAL ARCHITECT + MARKET BOARD

A good job is not a price.
It is a configuration of economics.

Underwrite RFQs, contracts, partnerships, and capacity opportunities by total deal quality: material exposure, scrap rights, excess stock, tooling, freight, quality burden, payment timing, machine fit, recurrence, and hidden value.

ADD DEAL / RFQ

Underwrite before you chase it

SHOP MATCH REQUIREMENTS
DEAL-STRUCTURE MEMORY

Remember the terms that changed the economics

BULK BOARD IMPORT

Paste CSV or JSON RFQs

Up to 10,000 deals per import. The board renders the highest-priority slice while the matcher scores the full set.
DEAL BOARD

Rank opportunities by total economic quality

Headline value is only one gene.
SUCCESS PORTFOLIO · EVOLUTION ENGINE

Don't bet the company on one idea.
Evolve a portfolio toward evidence.

The Portfolio Engine allocates a bounded experiment budget across conventional high-fitness opportunities and protected high-surprise bets. Winners earn more attention, losers become mutation material, and the system changes framing/distribution before assuming the technology itself is wrong.

EVOLUTION ROUND

Set the search pressure

NATURAL SELECTION POLICY

Exploit what works. Explore what might fool us.

Exploit laneCommercial fitness, evidence, information value and portfolio diversity.
Explore laneA protected budget reserve for high-surprise / high-information hypotheses.
SelectionValidated tests increase fitness; failed tests reduce it without deleting the genes.
MutationChange buyer, channel, business model or proof before rebuilding the technical primitive.
DiversityA portfolio of eight near-identical HVAC offers is still one bet.
Budget disciplineAllocations are ceilings for cheap tests, not automatic spending authority.
SELECTED PORTFOLIO

Current bets and experiment allocations

NEXT GENERATION

Single-gene mutations worth comparing

Mutations consume no budget until you choose to test them.
EVOLUTION MEMORY

Portfolio rounds

OPPORTUNITY MATRIX

Evidence-ranked hypotheses, not commitments

EXPERIMENT RUNNER

Don't debate the market.
Run the cheapest useful test.

Turn an Opportunity into an executable Success Experiment. Experiment Runner creates the hypothesis, proof requirement, audience, dependency graph and evidence thresholds—and can hand the whole experiment to the Success Workforce.

CREATE PULL TEST

Opportunity → experiment

ACTIVE TESTS

Experiments in motion

EXECUTION ROUTER

Tasks become jobs.
Jobs come back as evidence.

Map each Success worker to a safe execution adapter. Preview the exact payload first, hand work to a human when useful, or route it to an HDP/API webhook. External results stay reviewable unless you explicitly enable auto-apply.

WORKER ROUTES

Who goes where?

Every experiment task names a worker. Routes decide which adapter receives that worker's execution envelope.

ADD WEBHOOK ADAPTER

Connect an existing agent/API

ADAPTERS

Execution surfaces

Dry-run is the default. No external action happens until a worker route points at an enabled webhook.
EXECUTION MEMORY

What was routed, where, and what came back

WEBHOOK RETURN CONTRACT

Optional structured result

Adapters can return any JSON. These fields are recognized when you apply the result:

{
  "summary": "25 prospects researched and scored",
  "taskStatus": "done",
  "metrics": { "prospects": 25 },
  "evidence": ["25 matched records written to CRM"],
  "objections": [],
  "learnings": ["Roofing owners responded more strongly than general contractors"]
}
SUCCESS WORKFORCE

Give the experiment a goal.
Let the team move the work.

Success Workforce executes experiment task graphs in dependency order. It pauses at live-action approvals, review gates, human handoffs, disabled/dry-run routes, budget ceilings, failures, or a decisive market result.

CAMPAIGNS

Experiments available to the workforce

CONTROLLED AUTONOMY

What makes a run stop?

DependenciesWorkers only receive tasks whose prerequisites are complete.
Approval gatesLive seller/action webhooks require explicit approval by default.
Review gatesUntrusted results pause until evidence is applied.
BudgetRuns halt when recorded experiment spend reaches the ceiling.
Retry safetyAutomatic retries occur only on adapters explicitly marked retry-safe.
Decision stopValidated or failed experiments stop further autonomous work.
No workforce plan selected.Choose Plan on an experiment to see dependencies, routes, approvals and blockers before anything executes.
WORKFORCE MEMORY

Autonomous run history

SUCCESS PIPELINE

Built is not done. Revenue is evidence.

SUCCESS SWARM

Hundreds of tiny perspectives. One ranked decision.

Cheap local evaluators inspect hidden-product, bundling, vertical, distribution, integration, evidence, capital, adjacency, risk, and other lenses. Later, selected lenses can be upgraded to deeper model workers.

LATEST VERDICT

No swarm run yet

—
PHILOSOPHY

Competition is an input, not a verdict.

Competitor raises capitalWhat market did they validate?
Big platform commoditizes a featureWhat can we now use cheaply one layer above?
Incumbent dominates a verticalWhat customer base did they educate for us?
Customer rejects an offerWhat evidence did we just gain?

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