Echo · Hiring a Synthetic Team

Hiring A Synthetic Team /synth-team/CLAUDE.md NAVIGATING YOUR WORTH

        # HIRING A SYNTHETIC TEAM
        
        OVERVIEW: As a solofounder, I need an A1 innovation product team that can help me take Echo from zero → one on my own.
        
        CONSTRAINT: pre-revenue · no headcount · production deadline: 05-28-2026
        
        THESIS: Structured work belongs to agents. Judgment stays with the founder. Scale through codification, not headcount.

        ## OKR
        
        OBJECTIVE: Jada can solo manage a production-ready and safe AI-native application
        
        > KR.1: Jada codifies her process for building ventures and products
        > KR.2: Jada codifies her expertise and trains her team
        > KR.3: Jada determines the right interface for engaging with her team
        > KR.4: Jada determines the scope of the first workflow
      

## THE TEAM AND THEIR PROCESSES> processes complete workflowsproduct_refinement  type: process  ·  focus: extraction  ·  action: classify, chunk, assign, extract entities, route to agentspeer_review         type: process  ·  focus: evaluation  ·  action: validate completeness, score confidence, flag conflicts
> agents manage objectives directlynyx          role: conductor      ·  owns: task creation, dependency tracking, gate evaluation, stall detectionnorth        role: strategy       ·  owns: venture brief, business model, OKRs, market, risks, constraintsscout        role: discovery      ·  owns: evidence, personas, observed workflows, friction, pain pointsharper       role: communication  ·  owns: lexicon, voice, brand, GTM, launch, content, copy, UX writingrei          role: design         ·  owns: design system, user flow, prototypes, microcopy, onboardingcadence      role: execution      ·  owns: scope, sprints, requirements, gates, readinessnavy         role: development    ·  owns: APIs, MCPs, models, infrastructure, security, datasage         role: monitoring     ·  owns: KPIs, observability, retention, drift, performance
            API
            Anthropic
          

            Model
            claude-sonnet-4-6
          

            Interface
            Product Refinement
          

Before our agents can work

They need a shared knowledge base.

Without shared context, your agents will optimize for different realities. Give them one source of truth, and they start pulling in the same direction.

Process 01 · Data Intake

Product Refinement in four passes

Upload your materials and let us handle the intake so your agents don't have to guess what they're working with.

INTAKE

Upload Data intake[docs]

docs · images · prototypes · structured data

PASS 1

Classify classified_doc[]

doc type · purpose · recency · authorship

PASS 2

Chunk chunk[]

docs are split into chunks · one chunk, one idea

PASS 3

Assign chunk[].owner

primary agent assigned · backup assigned as needed

PASS 4

Extract entity[]

personas · features · constraints · assumptions · metrics · dates

OUTPUT

Refinement RecordRR-001

everything extracted, assigned, and scored

Process 02 · Evaluation

Peer Review to evaluate the RefinementRecord

Runs after every process that produces a record. Scores completeness, measures confidence, and surfaces gaps. Does not block work. Tells agents where to look harder.

INPUT

Refinement Record RR-001

docs · chunks · entities · assignments · coverage · confidence

REVIEW 1

Coverage Score 0.00 to 1.00

What percentage of the possible knowledge map does your upload cover?

REVIEW 2

Confidence Score 0.00 to 1.00

How clean was the extraction, and how much of it can the agents trust?

REVIEW 3

Flag Generation flags[]

Every issue found, categorized, and surfaced before downstream work begins.