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ZAVOEN

Intelligence, made operational.

ZAVOEN builds intelligent systems for clinical intelligence, revenue operations and autonomous work. Scrabyt is live; GTM Engineering and Agent OS are coming soon.

Explore systemsSee Scrabyt, live

02 — Systems

Three systems. One intelligence layer.

  1. 01Live

    Scrabyt

    Clinical intelligence

    From conversation to clinical action: the note, prescription, bill and follow-up, drafted for the doctor to review and sign.

    Explore Scrabyt

  2. 02Coming soon

    GTM Engineering

    Revenue intelligence

    Build GTM workflows, approve consequential actions, and let specialized agents execute the work.

    Preview GTM Engineering

  3. 03Coming soon

    Agent OS

    Autonomous operations

    Intelligence that can act: a task is planned, divided among specialist agents, executed with tools and verified before it returns.

    Preview Agent OS

Outcome Explorer

What do you want to move?

Recommended system

Agent OS

Autonomous operations

Concept · illustrative

Recommended system

Scrabyt

Clinical intelligence

Synthetic demo

Recommended system

ZAVOEN Intelligence

Data → ML

Concept · illustrative

Recommended system

GTM Engineering

Revenue intelligence

Concept · illustrative

Recommended system

Agent OS

Autonomous operations

Concept · illustrative

Remove repetitive work: Agent OS

  1. A task enters
  2. The OS decomposes it
  3. Specialist agents activate
  4. Each does its part and reports back
  5. The result is verified and a person approves it

Turn conversations into action: Scrabyt

  1. Voice
  2. Transcript
  3. Context
  4. SOAP note
  5. Prescription, signed by the doctor
  6. Billing
  7. Follow-up

Turn data into decisions: ZAVOEN Intelligence

  1. Raw data arrives
  2. It is structured
  3. A model is assembled
  4. The data flows through it
  5. A decision, reviewed by a person

Move pipeline: GTM Engineering

  1. Discover accounts
  2. Enrich them
  3. Research buying signals
  4. Score and qualify
  5. A person approves
  6. Execute

Coordinate autonomous work: Agent OS

  1. A goal enters
  2. The OS plans the work
  3. Eight specialists stand ready
  4. The work passes from specialist to specialist
  5. The result is verified and a person approves it
Explore Agent OS
Explore Scrabyt
Talk to ZAVOEN
Explore GTM Engineering
Explore Agent OS

03 — ScrabytLive

Scrabyt Clinical intelligence OS.

Turn conversations into action. The proof that ZAVOEN already ships: in clinics today.

Explore Scrabytscrabyt.com (opens in a new tab)
  1. Voice
  2. Transcript
  3. Context
  4. SOAP
  5. Prescription
  6. Billing
  7. Follow-up

Simulated workflow · Synthetic patient data. Demonstration only. Not medical advice.

01 / 07

From conversation to clinical action.

VoiceConversation captured. The doctor speaks, Scrabyt listens.

TranscriptSound becomes language, as it is said.

ContextWhat was said separates into what it means.

SOAPA SOAP note, coded, drafted for review.

PrescriptionScrabyt proposes. The doctor reviews and signs.

BillingThe bill is raised from the same visit.

Follow-upFollow-up is booked. The clinic moves on.

Consultation noteListening
  1. DoctorWhat brings you in today?
  2. PatientFever for three days, and a dry cough. Mostly at night.
  3. DoctorAny breathlessness or chest pain?
  4. PatientNo. Just tired. I take my sugar tablet twice a day.
  5. DoctorTemperature is 38.4, chest is clear. This looks viral. Paracetamol when needed, plenty of fluids, and we review in two days.

Patient context · extracted from the conversation

Symptoms
Fever × 3 daysDry cough, worse at nightNo breathlessness
History
Type 2 diabetes
Medication
Metformin 500 mg BD
Examination
T 38.4 °CChest clear
S
Fever × 3 days, dry cough, worse at night. No breathlessness, no chest pain. Known type 2 diabetes on metformin.
O
T 38.4 °C · RR 18 · SpO₂ 98% · chest clear on auscultation.
A
Acute upper respiratory infection, likely viral.
P
Symptomatic care, oral fluids, review in 48 h or earlier if breathless.

ICD-10 J06.9 · Acute upper respiratory infection, unspecified

  1. Scrabyt drafts
  2. Doctor reviews
  3. Doctor signs
  • Tab. Paracetamol 650 mgSOS, max 4 per day · 3 days
  • Syp. / Tab. as advised for coughHS · 3 days

Interaction check · against Tab. Metformin 500 mg BD

No interactions found with current medication.

Paracetamol: do not exceed 4 g per day.

Review and sign

Consultation → Billing → Invoice

  • Consultation₹600.00
  • Pharmacy · Tab. Paracetamol 650 mg × 10₹31.50

Invoice total₹631.50

SignedSOAP note · J06.9Rx × 2 · interaction checked
  • RecordsSigned note filed to the patient record
  • PharmacyParacetamol 650 mg × 10
  • Billing₹631.50 · consultation and pharmacy
  • Follow-upWhatsApp check-in · review Thursday 10:30

AI-assisted. Human-reviewed. The doctor signs.

The follow-up opens the next consultation

04 — GTM EngineeringComing soon

GTM Engineering From intent to pipeline.

Connecting GTM data, signals, workflows, AI agents and execution into one operating layer.

Join the waitlistPreview GTM Engineering

Simulated operation · Demo data. Not customer results.

01 / 08

One operation, from intent to pipeline.

DiscoverThe market is swept. The accounts that match become the target set.

EnrichEach account gains context, field by field, with its source.

ResearchSignals converge on each account: hiring, funding, technology, intent.

ScoreSignals are weighed. The accounts reorder, and the best move to the front.

ApproveThe operation stops. A person decides what happens next.

ExecuteApproved accounts are pushed to the destination.

MeasureEvery step of the run is counted.

LearnOutcomes return as signal. The next workflow starts smarter.

Operation #0142 · intent

Find Series B SaaS companies in the UK and Germany with 100–1,000 employees that are hiring AI engineers. Reach the technical leaders, and push accounts scoring above 80 to HubSpot.

Market
UK and Germany
Stage
Series B SaaS
Signal
Hiring AI engineers
Rule
Score above 80
—
Accounts
—
Qualified
—
High intent
—
Approved
—
Executed

A simulated GTM operation, with demo data: 1,284 accounts discovered, 212 qualified, 18 with high intent, 11 approved by a person and 11 pushed to the destination.

  1. Discover: The market is swept. The accounts that match become the target set.
  2. Enrich: Each account gains context, field by field, with its source.
  3. Research: Signals converge on each account: hiring, funding, technology, intent.
  4. Score: Signals are weighed. The accounts reorder, and the best move to the front.
  5. Approve: The operation stops. A person decides what happens next.
  6. Execute: Approved accounts are pushed to the destination.
  7. Measure: Every step of the run is counted.
  8. Learn: Outcomes return as signal. The next workflow starts smarter.

AI proposes. A person approves. Then it executes.

An operating layer

Not another lead database. The system behind GTM work.

  1. 01Experience layerCanvas, copilot, dashboards
  2. 02Intelligence layerAI agents, scoring, signals
  3. 03Execution layerWorkflow engine, approvals, retries
  4. 04GTM data layerAccounts, people, deals, signals
  5. 05Integration layerYour CRM and the tools your team already uses
  6. Sits on top of the CRM, email and enrichment tools a team already uses.

05 — Agent OSComing soon

Agent OS Specialized agents for specialized work.

An operating system for specialist agents. Give it one task; it decides which specialists the work needs, runs them, checks the result and asks a person to approve.

Preview Agent OS

Simulated run · Demo data. Concept.

Task (demo): Prepare the weekly operations review.

  1. TASK: Task received
  2. OS: Routed to 4 specialists
  3. RESEARCH AGENT: Market context summarized
  4. DATA AGENT: Performance metrics extracted
  5. ANALYSIS AGENT: Trends and anomalies identified
  6. REPORT AGENT: Draft presentation generated
  7. VERIFY: Conflicting metrics detected
  8. YOU: Human review required
  9. YOU: Approved
  10. OS: Execution complete

01 · TaskOne task, in plain language.

02 · RouteThe OS breaks it into parts and decides which specialists it needs.

03 · SpecialistsEach specialist does its part and reports back.

04 · VerifyThe results are checked against each other before they count.

05 · ApproveThe run stops. A person decides what happens next.

06 · ExecuteApproved, the result is carried out.

07 · LearnWhat happened is remembered for the next task.

An operating system, not a chatbot.

06 — Intelligence layer

All of it runs through ZAVOEN.

Three systems, one intelligence layer. Context comes in from each of them, a person approves what matters, and only then does anything go back out.

Three systemsThree systems. Each sends what it knows.

01 / 06 · ContextContext arrives from every system.

02 / 06 · UnderstandThe layer works out what it means.

03 / 06 · DecideIt proposes what should happen next.

04 / 06 · ApproveIt stops. A person approves what matters.

05 / 06 · ActApproved, it goes back out: clinical action, pipeline action, operational execution.

06 / 06 · LearnWhat happened returns as context.

Concept view · Select a system

ZAVOENIntelligence layer

Concept diagram. Three systems send signals into the ZAVOEN intelligence layer: Scrabyt sends clinical signals, GTM Engineering sends account signals, and Agent OS sends task signals. The layer runs one loop:

  1. Context: Context arrives from every system.
  2. Understand: The layer works out what it means.
  3. Decide: It proposes what should happen next.
  4. Approve: It stops. A person approves what matters.
  5. Act: Approved, it goes back out: clinical action, pipeline action, operational execution.
  6. Learn: What happened returns as context.

Concept architecture · not a deployment diagram

07 — Technical Architecture

Intelligence primitives.

ZAVOEN provides the essential building blocks for autonomous software — designed for context, reasoning, execution, verification and control.

Ten capabilities · four layers · one core

ZAVOEN provides the essential building blocks for autonomous software — designed for context, reasoning, execution, verification and control.

ZAVOEN Intelligence

Foundation

01Agent Orchestration

Work divided among specialist agents, run in parallel and handed off with its context.

  1. Task decomposition
  2. Agent assignment
  3. Parallel execution
  4. Synthesis
02Context & Memory

What a system has learned about people, accounts and work, recalled when it is needed.

  1. Entity resolution
  2. Recall
  3. Context
  4. Use
03Workflow Intelligence

Multi-step work that turns a plain-language intent into completed, verified actions.

  1. Natural language
  2. Planning
  3. Execution
  4. Verification
  5. Outcome

Understanding

04Multimodal Understanding

Unified reasoning across unstructured text, voice recordings, and visual documents.

  1. Ingestion
  2. Transcription
  3. Documents
  4. Synthesis
05Decision Intelligence

Weighing options and intent, within rules you set, before anything moves.

  1. Intent
  2. Scoring
  3. Rules
  4. Routing

Control

06Evaluation Engine

OUTPUT → PASS · REVIEW · REJECT

Outputs checked against expected outcomes before they count.

  1. Expectation
  2. Execution
  3. Check
  4. Pass / fail
07Human Control

DECISION → HUMAN CHECKPOINT → ACTION

Checkpoints for consequential actions, human-in-the-loop overrides, and auditable states.

  1. Checkpoint reached
  2. Human review
  3. Modification
  4. Approval
  5. Resume

System

08Data → ML

From raw operational data to models that improve the next decision.

  1. Extraction
  2. Preparation
  3. Training
  4. Improvement
09Observability

WHAT · WHERE · WHEN · WHY

Every run traceable: what happened, where, when and why.

  1. Log capture
  2. Metric aggregation
  3. Tracing
  4. Visualization
10Governance

Access by role, protection for personal data, and an audit trail of every action.

  1. Identity
  2. Policy check
  3. Data protection
  4. Audit log

08 — Research & Engineering

The work behind the work.

What we are working through while we build these systems. Notes are published as the work matures; nothing has been published yet.

Research at ZAVOEN

Research in progress01 / 03

How ZAVOEN evaluates multi-step autonomous workflows

Deterministic evaluation, probabilistic scoring, and what it takes to trust a workflow in a high-stakes environment.

ZAVOEN · Engineering · Evaluation

Not yet published

Research in progress02 / 03

Managing context decay in long-running agentic sessions

Techniques for preserving semantic memory and entity resolution across multi-day autonomous workflows.

ZAVOEN · Engineering · Context & memory

Not yet published

Research in progress03 / 03

Probabilistic intent classification in clinical environments

Exploring the boundaries of intent routing when strict compliance and auditable decision trees are non-negotiable.

ZAVOEN · Engineering · Clinical intelligence

Not yet published

09 — Company

We build systems that think in context. And act with control.

ZAVOEN builds intelligent systems for clinical intelligence, revenue operations, and autonomous workflows — designed to move from understanding to action without losing human control.

Founder · Builder

Hello, I’m Sachin.

Most work today is spent moving fragments: from a conversation to a note, from a spreadsheet to a model, from one system to another. People are good at judgement. They should not be the glue.

So we build operating systems that take the fragments and return finished work, and we keep a person at every point where a decision carries weight. The AI drafts. People decide.

What we believe

  1. 01

    Context before action.

    Intelligence starts with understanding.

  2. 02

    Human control is a feature.

    The best automation knows when to stop.

  3. 03

    Intelligence should compound.

    Every workflow should make the next one smarter.

  4. 04

    Measure the system, not the spectacle.

    Useful intelligence leaves evidence.

The ZAVOEN system

Three systems. One way of building them.

  • 01ScrabytClinical intelligence
  • 02GTM EngineeringRevenue intelligence
  • 03Agent OSAutonomous operations
ZAVOEN

Intelligence layer

ContextDecideAct

Human control

Learn ↺

How we work

  1. 01ObserveWatch the work as it is really done.
  2. 02UnderstandFind where judgement is needed.
  3. 03PrototypeBuild the smallest system that works.
  4. 04InstrumentMake every step visible.
  5. 05EvaluateCheck it against an agreed baseline.
  6. 06DeployPut it where the work happens.
  7. 07Learn ↺Feed the outcome back in.

↺ Learn feeds the next Observe

The goal isn’t more software.

It’s less work between knowing and doing.

ZAVOEN is building toward intelligent systems that can understand complex work, coordinate the right actions, and continuously improve — while keeping humans in control of consequential decisions.

ZAVOEN

Intelligence, made operational.

Work with ZAVOEN

Workflow assessment

Bring us one workflow.

We’ll show you where intelligence can remove the work.

Tell us how the work happens today. We’ll help identify where context, automation, evaluation and human control can improve the workflow.

Workflow

One tap is enough · about 20 seconds

Your workflowAssessment preview · how we read a workflow

  1. Input
  2. Context
  3. Repetition
  4. Decision
  5. Human controlA person decides
  6. Action
  7. Outcome ↺

The next system is already moving.

Tell us what should move on its own. A person reads every message.

10 — Contact

Write to us.

We'll show you where intelligence can remove the work. A person reads every message and replies within two working days.

hello@zavoen.com

Write to us