01 · MASTOS
Most AI is trained on the world's data.
The ones that win are trained on yours.
Purpose-built small language models, trained on your domain. Acquisition, communications, and workflows on a single platform.
PROOF · IN PRODUCTION
INTERACTIONS
500K+
Autonomous, across voice, SMS, and digital.
BOT-TO-HUMAN RATIO
1:24
One human covers what 24 bots resolve.
INBOUND DEFLECTION
70%
Volume resolved before reaching a human.
PHI EXPOSURE
ZERO
By design. Across every capability.
02 · CAPABILITIES
One platform. Five capabilities.
Acquisition through governance, running on a single intelligence layer. Each capability does one job well. All five share the same models, the same data, the same audit trail.
Capability 01
Acquisition.
Multi-channel patient and customer acquisition. Autonomous outreach, qualification, and conversion across voice, SMS, and digital. Production-proven at 500K+ interactions.
OUTREACH · QUALIFY · CONVERT
Capability 02
Lifecycle.
Engagement across the full customer journey. Reactivation, retention, scheduled communication, behavioral triggers. Built for systems where every touch is logged and governed.
REACTIVATE · RETAIN · TRIGGER
Capability 03
Orchestration.
Intelligent triage and routing. Inbound volume classified, prioritized, and directed to the right resource. 70% deflection in production. Sub-1% error rate.
CLASSIFY · PRIORITIZE · ROUTE
Capability 04
Clinical intelligence.
The domain-intelligence layer tuned for healthcare. It learns from encounters, outcomes, and workflows to classify cases, surface risk, and keep every care loop inside the rules.
ENCOUNTERS · OUTCOMES · WORKFLOWS
Capability 05
Governance.
PHI firewall, audit trails, policy enforcement. Built into the platform, not bolted on after. Compliance as a system property across every other capability.
AUDIT · ENFORCE · CONTAIN
03 · HOW IT WORKS
One operating layer. Many environments. Zero PHI exposure.
mastOS replaces fragmented point tools with a single intelligence layer. Shared models. Isolated data. A taxonomy that gets sharper with every interaction.
AT THE ORGANIZATIONAL LEVEL
From fragmented stack to operating layer.
BEFORE
A grid of point tools.
CRM
Helpdesk
Chatbot A
Chatbot B
SMS Tool
Email Platform
Voice IVR
Spreadsheets
Custom scripts
Each tool owns its own data. Each integration is a project. Each handoff is manual. The team operates the tools instead of the work.
AFTER · MASTOS
One operating layer.
SINGLE INTELLIGENCE LAYER
mastOS
ACQUISITION · LIFECYCLE · ORCHESTRATION · INTELLIGENCE · GOVERNANCE
All channels feed the same brain. All decisions share the same taxonomy. All actions are governed by the same audit trail. The team operates the work, not the tools.
AT THE TECHNOLOGICAL LEVEL
Shared platform. Isolated tenants. A loop that learns.
The platform is built once and deployed per tenant. The intelligence layer is shared infrastructure. Customer data lives in sealed environments. Every interaction sharpens the system without exposing what the underlying data contains.
SHARED LAYER
Built once. Deployed everywhere.
Engines, models, and taxonomy are shared infrastructure. Every Harbor inherits the same intelligence and the same governance. New capabilities ship to every tenant simultaneously.
ENGINES · MODELS · TAXONOMY · GOVERNANCE
ISOLATED TENANTS
Each Harbor is sealed.
Per-tenant operational stores, separate data lake containers, isolated networks, customer-managed keys. Client A data never crosses Client B infrastructure. Same templates, different sealed environment per Harbor.
DEDICATED STORES · CMK · SEALED NETWORK
CONTINUOUS LOOP
AI takes it as far as it can. Humans inherit the context.
The taxonomy sharpens with every encounter. When the work escalates to a human, the full context travels with it — across channels, across teams, across the patient's history. One thread. Every touchpoint.
HISTORICAL → PREDICTIVE → PERSONALIZED
WHY A SMALL LANGUAGE MODEL
Large models know the internet. Small models know your business.
LARGE LANGUAGE MODEL
Trained on the open internet
General-purpose intelligence
Broad context, shallow accuracy
Hard to govern, opaque outputs
Expensive at scale
SMALL LANGUAGE MODEL · MASTOS
Trained on your domain
Purpose-built for your workflows
Narrow context, deep accuracy
Auditable, explainable, controlled
Efficient at scale
ZERO PHI ARCHITECTURE
PHI never enters the intelligence layer.
Shared infrastructure runs on de-identified payloads and identity tokens. Raw clinical content stays in the Harbor zone — encrypted with customer-managed keys, behind isolated network boundaries. Zero PHI by design.
SHARED · MAST-LEVEL
Intelligence layer
Engines, models, taxonomy, vector search, embeddings. Operates on de-identified payloads and identity tokens only.
DE-IDENTIFIED · ID-BASED
REDACTION
BOUNDARY
NO PHI
CROSSES
ISOLATED · PER-HARBOR
Harbor PHI zone
Patient records, clinical content, EHR-bound data. Customer-managed encryption keys, isolated network, sealed tenant per Harbor.
ENCRYPTED · CMK · SEALED
Principle 01
Redaction at the edge.
Inbound payloads are de-identified before they touch the operational layer. The operational store and the event bus never see raw PHI.
INGEST → DE-IDENTIFY → PERSIST
Principle 02
Identity, not PHI.
Engines operate on identity tokens — harbor_id, patient_id, session_id. Decisions happen without reading clinical content.
TOKENS · NOT CONTENT
Principle 03
Separation of layers.
Logic is shared infrastructure. PHI lives only in the Harbor zone, encrypted with customer-managed keys, behind isolated network boundaries.
SHARED LOGIC · ISOLATED DATA
HIPAA-certified · Azure HIPAA-compliant · TLS 1.2 minimum · Encrypted in transit and at rest · CMK at Harbor provisioning · 7-year retention · Soft-delete only
04 · CONNECT
See it run.
mastOS is live in production. The platform site is launching soon. Watch the build, follow the roadmap, and reach out when your problem fits.
Explore mastOS.ai →
For
Healthcare operations leaders replacing fragmented AI tools.
CTOs evaluating production AI infrastructure in regulated environments.
Founders building in healthcare or regulated industries.