Frequently asked questions

No. Your systems of record, ERP, CRM, WMS, TMS, operational technology, legacy platforms, continue to operate as systems of record. The PolyPhaze Knowledge Fabric™ deploys beneath them as a governed intelligence layer, making them coherent and trustworthy for agents and analysts without replacing, migrating, or disrupting any of them.

Palantir builds a new data ontology on top of the customer's existing data, typically a multi-year program that introduces a Palantir-native operational interface and data model. PolyPhaze governs and connects what the organization already has: existing ERP, CRM, operational systems, and data platforms remain in place and become coherent and trustworthy for AI agents and human decision-makers in 30 to 90 days. The architectures serve different organizational needs. Programs that need a new operational interface built on top of their data are Palantir territory. Programs that need existing data made trustworthy for AI agents and human use, without rearchitecting the data model, are PolyPhaze territory.

Snowflake and Databricks are data storage, processing, and query platforms. PolyPhaze is not a storage platform, it is the trust layer that operates above them and makes the data they hold trustworthy for AI agents and human decision-makers. A data lake holds what humans decided to curate and store. PolyPhaze connects to all the data to discover all the data that the lake was never shown including: contracts, operational data, supplier feeds, sensor data, resolves meaning across system boundaries, and certifies the Trust Score on every piece of context before an agent reads it or acts on it. PolyPhaze integrates with Snowflake and Databricks rather than replacing them.

Most data governance tools document the data estate, they describe what exists, who owns it, and what the policies say. PolyPhaze enforces governance continuously as an architectural property: contracts enforced in software, quality observable per entity per moment, lineage captured at write time, and a self-maintaining catalog that does not depend on quarterly steward reviews to stay current. The distinction is between a governance program and a governance capability. Programs require headcount and executive attention to remain active. The Knowledge Fabric operates independently of both. In most deployments, PolyPhaze becomes the enforcement layer that makes the policies the existing governance tool documents actually operative in the data.

Not necessarily. These platforms operate at different layers and serve different purposes. Microsoft Fabric and Databricks Unity Catalog are data processing and storage platforms with governance features layered on. Collibra is a metadata catalog and governance workflow tool. PolyPhaze is the trust layer beneath all of them, resolving entity meaning across system boundaries, governing both agent reads and writes, computing Trust Scores on every data point, and maintaining the immutable audit trail that makes AI decisions defensible. PolyPhaze integrates with these platforms and in most deployments makes them more productive by ensuring the data they process is trustworthy before they process it.

A federated data architecture is an organizational model: it distributes data ownership to the domain teams closest to each dataset and asks each domain to publish its data as a product. It is a governance philosophy and operating model, not a technology. The Knowledge Fabric is the technology that makes that model trustworthy — applying entity resolution, Trust Scores, and lineage across the distributed domains it creates. Federating ownership without a trust layer just produces distributed data silos instead of centralized ones. The Knowledge Fabric provides the shared definition of quality and the governed exchange between domains that makes federation actually work.

Building the Knowledge Fabric's capabilities, entity resolution without matching keys, continuously-updated Trust Scores, atomic decision records, sub-loop latency trust evaluation, governed writes with Verification, is a multi-year platform engineering effort. The output is a custom system that requires ongoing maintenance as the enterprise's system landscape evolves and as AI model requirements change every few months. PolyPhaze is the result of that investment already made, deployed against existing systems in 30 to 90 days. The build path also forgoes the compounding learning that comes from a platform operating across multiple enterprise deployments. The question is not whether the capabilities can be built, they can. The question is whether the time-to-value, the ongoing maintenance cost, and the opportunity cost of the engineering capacity are worth it compared to deploying a proven platform.

The typical project takes 30 to 90 days. No rip-and-replace. No migration prerequisite. PolyPhaze connects to existing systems in their current form. The first meaningful output typically surfaces within the first week of a pilot deployment. Each product section notes any scenario-specific timing considerations.

PolyPhaze is model-agnostic. Any model, frontier or open source, cloud or on-premise, public or private, can read from and write to the Knowledge Fabric through the Model-Context Protocol layer. Multiple models can run simultaneously against the same fabric. Models change every three to six months. The trusted data foundation underneath them is what stays.

A computed, multi-component confidence rating that travels with every data point and every AI action. Five components: data freshness, source completeness, cross-source consistency, referential integrity, and historical pattern match. Agents gate against it before acting. Executives present it to boards. Auditors trace it to source. It is the mechanism that converts 'I think so' into a defensible, documented number.

Every proposed agent write passes through the Verification layer before it executes. The checkpoint evaluates the action against governing contracts, policies, and Trust Score thresholds. If it passes, the write is atomic, it either completes cleanly or rolls back completely, and generates an immutable audit record at the moment it occurs: what was read, what Trust Score that data carried, what was decided, what was written, and the identity and authorization context of the agent. If it does not pass, the agent receives a governance signal telling it how to route the action appropriately. This checkpoint is architectural. It cannot be bypassed.

Yes. PolyPhaze supports three ingestion modes: streaming for systems with native change-data-capture, polled for systems without it, and federated for systems where data residency or regulatory requirements prohibit replication. In federated mode, the Knowledge Fabric queries data in place. The source remains authoritative and physically undisturbed. Nothing leaves the boundary.

The operating arc of every PolyPhaze workbench and agent output. What surfaces the finding, what the Knowledge Fabric detected in the connected data. So What translates the finding into its P&L or operational implication. Now What recommends the specific action, with suggested owner and timeline, traced to the source data that produced it. Every workbench, every agent recommendation, and every Trust Suite alert follows this arc.

Yes. Zero Trust architecture is foundational, aligned to NIST 800-53, NIST 800-171, and NIST SP 800-207. Attribute-Based Access Control is enforced at the data product level, classification and access permissions travel with the data, no centralized authorization server required. FedRAMP Moderate and CMMC Level 2 certifications are in progress. Federal deployments run on AWS GovCloud. Full compliance posture at polyphaze.com/platform.

The trust layer that makes enterprise data AI-ready. Most platforms that call themselves a "fabric" are storage and query platforms with a governance layer added on top. They hold data, process it, and make it queryable. That is useful. It is not a fabric in any meaningful sense of the word, a fabric connects things and gives them coherence. A storage platform holds things and makes them accessible.

The PolyPhaze Knowledge Fabric™ is the trust layer that makes enterprise data trustworthy before anything reads it, human or agent, and continues governing it when something acts on it. That bidirectional governance is what makes it categorically different from every other platform using the word.

Here is what it actually does that others do not:

It resolves meaning, not just access. The same customer exists in your CRM as "Acme Corp," in your ERP as account ID 42891, and in your contract vault as "Acme Corporation LLC." Every other fabric gives every connected system access to that data. The PolyPhaze Knowledge Fabric resolves those three identities into one canonical entity, continuously, without matching keys, before any agent or analyst ever asks a question. The agent gets a precise answer because the ambiguity was removed before the question was formed. That is entity resolution at the fabric level. No other fabric does this by design.

It certifies confidence, not just currency. Every other platform can tell you when data was last updated. The PolyPhaze Knowledge Fabric computes a PolyPhaze Trust Score™ on every data point, five components, continuously updated: data freshness, source completeness, cross-source consistency, referential integrity, and historical pattern match. That score travels with the data wherever it goes. When an AI agent reads context, it reads the Trust Score alongside it. When the confidence is below threshold, the agent routes to human review rather than acting. No other fabric gives agents a calibrated confidence signal they can reason against and gate against.

It governs the write, not just the read. Every fabric governs what agents read. None of them govern what agents write back, and that is where enterprise AI breaks down at scale. When an AI agent in the PolyPhaze Knowledge Fabric proposes an action, that proposed write passes through the Verification layer before it executes. Verified against governing contracts, policies, and Trust Score thresholds. If it passes, the write is atomic and reversible. If it does not, it stops. And every write generates an immutable audit record at the moment it occurs: what the agent read, what Trust Score that context carried, what it decided, what it wrote. No other fabric addresses the write side with this architecture.

It preserves lineage in both directions. Backward to the source records that produced every insight. Forward through every decision, every action, and every state change that followed. When your board asks why the system did something, or your auditor asks what changed, the answer is one click, available in seconds, regardless of how the underlying systems have evolved since. No reconstruction. No forensics. The record was written at the moment of occurrence.

It serves humans and agents from the same foundation. There is no separate AI data tier in the PolyPhaze Knowledge Fabric. The CFO reading the Optimize workbench and the AI agent generating a supply chain recommendation are operating on exactly the same governed fabric state. One foundation. One Trust Score. One lineage chain. One version of the truth.

That is what a fabric should be. And that is what makes this one different.

Data lakes and warehouses stores and query data. The Knowledge Fabric governs it, continuously, in both directions of a read/write exchange, for both human and machine consumers. It resolves what the data means across system boundaries (entity resolution), certifies how much to trust it (Trust Score), traces where it came from and every transformation applied to it (lineage), and governs what happens when something acts on it (Verification and atomic audit records). None of those properties exist in a data lake or warehouse by design.

Yes, and immediately! PolyPhaze delivers measurable P&L value on the systems already running the business with or without agentic AI. The same Knowledge Fabric that makes AI agents trustworthy also surfaces the revenue leaking through contract gaps, the working capital trapped in disconnected inventory systems, the cost anomalies buried in fragmented spend data, and the operational constraints visible in the data but not surfaced by existing tools. These are findable and actionable now, from the systems already in place. The additional value from agentic AI, agents that act on trusted data without requiring a human at every step, is available when the organization is ready. Building the foundation now means AI deployments move to production the day they are authorized rather than waiting 12 to 18 months for the data layer to catch up.

The Model-Context Protocol is the programmatic interface through which AI agents read from and write to the Knowledge Fabric. It delivers context to agents in the form they require, semantically resolved, freshness-attested, and Trust Score-bearing, and positions the Verification layer between the agent's proposed writes and the systems of record they would affect. Every agent, regardless of which model powers it or which workflow deployed it, reads from and writes to the same governed fabric state through the same interface. MCP matters because it makes the Knowledge Fabric model-agnostic: the trust layer is independent of the agent framework, the model provider, and the application that deployed the agent.

Data freshness is a first-class architectural property of the Knowledge Fabric, not metadata that may or may not be present. Every piece of context an agent reads carries a freshness attestation, a documented statement of when the underlying source data was last confirmed current. Agents can be configured to gate against freshness thresholds: if the data they need to act on is older than the defined window, the agent waits for a current answer rather than proceeding on stale context. Currency of information is a setting, not a hope.

Yes. PolyPhaze is model-agnostic and agent-framework-agnostic. Agents already built on any framework, or any other architecture connect to the Knowledge Fabric through the MCP layer and route their reads and writes. The trust and governance properties apply to any agent that connects, regardless of how it was built or which model powers it.

Nothing changes. The Knowledge Fabric is the foundation the model reads from and writes to. When a new model is deployed, it connects to the same governed fabric state through the same MCP interface. Trust Scores, entity resolutions, lineage chains, and Verification policies do not need to be rebuilt for each new model. The model changes. The trusted foundation underneath it does not.

The PolyPhaze Trust Score™ is the mechanism. Agents can be configured to proceed only when the Trust Score on the data they are reading meets a defined threshold, and to route low-confidence context to human review before acting rather than proceeding on data the system is uncertain about. If the Trust Score on a particular data asset falls below the configured threshold, the agent does not act on it. It routes appropriately. This governance is architectural: it cannot be bypassed by the agent or overridden by the application that deployed it.

The Knowledge Fabric resolves the same real-world entity, a customer, supplier, product, asset, or contract, across every connected system without requiring matching keys. The same company appearing as 'Acme Corp' in the CRM, 'Acme Corporation LLC' in the contract vault, and account ID 42891 in the ERP resolves to one canonical, Trust-Scored record. Entity resolution runs continuously, ahead of any query or agent read, so every consumer receives a coherent answer before the question is asked.

Through Slipstream, the connector layer, which supports nine authentication types and connects to SAP S/4HANA, Oracle EBS, MS Dynamics, Salesforce, NetSuite, Workday, and custom APIs. Data remains in source systems, the Knowledge Fabric reads it and creates a governed semantic layer above it. Source systems are never written to without passing through the Verification layer. Three ingestion modes cover every deployment scenario: streaming, polled, and federated.

The PolyPhaze application that deploys multiple domain agents using the Knowledge Fabric and routes every finding, with Trust Score, lineage, and a specific recommended action, through the What → So What → Now What™ framework. Every finding tells the user not just what is happening, but what it means for the P&L and exactly what to do about it.

Revenue Growth (pricing precision, win-rate lift, retention, cross-sell, share of wallet), Expense Reduction (process automation, fraud detection, leakage, vendor consolidation), Working Capital (DSO compression, inventory rightsizing, payment terms, collections targeting), Capital Efficiency (asset productivity, capex prioritization, capacity utilization), Risk & Compliance (audit observations, regulatory readiness, control drift), Speed & Cycle Time (time-to-decision, time-to-close, time-to-action), and Workforce & Culture, the seventh domain that measures AI adoption velocity and role evolution as agents absorb analytical work.

The full leadership team and the operators who report to them: CEO, CFO, CRO, COO, CPO, CDO, CHRO, and the FP&A and analytics teams that brief them. Each workbench is persona-tuned: the CFO sees EBITDA impact and balance sheet implications; the VP of Sales sees win rate and pipeline conversion; the VP of Operations sees cycle time and throughput. The same governed data surfaces differently to each role.

Dashboards and BI tools answer the What which can someone to understand what happened. OPTIMIZE adds the So What (what it means for the P&L) and the Now What (the specific recommended action(s) with owner and timeline). That is the What → So What → Now What™ arc. PolyPhaze is infrastructure for action, not a tool for observation. The workbench does not stop at the insight. It routes to the decision.

Override capture records the reason. Every missed recommendation enters a continuous improvement loop, the reason informs future recommendations, making the next cycle more accurately calibrated to how the organization actually makes decisions. The platform improves from every outcome, including the ones where it was overridden.

PolyPhaze's application for the full deal lifecycle, structured around the three phases of an acquisition and the very different data access rules that govern each one. During diligence, when buyers are legally restricted to what the seller has made available in the data room, PolyPhaze reads, reconciles, and analyzes those documents to find the inconsistencies, entity conflicts, and gaps between stated assumptions and what the data actually supports. After close, when the buyer has legitimate access to the target's operational systems for the first time, PolyPhaze connects to those systems and compares what they actually show against what the data room represented. We call this, ensuring the Words & Music Match. For organizations that are looking to create value and sell in the future, such as a Private Equity (PE) firm would do, they typically have what they call a ‘hold period,’ During that period, PolyPhaze tracks actual performance against the Value Creation Plan with a Trust Score on every number, so that at exit ,the financial story is already documented and auditable rather than assembled from scratch.

During diligence, PolyPhaze works exclusively with materials the seller has made available in the data room. These data rooms typically contain audited and management financial statements, customer lists and revenue by account, contracts with key customers and suppliers, employee records and compensation summaries, operational metrics, and due diligence questionnaire responses. PolyPhaze reads all of these documents and works with what is there, it does not supplement data room materials with information from outside the data room during the diligence phase.

Data rooms are assembled by the seller or their representative, and sellers often present information in the most favorable light. PolyPhaze reads every document and looks for three categories of issue. First, internal inconsistencies, the same number appearing differently in different documents. A customer count in one report that does not match the revenue by customer in another. Headcount in the HR summary that does not align with the compensation expense in the P&L. Second, entity conflicts, the same customer, supplier, or contract appearing under different names or identifiers across different documents. PolyPhaze resolves these into unified records and flags any resolution that is uncertain. Third, gaps between stated assumptions and what the data actually supports, synergy hypotheses built on customer overlap the data room does not corroborate, margin assumptions the operational data does not support, or revenue projections that imply retention rates the historical data does not reflect. Every finding carries a Trust Score so the deal team knows which concerns are well-evidenced and which require more information from the seller.

A post-close capability that runs after the deal has closed and the buyer has legal access to the target's operational systems for the first time. During diligence, the buyer saw what the data room represented. After close, the buyer can see what the target's systems actually show. Words & Music Match compares those two pictures and surfaces every place they diverge, customer concentration stated as 38% in the data room, the billing system showing 44%; working capital represented one way in the quality of earnings, the actual system reflecting different payment terms; revenue quality summarized in management's reports, the actual invoicing data telling a more nuanced story. These variances are not always bad surprises. But they are almost always have a few surprises, and surfacing them in the first few days after close gives the integration team time to respond before they become operational or financial issues.

Integration is where deals succeed or fail at the data layer. Two companies carry different definitions of the same entity, the same customer in one company's ERP is a different record in the other's CRM, the same supplier under different names, products coded differently across systems. The entity resolution work done during diligence carries forward into integration and gives the team a documented head start on those mapping problems before they encounter them in live systems. After close, PolyPhaze connects to the target's actual operational systems and continues entity resolution against live data, producing a single governed view of the combined entity that both teams can work from.

For organizations consolidating systems, merging ERPs, migrating the target onto the acquirer's platforms, retiring legacy applications, the PolyPhaze MIGRATE application is included in the M&A package. It deploys at the start of the consolidation work, profiles what is actually in each source system before any records move, surfaces the entity conflicts and undocumented dependencies that would otherwise become cutover surprises, and runs continuous reconciliation during the migration window. The entity resolutions from diligence and early integration are the starting point, the migration does not rebuild what the deal team already mapped. At go-live, the destination system carries a Trust Score on every data element, and the same governed fabric persists as the operational intelligence layer going forward.

During diligence, the deal team often develops a Value Creation Plan (VCP), specific synergies expected from the deal, quantified and timed. PolyPhaze locks that plan at close as an immutable baseline. Every month for the life of the hold, the Synergy Capture Agent measures actual performance against that locked model and produces a current view of what has been captured, what is on track, and what is at risk, with a Trust Score on every measurement. When a synergy claim is well-supported by operational data, the Trust Score is high. When a claimed capture is not yet visible in the numbers, the Trust Score reflects that uncertainty. At exit, this continuous record becomes the sell-side EBITDA bridge: the documented path from acquisition EBITDA through every improvement made during the hold, already traceable and auditable rather than assembled for the first time in the sale process.

Both, though the deployment model do differ. Strategic acquirers typically deploy it transaction by transaction, setting up a new diligence workspace for each deal and carrying it through integration into the combined business's operational intelligence layer. PE sponsors typically deploy it once as a fund-level operating standard, applying the same architecture to every portfolio company from the moment of acquisition. This gives the sponsor a consistent view across the portfolio: value creation plan execution tracked at the company level, performance metrics aggregated at the fund level for LP reporting, with no company-level data crossing deal boundaries. The PE use case is particularly valuable at exit, a company that has been running PolyPhaze through its hold period arrives at the sale process with a documented, Trust-Scored record of every value creation initiative and its outcome. That is the most compelling form of sell-side preparation because it was produced in real time, not assembled after the fact.

A buyer's diligence team will ask for evidence of every claimed value creation achievement. If that evidence was documented continuously, month by month or quarter by quarter, with a Trust Score on every data point, the answers are already there and already auditable. PolyPhaze produces the EBITDA bridge at exit from the governed data foundation that has been running throughout the hold: the documented path from acquisition EBITDA through every improvement, with full traceability to the operational data that supports each claim. The sell-side preparation cycle compresses significantly because the work is already done. And sophisticated buyers who run PolyPhaze-based diligence will find exactly what the seller represented, because the underlying record is governed, Trust-Scored, and traceable to source.

At the start, before the destination schema is committed. The most expensive migration failures surface during cutover because the source data was more complex, more inconsistent, and more entangled than the migration plan assumed. Sampled assessments miss the long tail. PolyPhaze profiles the source exhaustively in the discovery phase, surfacing entity conflicts, undocumented dependencies, and business rules enforced by application code, when there is still time to plan for them. Engaging at cutover means rebuilding trust from scratch on the new system. Engaging at the start means the trust posture is established before any record moves.

Legacy retirement, cloud migration, SAP ECC to SAP RISE, M&A system consolidation, and any application migration where data quality and referential integrity at the destination really matter. The platform is source and destination-agnostic, it deploys beneath whatever ERP, CRM, claims platform, or other data store the migration uses.

The platform profiles the full ECC data estate in the discovery phase, surfacing entity master conflicts, accumulated customizations, and undocumented business rules before any migration decision is committed. Every scope decision (migrate, transform, consolidate, or retire) is recorded as an atomic immutable record with full rationale. The dual-namespace reconciliation layer runs continuously during the S/4HANA cutover window. The Trust Score on the destination data is at parity with or exceeding the source on day one of go-live. Post-migration, the Knowledge Fabric persists as the RISE governance layer, the migration investment compounds into ongoing operations.

The source namespace reads from the legacy system continuously throughout the migration. The target namespace activates at cutover start and reads from the destination. The Reconciliation Agent queries both namespaces simultaneously, running every 15 minutes during active cutover windows, comparing record counts, data elements, checksums, key completeness, and business rule compliance. Exceptions surface in real time with specific remediation, not 60 minutes later when a rollback may be the only option remaining.

The same Knowledge Fabric that governed the migration persists as the operational intelligence layer over the destination system. Entity resolutions, Trust Scores, and lineage chains built during migration are the starting point of every future Optimize recommendation. Migration is the entry point. The trust posture is permanent.

Most companies are sitting on data they have spent years collecting, about their customers, their products, their markets, their operations, and using it only internally. Data Commerce is the PolyPhaze application that helps organizations turn that internal data into something they can sell.

A company that has been selling insurance for twenty years has built a detailed picture of risk, claims, and customer behavior that agents and brokers would pay to access. A distributor who has moved products through retailers for a decade knows things about demand and shelf performance those retailers would find valuable. A manufacturer tracking equipment across thousands of installations has reliability data that parts suppliers and service providers would buy. Most organizations have something like this. The barrier has never been the data, it has been the infrastructure required to deliver it externally in a way that is trustworthy, compliant, and governed well enough to sell.

Data Commerce builds that infrastructure. It takes internal data, enriched with external data where relevant, and packages it into a live, continuously-updated product that can be licensed to existing customers, partners, or entirely new markets. It classifies what is sensitive and protects it before anything leaves. It enforces different rules for different recipients. It monitors every delivery for quality. And it attaches a PolyPhaze Trust Score™ to every data point, so recipients know exactly how much to rely on what they are receiving.

The result is a new revenue line built on an asset the organization already owns.

Chief Product Officers (CPO) building new revenue lines from existing data assets, Chief Revenue and Chief Commercial Officers expanding into data-driven commercial relationships, and CDOs productizing the organization's data estate. The barrier to productizing internal data has never been the data itself, it is the trust layer required to deliver it externally with defensible provenance, enforced compliance, and contractual defensibility. Data Commerce is that layer.

It changes with it. A Data Commerce product is a live, governed view of the Knowledge Fabric state for a defined scope, not a copy, not an export. Source data changes propagate continuously. The Freshness Monitor holds delivery automatically if the staleness SLA is breached, with no manual intervention required.

A compliance agent classifies every candidate field for PII, PHI, and regulatory exposure before any product is published. When uncertain, the default treatment is exclude, not pass-through. GDPR, HIPAA, CCPA, NAIC, and GLBA obligations are enforced at the fabric layer. Entitlements are per-recipient: the same field can pass through for one recipient and be tokenized for another.

The Trust Suite is the PolyPhaze capability that makes those questions answerable, not through periodic reports assembled after the fact, but through continuous, automated evidence collection and enforcement built into the architecture.

It operates at two levels simultaneously. At the boundary of the organization, it governs compliance evidence, AI agent activity, and security posture, catching issues at the perimeter before they become incidents. Inside the Knowledge Fabric, it governs data quality, AI decision behavior, and operational anomalies, monitoring the interior continuously so that every AI action and every data-driven decision is traceable and defensible. The entire suite deploys inside the customer's own environment. Nothing leaves the boundary.

The Trust Suite is designed to make the records in GRC tools like ServiceNow, Archer, and Vanta more defensible, not to replace them. It produces continuous, structured evidence output in standard formats that import directly into major GRC platforms, and can push alerts and control status updates in real time via webhook or API. Organizations that already have GRC workflows in place find that the Trust Suite provides the continuous evidence collection layer those workflows have always assumed but rarely had. The GRC tool manages the process. The Trust Suite provides the proof.

Most AI governance tools monitor the model; they watch what the model predicts and flag when predictions drift. The Trust Suite takes a very different approach: it monitors the impact of AI decisions at the data layer, which is the only place where impact can be consistently measured regardless of which system produced it or whether it was sanctioned. Every consequential AI decision leaves a fingerprint in the data, a price changes, an order is placed, a credit limit is modified. The Trust Suite detects those fingerprints against an established baseline, identifies when AI activity is operating outside its intended boundaries, and surfaces the evidence needed to respond, whether the activity came from a governed PolyPhaze agent, a third-party system, or an unsanctioned tool running outside the organization's knowledge. No model access required.

The PolyPhaze application that creates a continuously-updated, Trust-Scored digital model of every physical asset by resolving entities across every system that touches it, CMMS, ERP, OEM technical databases, condition monitoring historians, operations systems, and regulatory records, into a single governed view. Every sensor reading is Trust-Scored. Every maintenance action is lineage-documented. Every compliance status is traceable to the record that produced it.

Asset Twin builds a live, trusted digital model of any physical asset in any asset-intensive industry, configured on the same Knowledge Fabric architecture regardless of what the asset is or where it operates. Any industry where assets have maintenance histories, regulatory obligations, condition monitoring, and operational records spread across multiple systems is a candidate.

Examples of where it can be helpful include: Manufacturing, production facilities, plant equipment, and OEM operations. Energy and oil and gas, wells, compressor stations, pipelines, processing facilities, and offshore platforms. Transportation and vehicles, commercial trucking, passenger vehicles, commercial aviation, maritime vessels, and military weapon systems including aircraft, ground vehicles, and naval platforms. Every asset class carries its own regulatory framework, FAA and EASA airworthiness directives, DOT and FMCSA compliance, classification society certification, and defense sustainment and readiness requirements, and Asset Twin is configured for each.

The underlying capability is the same regardless of the industry. One authoritative, Trust-Scored asset, resolving its identity across every system that touches it, maintenance, ERP, OEM technical databases, condition monitoring, and regulatory records, with every event lineage-documented and every compliance status traceable to the record that produced it. If there is a physical asset generating data across disconnected systems, Asset Twin can govern it.

The Asset Twin resolves the complete history of a physical asset, serial number, operating history, OEM life limits, maintenance events, and condition monitoring data, into one canonical Trust-Scored record. Predictive maintenance agents compare that record against failure-mode baselines and OEM intervals and route actionable findings with Trust Score, owner, and timeline to the maintenance workbench, before the failure event, not after.

Component-level tracking follows serialized parts across asset assignments. The maintenance and operational history of a component travels with the component when it moves between platforms, it is never lost because the asset assignment changed. The same engine's airworthiness directive compliance, OEM life limits, and maintenance history remain intact and traceable wherever it is installed.

The defense expression of the PolyPhaze Knowledge Fabric™, a trusted sustainment intelligence layer that connects to a program's maintenance, logistics, and readiness systems and gives the program office a complete, governed, real-time readiness picture under its own authority.

The Mission Readiness Platform™ is available through Other Transaction Agreements (OTA), SBIR/STTR phases including Direct-to-Phase II, and GSA Schedule vehicles. PolyPhaze is an active SBIR Phase II/III performer with the U.S. Air Force Rapid Sustainment Office. Prime/sub teaming arrangements are available with established defense and aerospace integrators. For federal procurement offices, the PolyPhaze account team can provide current vehicle availability and period of performance terms.

Organic data intelligence means the program office owns its own readiness picture, not a contractor, not a vendor, not a derived report. The Mission Readiness Platform™ is the operational form of that mandate: sustainment systems remain in place, the platform connects to them and reconciles them, and the program office holds the governed, lineage-documented readiness intelligence under its own authority. Every answer trace to the source systems that produced it.

FedRAMP Moderate and CMMC Level 2 certifications are in progress. Architecture is aligned to NIST 800-53 Rev 5, NIST 800-171 Rev 2, and NIST SP 800-207 Zero Trust today. AWS GovCloud is the federal deployment environment. ABAC security is embedded at the data product level, classification travels with the data, CAC/PKI, NIPR, SIPR, and cross-domain compatible. Full compliance posture at polyphaze.com/platform.

No. The Mission Readiness Platform™ sits beneath the systems already running the program, connects to them, reconciles them, and traces every answer back to source. Sustainment systems remain the systems of record.

PolyPhaze pricing is configured to deployment scope: the systems connected, the applications deployed, and the scale of the enterprise. There is no per-user pricing model, the Knowledge Fabric serves every authorized consumer from one governed foundation, and usage does not create marginal cost at the consumer level. Contact PolyPhaze at info@polyphaze.com for a scoped commercial proposal.

No. Pricing is not structured on a per-user or per-data-volume basis. The commercial model is configured to the deployment scope, the systems connected, the PolyPhaze applications deployed, and the scale of the enterprise. Adding business users or connecting additional workbench consumers does not scale linearly with headcount.

Pilots are scoped individually based on the systems being connected and the use case(s) being validated. A typical 30-day pilot connects two to three source systems, deploys against one defined use case, and produces Trust-Scored findings against the customer's actual data, not synthetic data, not a demo environment. Pilot pricing is structured to make the value demonstrable before a full commercial commitment and the cost is applied to the first year’s pricing. Contact polyphaze.com for pilot scoping and pricing.

Enterprise deployments are structured as annual software agreements covering the Knowledge Fabric and the PolyPhaze applications deployed. Professional services for deployment are available from PolyPhaze directly and through channel partners. Federal deployments are available through OTA, SBIR, and GSA Schedule vehicles. Multi-year agreements are available with favorable terms for longer commitments.

We provide a 30-day pilot on the customer's own systems against a defined use case. Pilot artifacts, findings, Trust Scores, lineage chains, and the initial entity resolution model, belong to the customer regardless of what follows commercially. There is a charge for time and effort to produce and run the pilot as it is designed to produce something useful in the first week and a documented business case by the end of day 30.

PolyPhaze deploys through a structured implementation process led by PolyPhaze solution architects, with channel partner delivery available for large-scale or federal engagements. The typical customer-side team is a technical lead (CTO or VP Engineering designation), a data lead (CDO or head of data engineering), and an executive sponsor. PolyPhaze does not require a large internal team to stand up, the platform is designed to connect to existing systems rather than requiring significant internal development.

We typically require three things: system access credentials for the source systems being connected, a defined primary use case for the pilot, and an executive sponsor who can unblock access and prioritize the engagement internally. PolyPhaze handles the connection, configuration, and initial entity resolution. No data preparation, data cleaning, or integration build is required from the customer before deployment starts.

Minimally disruptive by design. PolyPhaze reads source systems without writing to them, modifying them, or requiring downtime. Source system teams are involved in providing access credentials and reviewing connection configurations, typically a few hours of effort, not a sustained engagement. Business users interact with a live system from day one rather than a staging environment. There is no migration phase, no data preparation phase, and no parallel-run period.

No dedicated headcount is required. The Knowledge Fabric maintains itself, catalog maintenance, entity resolution, Trust Score computation, and contract enforcement run autonomously. Post-deployment, the internal effort concentrates on reviewing exceptions the platform surfaces and acting on workbench findings. That work happens within existing roles rather than requiring a new function. Typical post-deployment internal effort is a few hours per week per business domain covered.

PolyPhaze provides structured onboarding for both the technical team (connector configuration, namespace management, Trust Score threshold configuration) and the business workbench users (workbench navigation, finding interpretation, action routing). Training is delivered as part of the implementation and tailored to the specific applications deployed. Ongoing documentation and release notes are available through the PolyPhaze customer portal. Partner-specific enablement programs are available for channel and federal deployments.

All three. PolyPhaze can deploy in a private cloud in the customer's own cloud environment, and on-premise for (edge) environments where external connectivity is restricted or prohibited, or in public cloud providers such as GPC, AWS, Azure. Federal deployments run on AWS GovCloud.

Air-gapped deployments are supported; a single configuration flag disables all outbound calls. The deployment model is a deployment-time decision, not an architectural constraint.

The Knowledge Fabric is designed to be self-maintaining: catalog updates, entity resolution, Trust Score computation, and contract enforcement run autonomously. Software updates and enhancements are delivered continuously in SaaS deployments and as versioned releases for on-premise and air-gapped environments. No customer-side redeployment is required for Trust Score model updates or agent improvements, these are delivered at the platform layer.

Recovery point objectives and recovery time objectives are configured to deployment requirements. Because PolyPhaze reads source systems rather than holding the authoritative copy of enterprise data, the disaster recovery concern is restoring the governed intelligence layer, the source data itself remains in the customer's own systems throughout.

DATA PRIVACY & SOVEREIGNTY

PolyPhaze does not store a persistent copy of customer operational data. The Knowledge Fabric reads source systems and maintains the governed intelligence layer, entity resolutions, Trust Scores, lineage chains, and audit records, but the underlying operational data remains in the customer's source systems. By default, data lives in the fabric only as long as the current operation requires it. Once the operation completes, the data is released. SaaS deployments run on AWS infrastructure in the customer's designated region. On-premise and private cloud deployments run entirely within the customer's own environment.

PolyPhaze reads source systems and holds it while it uses it; it does not replicate or store a persistent copy of operational data. The fabric maintains computed artifacts, entity resolutions, Trust Scores, lineage metadata, and audit records, but not copies of the underlying source records.

Because PolyPhaze does not store a persistent copy of customer operational data, data subject request handling, right of access, right to erasure, right to rectification, executes at the source system level. PolyPhaze assists with identifying where a data subject's records appear across connected systems through the entity resolution layer, which can make the data subject request process faster and more complete than manual searching across systems. Compliance treatment and PII classification run at the fabric layer in Data Commerce deployments, ensuring no PII passes through a data product without explicit field-level authorization.

Yes. Cloud deployments can be configured to specific regions to meet data residency requirements. On-premise and private cloud deployments run entirely within the customer's own geographic boundary. The platform architecture ensures that data governed by strict residency requirements never leaves its home environment.

Platform telemetry, system performance, connection health, error logs, is collected for operational support purposes. No customer operational data is accessed or used for any purpose outside the customer's deployment unless agreed to by customer. No customer data is used to train PolyPhaze platform models or shared with any third party.

Full data handling terms are in the PolyPhaze Master Service Agreement.

DATA GOVERNANCE & MANAGEMENT

Governance is built within the platform as architecture. Traditional governance programs require sustained human attention to stay active; they decay when the executive sponsor moves on or the budget tightens. PolyPhaze governance is enforced continuously in software, at the data layer, through agents and the governed fabric architecture. Data contracts are enforced the moment they are defined. Trust Scores update continuously without human intervention. Agents monitors the data estate and routes genuine exceptions, decisions that require business context and organizational judgment, to the appropriate human decision-maker. Governance becomes a durable property of the data environment rather than a program that requires organizational attention to sustain.

Traditional governance programs document the data estate and define policies. PolyPhaze enforces them, continuously, in software, through agents that runs at the fabric layer. Data quality in a traditional program is a scorecard produced periodically. In the Knowledge Fabric, it is the Trust Score, computed per data asset, per moment, across multiple measurable components. Data contracts in a traditional program are policy documents that rely on human review to detect violations. In the Knowledge Fabric they are enforced in software, with violations surfaced the moment they occur. The fundamental difference is that PolyPhaze governance runs whether or not anyone is attending to it. No sponsorship. No headcount. No decay.

Typical data governance failed for a structural reason that PolyPhaze addresses architecturally. Governance programs that don’t survive often do so because it depended on human activity, catalog curation, policy review, committee cycles, executive sponsorship, to remain active. When those inputs reduced, the program decayed. PolyPhaze removes that dependency entirely. The AI agents handles the work that previously required a dedicated human function.

Four things happen simultaneously. The Trust Score for the affected data asset updates to reflect the degradation, so every consumer reading that asset reads the lower confidence signal alongside the data. Any data contracts governing that asset are evaluated for breach, violations are surfaced to the contracting parties immediately by AI agents. Any AI agents reading that asset gate against their configured Trust Score threshold and route to human review rather than acting on low-confidence data. And if the asset is feeding a Data Commerce product, the Trust Score Guard holds delivery automatically until quality is restored. The human governance team does not need to detect the problem, the platform detected it, adjusted the confidence signal, notified the affected parties, and protected the downstream consumers before anyone was asked to act.

Attribute-level lineage. Every claim in a workbench finding or AI recommendation traces to the specific field, in the specific source record, in the specific source system that produced it, with every transformation documented between that source and the output. The lineage chain is bidirectional: backward to source and forward through every decision, action, and system write that followed.

Yes. Lineage and audit records are exportable in structured formats for regulatory submission, auditor review, and GRC platform integration. The Knowledge Fabric generates audit packets on demand, complete lineage chains, Trust Score history, transformation records, and decision records, for any data asset or business decision, at any point in the past, regardless of how the underlying systems have evolved since. For regulatory frameworks that mandate lineage documentation, BCBS 239, DORA, GDPR Article 30, SEC Rule 17a-4, PolyPhaze can provide framework-specific export configurations.