"ArcXA Model Service is the policy enforcement point between enterprise data and AI — ensuring every SQL-sourced insight is governed, traced, and explainable."
ArcXA Model Service (AMS) — SQL-to-AI Governance Value
ArcXA bridges the gap between structured enterprise data and AI consumption — with full auditability at every layer.
ArcXA creates value across the stack:
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1. Semantic Grounding for NLP-to-SQL
Raw SQL queries have no meaning — they're just syntax. AMS wraps SQL assets with semantic metadata (via the SPO triple store), so when an AI or LLM generates or receives SQL:
- It knows what a table or column actually means in business context
- It can validate intent vs. schema before execution
- Hallucinated or malformed queries get caught at the governance layer
Value: AI agents stop "guessing" at schema — they operate against a governed semantic map.
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2. Data Lineage & Provenance for AI Outputs
When AI generates insights from SQL-derived data, the model service tracks:
- Which SQL query sourced the data
- What transformations occurred
- Who authorized the query and when
Value: Defense and enterprise buyers get explainable AI — every AI output can be traced to its governed data origin.
3. MCP Integration Layer
ArcXA's model service acts as a governed MCP connector — AI agents (Claude, GPT, custom LLMs) talk to ArcXA instead of raw databases, meaning:
- Access control is enforced before SQL hits the data layer
- Query patterns are logged for compliance (CMMC, FedRAMP, FISMA)
- The AI never gets "raw" access — it gets governed, policy-filtered access
Value: Dramatically lowers risk for DoD and regulated enterprise deployments.
4. Schema Discovery → AI Readiness Scoring
AMS can assess existing SQL schemas and score them for AI readiness:
- Identifies ambiguous or undocumented columns
- Flags data quality issues that would corrupt AI outputs
- Recommends semantic enrichment targets
Value: Converts the Migration Readiness Assessment into an AI Readiness Assessment — a natural upsell motion.
5. Institutional Memory Preservation
For legacy systems (DB2, AS400, Oracle), SQL encodes decades of business logic that lives nowhere else. ArcXA governs and exposes that logic to AI without requiring a full migration:
- Legacy SQL gets mapped into the KGNN knowledge graph
- AI can query institutional knowledge that was previously inaccessible
- Migration can happen incrementally, governed at every step
IBM's P11 methodology is fundamentally a structured knowledge transfer and migration framework.
ArcXA's SPO triple store + KGNN is a semantic capture and resolution engine.
The pairing works because every P11 engagement generates institutional knowledge (schemas, dependencies, configurations, entitlements, runbooks) that currently lives in documents, tribal memory, and disconnected tools — exactly what ArcXA was built to ingest, map, and make queryable.
• Passport Advantage • Lab Services • Tech Life Services (TLS)
Passport Advantage
The problem: Passport Advantage manages software licensing entitlements across complex enterprise portfolios. As clients migrate to IBM Power11, cloud, or hybrid environments, license mapping breaks — what was entitled on the old platform doesn't automatically translate to the new one, and nobody has a clean semantic map of what's deployed vs. what's licensed vs. what's actually being used.
ArcXA + KGNN helps:
- Entitlement graph mapping — ArcXA ingests Passport Advantage entitlement records and builds an SPO graph:
License(S) → entitles → Workload(O). The KGNN then resolves which deployed workloads on the legacy system map to which PA entitlements on the target platform, surfacing gaps and over-licensing automatically. - Migration Readiness Assessment (MRA) integration — ArcXA's MRA module can score each licensed product for migration complexity, dependency depth, and compliance risk before the migration begins.
- NLP query interface for license managers — non-technical license compliance officers can ask "which entitlements expire within 90 days of our go-live date?" without needing a DBA or spreadsheet analyst.
- Audit trail — every entitlement resolution decision is captured in the lineage graph, creating a defensible record for IBM audit events.
Lab Services
Problem: IBM Lab Services engagements involve deep technical assessment — architecture reviews, performance benchmarking, proof-of-concept builds, and integration testing. The outputs are rich but ephemeral: findings live in engagement reports, not in a queryable knowledge layer that persists across engagements or feeds the next project.
How ArcXA + KGNN helps:
- Engagement knowledge capture — ArcXA ingests Lab Services assessment artifacts (architecture diagrams, schema exports, dependency maps, test results) and structures them as SPO triples. The KGNN links entities across engagements:
System(S) → depends_on → Component(O)becomes institutional memory, not a PDF. - Cross-engagement pattern recognition — the KGNN identifies recurring migration blockers across the Lab Services client base (e.g., "DB2 stored procedures with embedded business logic appear in 73% of IBM i modernization engagements") — turning individual assessments into a learning network.
- Accelerated PoC scoping — when a new Lab Services engagement begins, ArcXA can query the knowledge graph for analogous prior architectures and pre-populate the assessment framework, compressing the discovery phase.
- IBM Power11 optimization mapping — ArcXA's schema discovery + KGNN can map legacy workloads to Power11 deployment profiles (bare metal, LPARs, cloud) and recommend optimal configurations based on observed dependency patterns.
Tech Life Services (TLS)
Problem: TLS manages the full hardware and software lifecycle — from procurement through decommission. The critical gap is lifecycle intelligence: understanding what's running on aging infrastructure, what its dependencies are, what the migration path looks like, and when the risk of staying exceeds the cost of moving. This is almost entirely a knowledge graph problem.
How ArcXA + KGNN helps:
- End-of-life dependency mapping — ArcXA builds a live SPO graph of hardware assets, the software running on them, and the data flows those systems participate in. When a TLS alert fires on an end-of-support asset, the KGNN immediately surfaces all downstream dependencies — no manual impact analysis.
- Institutional Sizing Tool (IST) integration — ArcXA's IST module can ingest TLS asset inventory and produce migration complexity scores tied to actual infrastructure topology, not just hardware specs.
- Decommission sequencing — the KGNN resolves safe decommission order across interdependent systems: which assets can be retired first without breaking downstream consumers. This is the hardest part of large TLS engagements and currently done manually.
- Continuous lifecycle graph — rather than point-in-time assessments, ArcXA maintains a living graph that TLS teams can query at any stage: "what changed in this client's environment since the last assessment?" via NLP interface.
- Zero Trust posture for aging infrastructure — for federal TLS clients, ArcXA's ICAM layer can flag lifecycle risk through a security lens: assets past end-of-support that are still handling classified or sensitive data, scored against the Zero Trust authority framework.
Value: Unlocks AI value from legacy systems before modernization is complete.
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