Our governed enterprise context layer makes AI knowledgeable, decisive, trustable and dependable on the platforms you already own.
AI agents that lack business meaning, tacit knowledge and enterprise context give inconsistent answers, explain their decisions and go stale after launch. Most enterprise AI solutions never reach production, and some can't complete workflows at all. With a governed context capability in place, enterprise agents get the right knowledge and rules at the right time and can finally be depended on in production.
Context Intelligence is DXC's build-and-run service that converts scattered enterprise knowledge into a governed, machine-readable context that powers a closed decision loop for your agents to succeed. Our context intelligence services assess where decisions fail, build and reconcile the context, connect it to a closed decision loop and run it as a managed service. With our agentic context engineering solutions, your agents get consistent definitions, traceable decisions and knowledge that stays fresh , on your existing platforms.
DXC runs a fixed-scope engagement (lasting a few weeks) that identifies gaps in the business context of currently deployed AI agents. We interview chief information officers (CIOs) and chief digital and artificial intelligence officers (CDAOs) and operations teams; review existing agents and analytics; score context maturity; and baseline cost and risk. The output is a context-debt heatmap, prioritized decision backlog, value case and delivery roadmap.
We create an inventory of data sources, standard operating procedures (SOPs) and metrics, reconciling conflicting definitions, building semantic and relationship models, grounding unstructured knowledge and exposing everything as callable context services.
Connecting governed context to a named decision or workflow can detect changes, explain drivers, recommend actions, simulate trade-offs, route approvals and write back to systems of record. Every decision is traced for regulatory and audit purposes, with full provenance and source attribution.
KnowOps is DXC's Operation and Managed Service for maintaining context quality, freshness, retrieval accuracy, provenance and cost at scale . It includes curation forums with business owners, change control, Service Level Agreement (SLA) and Experience Level Agreement (XLA) reporting, and cost-versus-value dashboards.
The EU AI Act and DORA require organizations to explain how AI reached a given recommendation and reconstruct past decisions for auditors. DXC's approach is to set up governance and then operate it continuously via ownership models, decision accountability, control libraries, policy-as-code, provenance tracking and audit-evidence packs.
Our context intelligence enterprise solution combines deep operational knowledge with platform-neutral context engineering capability, accelerated delivery without vendor lock-in or additional runtime complexity, and provide traceable decisions, actionable insights and client ownership of deliverables and intellectual property.
DXC already manages runbooks, SOPs and incident history for clients across our managed services portfolio. Context builds start with real operational data, not months of discovery.
DXC Context Intelligence is platform-neutral, running on Databricks, Snowflake, Microsoft Azure, Amazon Web Services (AWS), Google Cloud or on-prem. A context-escrow clause guarantees client ownership of all deliverables. The result is no lock-in and no second runtime to manage.
Causal explanation, trade-off simulation, approval routing and write-back to systems of record ensures that every decision is traceable. Client ontologies remain client IP.
Our context engineering platform is applicable toward use cases in the following sectors:
Claims triage, credit escalation, payments repair and regulatory evidence.
Warehouse receiving decisions, autonomous troubleshooting and incident resolution.
Invoice extraction, reconciliation and anomaly detection.
Carrier-agnostic semantic layer, context lineage and unified platform operations.
DXC’s Context Intelligence offering follows a staged delivery model where each stage provides reusable, governed context assets.
Assess (2–4 weeks)
Pick one bounded context problem. Baseline reliability, cost, cycle time and risk. Identify context debt and produce a roadmap.
Build (14–20 weeks)
Inventory sources, reconcile definitions, engineer semantic models and expose governed context as callable services. Connect governed context to the selected agent or decision loop. Capture decision traces and outcomes.
Run and Govern (ongoing)
Monitor quality, operate curation forums and expand to adjacent domains. This is the KnowOps managed service.
DXC Context Intelligence is a build-and-run services offering that turns scattered enterprise knowledge into a governed, machine-readable context layer for AI agents and decision workflows. The offering converts data, business rules, SOPs, metrics and institutional expertise into callable context services that ground AI in how the business actually operates.
Context Engineering grounds AI agents in governed enterprise knowledge rather than raw retrieval, which is the primary cause of hallucinations and inconsistent output. Without a governed context layer, AI fills knowledge gaps with plausible but wrong information, so agents answer inconsistently and cannot explain how they reached a decision.
Context Engineering addresses this by building curated definitions, semantic models and validated relationships into a single source of truth. Agents draw from that layer instead of interpreting unstructured data on the fly. This raises answer accuracy, cuts hallucination rates and shortens time-to-answer, while giving every output traceable provenance back to its source.
DXC has operational access to client IT estates through existing managed services relationships. That access includes runbooks, SOPs and incident histories, which seeds the context layer faster than any competitor starting from discovery.
DXC is platform-neutral, with a context-escrow clause guaranteeing client ownership. We stay after the build, with our managed service component providing ongoing context curation, freshness monitoring and cost-versus-value reporting. And the output is a closed decision loop with causal explanation and write-back to systems of record.
Context Intelligence targets the gap between AI capability and business readiness. Nintey-five percent of enterprise AI pilots fail to reach production, and the reason is rarely the model itself. Four core challenges account for this, which DXC works through systematically.
Two forces are converging. On the technology side, agentic AI has moved from experimentation to production pressure. Boards and chief financial officers (CFOs) want measurable returns from AI spend; most organizations have multiple agent pilots underway but few AI projects in production. On the regulatory side, the EU AI Act and DORA now require organizations to explain AI-driven decisions and reconstruct them for auditors.
Context Intelligence addresses both. It gives agents the governed knowledge they need to be production-ready while providing the decision traces that regulators demand. The organizations building their context layer now will have a compounding advantage as agentic AI adoption accelerates.
Context Intelligence is platform-neutral by design and uses the infrastructure each client already owns. There is no proprietary runtime to install or license.
The architecture uses open standards including Model Context Protocol (MCP), GraphRAG, Semantics, Ontology and more to prevent vendor lock-in, and a context-escrow clause guarantees that all deliverables remain client IP. Integration works at two levels: the context layer connects to existing enterprise data sources, applications and systems of record, while the decision loop writes back outcomes and captures traces within those same systems.
Operational access: A provider that already manages your IT systems can build context faster because it has direct access to runbooks, SOPs and incident data.
Platform neutrality: Avoid providers that require a proprietary runtime or lock deliverables to a single cloud.
Build-and-run capability: Context degrades without ongoing management, so the provider should offer managed services after initial delivery.
Client IP ownership: Context assets should be contractually yours, with escrow provisions.
Measurable outcomes from day one: The provider should baseline current performance and commit to specific improvements.
