EnterpriseIQ comprehends your data estate — structured and unstructured — and gives your teams the building blocks to create their own intelligent tools. Compliance-ready. Governed by default. Operational in 12 weeks.
You've invested in the models. You've evaluated the platforms. You may have built internal prototypes. And yet — AI adoption across your organization remains stubbornly low.
The reason: AI models don't know that "revenue" means something different in your services division than in your product division, that your fiscal year starts in April, or that when your supply chain team says "lead time," they mean something specific to your vendor contracts. Without that understanding, every AI initiative starts from zero — every department builds its own approach, every pilot works in isolation and fails at scale.
The most valuable technology companies in the world have converged on a single architectural pattern: AI doesn't fail because the models are weak. It fails because the models don't understand the business.
The answer isn't a better model. It's a better understanding layer — a structured comprehension of your data, your entities, your relationships, and your business logic that sits between your AI capabilities and your enterprise data.
This layer has historically required eight-figure budgets and multi-year implementations — the kind of project only a Fortune 500 green-lights. EnterpriseIQ provides it as AI infrastructure.
| Layer | What it does | Component |
|---|---|---|
| Intelligence | Builds understanding of structured data (databases) and unstructured data (documents, including knowledge graphs) | DataIQ + DocIQ |
| Routing | Classifies intent, routes to the right engine, synthesizes unified answers from both | ContextIQ |
| Orchestration | Governs how AI tools, agents, and MCP servers work together — with RBAC, audit, and federation | ContextForge |
| Experience | Multi-model AI workspace with custom agents, visual workflows, voice, and canvas | AssistIQ |
| Management | Admin interface, SSO, OTEL-based observability, and model evaluation | EIQ Console |
EnterpriseIQ is a platform with six components, each with a clear job — two intelligence engines, one intelligent gateway, one orchestration layer, and two purpose-built interfaces. No phantom products. No vaporware. Every component is production-ready and deployed.
Your enterprise runs on databases. DataIQ makes them conversational. Ask in plain English — DataIQ uses RAG to find the right schema context, generates SQL, validates it, executes it, and returns results with auto-detected visualizations. Your data stays where it is.
Database support: PostgreSQL · MySQL · SQL Server · Oracle · SQLite · DuckDB · Snowflake · BigQuery · ClickHouse · Presto · Hive
Streaming execution · Batch processing · Reusable parameterized SQL · Auto-learning · 10+ chart types · Export: CSV, JSON, Excel, PDF, Parquet
Your enterprise also runs on documents — contracts, reports, emails, policies, presentations. DocIQ reads them, understands them, and builds a knowledge graph of everything they contain.
Document support: 32+ file types — PDF (including OCR) · DOCX · XLSX · PPTX · CSV · Email · Markdown · HTML · Code · EPUB · Images · Audio
Four search modes: Semantic · Full-text · Hybrid · Knowledge graph
101 API endpoints · 10 MCP tools · RAG with streaming and citations · Research agent · Collection-based RBAC · Batch processing · Named entity extraction
Knowledge Graphs
DocIQ automatically extracts entities, identifies relationships, detects communities, deduplicates references, and makes the entire graph queryable. Upload 10,000 contracts — DocIQ tells you which vendors appear across the most agreements, which terms are non-standard, and where patterns suggest risk or opportunity.
One question. The right engine. One answer. ContextIQ classifies intent — data question, document question, or both — and routes to DataIQ, DocIQ, or both in parallel. Results are synthesized into a single, coherent answer.
Intent classification · Parallel routing · Unified synthesis · Auto-ontology
One control plane for every AI capability in your enterprise. 135+ curated MCP servers. Per-user RBAC at server, tool, and resource level. Virtual server composition. SSO integration. REST-to-MCP translation. OpenTelemetry distributed tracing. Full audit logging.
SSO · OpenTelemetry tracing · OAuth gateway · Runtime config
Multiple AI models across 8 providers. Custom agents with per-agent RBAC. Visual workflow editor with 8 node types. Voice chat. Canvas mode. Switch models per-message. 7 languages.
DAG workflows · @-mention agents · 7 languages · Built-in tools
MCP & A2A server management · 135+ server catalog · Virtual server composition · Query builder · Training data management · Schema browser · Document management · Collection RBAC · Knowledge graph visualization · User management · System configuration · API Explorer
Full control — not a settings page
No single application could produce this answer. It required comprehension across CRM, support, competitive docs, contracts, and product roadmap — unified by the understanding layer.
Sovera identifies the API integration issue costing $420K in pipeline, the competitive gap from a feature delay, and the shortening contract lengths that signal buyer hedging.
The close rate issue is composite. Understanding shows you why — and what to fix first.
Sales asks about Q3 forecast and sees pipeline-weighted projections. Operations asks about capacity and sees supply chain data (not deal-level). Finance asks about margin impact and sees all financial data with vendor contract terms.
Every query is authenticated, authorized, and logged. Full audit trail. ContextForge enforces it all.
Three intelligent answers. Three permission levels. One governed infrastructure.
Sovera's monitoring detected a vendor billing discrepancy — invoiced rates 12% higher than contracted, traced to an unsigned amendment. The CFO got the anomaly via email at 7:02 AM. Procurement got the root cause via Slack. The audit log documented everything.
The understanding layer noticed, the governance layer routed, and the right people got the right information at the right time — with full documentation.
Every capability is governed from day one — designed with the assumption that your security, compliance, and legal teams have veto power over any AI deployment.
SSO/SAML integration. Multi-factor authentication. Custom RBAC with field-level data access control.
Full audit trail for every AI interaction. Compliance-ready for SOC 2 Type II and HIPAA. Custom compliance framework support.
Encryption in transit and at rest. Customer-managed encryption keys. Configurable data residency. Read-only app connections. Sovera never trains models on your data.
ContextForge policy engine for token budgets, model routing rules, content filtering, and response validation. Every AI request is governed, not just logged.
Enterprise AI infrastructure is a crowded market with big names and big promises. Here's where Sovera fits — and the differentiator nobody else offers: none of these alternatives make your people's work more meaningful. They're all products your teams use. EnterpriseIQ is a platform your teams build on.
| Build It | Hyperscalers | Enterprise Analytics | Dept. BI | EnterpriseIQ | |
|---|---|---|---|---|---|
| Time to value | 18-24 mo | 6-12 mo | 6-18 mo | 1-3 mo | 8-12 weeks |
| Understanding layer | You build it | You build it | Partial | None | ContextIQ |
| Structured + unstructured | Custom | Separate | Partial | No | DataIQ + DocIQ |
| Knowledge graphs | Custom | No | $$$ add-on | No | Automatic (DocIQ) |
| Teams build on it | Limited | No | No | No | Yes — agents, workflows, tools |
| Governance | Custom | Partial | At scale | No | ContextForge |
| Multi-LLM | Custom | Vendor-locked | No | No | 30+ models, 8 providers |
A dedicated team — named account manager and platform architect — who know your enterprise as deeply as the platform. Their job isn't just deployment. It's capability transfer.
Map your data estate. Understand departmental structure, decision workflows, and governance requirements. Design the deployment architecture. Identify the first tools your teams will build.
ContextForge gateway setup. Data engine configuration. Custom connectors. Department-specific agent configurations — built with your teams, not just for them. Knowledge graph ingestion.
Department-by-department rollout. Training that ends with each team building something new. Go-live support. Executive readout on capabilities deployed and transferred.
Named account manager · Platform architect · Monthly capability sessions · Quarterly business reviews · Custom development support · Unlimited training · 24/7 priority support
Not enterprise scale? See the product for individuals and teams, or compare all plans.
Talk to our team about a platform that comprehends your data estate, gives your teams the building blocks to create their own intelligent tools, and makes your people's expertise durable. Governed, scalable, operational in 12 weeks.
Custom scoping · Proof of concept available · Compliance-ready architecture