RISK-01
Hallucination & Inaccuracy
Without access to your actual data, agents rely on training patterns. They invent customer details, misquote pricing, suggest products already purchased.
Resolved by
Direct data references + provenance metadata
SHEET 01Technical Deep-DiveCONTEXT
Context Engine Architecture
How assistents.ai builds unified business context from 300+ systems, so every agent understands your data, your rules, and your workflows.
4Integrated layers
6Query pipeline steps
300+Enterprise connectors
<200msp99 context retrieval
SHEET 02Architectural RiskRISK-01..03
Why context matters
Without unified context, AI agents operate in isolation. Most AI platforms treat each query as independent: no knowledge of your customer history, your contract terms, your compliance rules, or your business logic.
RISK-02
Generic Responses
Agents return textbook answers instead of solutions tailored to your business. A support agent can't contextualize why a ticket happened or which contract applies.
Resolved by
Semantic graph relationships + cross-system joins
RISK-03
Permission Leaks
Without explicit permission boundaries, agents either over-share sensitive data or under-serve users by withholding data they should access.
Resolved by
Query-time ACL enforcement + audit logging
The context engine grounds every agent decision in actual business data: filtered by permissions, linked across systems, and auditable back to source.
SHEET 03Four-Layer ArchitectureEXPLODED
Architecture overview
Four integrated layers. Each adds intelligence; together they deliver real-time, permission-aware, semantically unified context.
- L04
Query Resolution
Intent ParserDecomposes query into intent + required entitiesContext AssemblyGathers data from multiple sources into unified result setRanking EngineScores relevance by recency and authorityProvenance MetadataAttaches source, timestamp, and confidence to every result - L03
Permission Layer
GovernedRole MappingMaps user identity to role-based permissionsSource ACL SyncSyncs ACLs from source systems in real-timeAudit LoggerLogs every data access for compliance audit trailEscalation RulesRoutes sensitive requests to human approval - L02
Semantic Fusion
Entity ResolverIdentifies and dedupes entities across data sourcesGraph BuilderConstructs semantic knowledge graphRelationship MapperMaps entity relationships across systemsConflict HandlerResolves data conflicts from multiple source systems - L01
Data Ingestion
API AdaptersConnect to 300+ APIs with format translationWebhook ReceiversReceive real-time push events from sourcesChange Data CaptureCapture incremental changes from databasesFormat NormalizersTranslate data from source formats to unified schema
SHEET 04Query FlowPIPE-01..06
How it works, step by step
From task assignment to agent execution: how the context engine delivers unified business context in real time.
01
Agent receives a taske.g., "Summarize Q4 pipeline for enterprise accounts"
<5ms02
Context engine identifies required data sourcesCRM deals, account records, activity logs, contract details.
~20ms03
Permission checkFilters to data the requesting user can access. Respects role-based controls across all source systems.
~15ms04
Semantic fusionConnects related entities across systems. Links deals to accounts, accounts to companies, products to SKUs.
~80ms05
Context delivered to agentIncludes provenance metadata: source system, data freshness timestamp, confidence score.
~60ms06
Agent acts with full business contextCites sources. All decisions auditable back to original data. No hallucination drift.
TOTAL <200ms6 steps TOTAL <200ms end to end provenance attached at delivery
SHEET 05Competitive AnalysisVS
Context engine vs RAG vs search
Real-time, unified, permission-aware context defeats point-in-time retrieval.
CriterionContext EngineTraditional RAGSearch-First
Data FreshnessReal-time (<5s latency)Batch-dependent (8-24h old)Search index refresh (1-4h)
Cross-System JoinsNative graph relationshipsVector embedding onlyKeyword + facet only
Permission EnforcementEnforced at query timePost-hoc filteringPer-document ACL only
Relationship UnderstandingSemantic graph (customer → deals → products)Vector similarity (probabilistic)Keyword overlap (shallow)
Latency (p99)<200ms800-1200ms300-600ms
Hallucination RiskVery LowHighMedium
6 criteria context engine vs traditional RAG vs search-first
<200msContext RetrievalSub-200ms p99 latency. Real-time context delivery.
300+System ConnectorsOut-of-the-box integrations to enterprise systems.
100%Permission ComplianceZero permission leaks. SOC 2 certified.
91%Hallucination ReductionDirect data references eliminate drift.
SHEET 06Technical DetailsSPEC
Technical specifications
Supported systems, data formats, and deployment architectures.
Supported Integration CategoriesSystems
CRMSalesforce, HubSpot, Pipedrive, Dynamics 365
ERPSAP, Oracle, NetSuite, Infor
HRISWorkday, BambooHR, ADP, Guidepoint
TicketingJira, Zendesk, ServiceNow, Help Scout
Data WarehouseSnowflake, BigQuery, Redshift, Databricks
File StorageGoogle Drive, OneDrive, S3, Box
CollaborationSlack, Microsoft Teams, Asana, Notion
AccountingQuickBooks, Xero, Netsuite, Stripe
Data Formats & ProtocolsDetails
StructuredSQL, JSON, Parquet, Avro
Semi-structuredXML, YAML, protobuf
UnstructuredPDF, Office docs, HTML, email
Real-time streamsKafka, Kinesis, Pub/Sub
Deployment OptionsDetails
Cloud (Multi-tenant SaaS)AWS-hosted, SOC 2 certified
Cloud (Dedicated VPC)Single-tenant, customer-managed keys
On-premisesAir-gapped, no external connectivity
HybridOn-prem data + cloud connectors
SHEET 07ResilienceFAIL-01..04
Failure handling and edge cases
How the context engine responds when things go wrong. Every failure mode has an explicit, documented handling strategy.
FAIL-01handled
Stale Data
When a source system becomes temporarily unreachable, the engine returns last known good data with a staleness flag and timestamp.
ResponseCached + flag
FAIL-02handled
Permission Conflicts
When source permissions change between ingestion and query time, the engine re-validates at query time. If permissions were revoked, data is excluded.
ResponseRe-validated + excluded
FAIL-03degraded
Connectivity Loss
When a source system goes offline during a live query, the engine returns partial results with degradation flags and source status indicators.
ResponsePartial + degraded flag
FAIL-04handled
Oversized Results
When context exceeds token limits for the requesting agent, the engine ranks by relevance and truncates with a continuation token.
ResponseRanked + truncated
SHEET 08EvaluateREADY
See the context engine in action
Watch how assistents.ai agents access unified context from your Salesforce, Slack, and data warehouse: in real time, with zero hallucinations, and full permission compliance.
- Subject
- Context engine · 4-layer architecture
- Integration
- 300+ connectors · SOC 2 certified
- Performance
- Unified context · <200ms p99
- Sheet
- 08 of 09 · Sign-off