The promise of Enterprise AI is simple: give an LLM access to your company's internal tools, and let it answer complex organizational questions. But in reality, enterprise search is broken. Naive vector retrieval fails the moment a query requires connecting the dots across disparate platforms.
This post details Hyperspace, a production-grade platform that solves workspace search by transforming fragmented data silos into a dynamically synced, self-correcting Knowledge Graph. Under the hood, Hyperspace is powered by Cognee, LangGraph, and Groq.
- User signs up for Hyperspace.
- User authorizes GitHub (selecting the repos to connect) and Google Docs (selecting the files to ingest).
- Once sources are selected, Hyperspace starts the Continuous Ingestion pipeline through a backend SQS queue and converts the data into a Knowledge Graph (KG).
- Updates run every 1 hour (optional).
- User interacts through a real-time chat interface backed by LangChain and the Cognee Graph DB.
Traditional enterprise search suffers from what can be called the "Context Fragment Tax." Information within an organization is rarely localized; it is distributed across specialized platforms:
- Code state and developer discussions live in GitHub.
- Product requirements and agile tracking sit in Jira.
- Standard operating procedures and long-form text occupy Google Docs and Slides.
- Cross-functional context flashes by instantly in Slack.
When an AI system relies purely on Standard Vector RAG (Retrieval-Augmented Generation), it runs into three fundamental roadblocks:
[Naive Vector Search] ──> Compares flat text snippets via cosine similarity
❌ Fails to link conceptually isolated platforms
❌ Lacks structural awareness (e.g., PR #5 relates to Ticket J-10)
❌ Blind to multi-hop corporate history
- The Multi-Hop Blindspot: If a user asks, "What was the technical resolution for the API outage mentioned in Slack yesterday?", a vector database looks for chunks containing "API outage text." If the actual fix is documented inside a merged GitHub Pull Request that doesn't explicitly repeat the Slack phrasing, standard RAG cannot connect the two.
- Context Fragmentation & Loss of Lineage: Chunking documents destroys structural hierarchy. A bullet point on slide 14 loses its association with the presentation's overarching project scope on slide 2.
- High Noise-to-Signal in Dynamic Environments: Slack threads and Jira comments are noisy. Vector embeddings capture the surface semantics of this noise rather than the underlying factual entities and their evolving states.
Hyperspace replaces naive vector lookups with a hybrid deterministic and semantic graph intelligence layer. Instead of keeping text chunks isolated, the engine extracts entities and relationships from every connected enterprise tool, constructing a unified corporate brain.
graph TD
%% Source Platforms
subgraph Enterprise Data Layer
GH[GitHub]
JI[Jira]
GD[Google Docs]
GS[Google Slides]
SL[Slack]
SF[Salesforce]
end
%% Ingestion Pipeline
subgraph Continuous Ingestion & Sync
Pipe[Ingestion Pipeline <br/> pymupdf / Docling / APIs] --> |Entity & E2E Relationship Extraction| Inf[BERT / LLM Extractors]
end
%% Core Engine
subgraph Hyperspace Core Orchestration Engine
Orch[Orchestration System <br/> LangGraph]
subgraph Cognee Dynamic Storage
CogKG[(Unified Knowledge Graph)]
CogMem[(Active Working Memory)]
end
SelfCorr[Self-Correction Layer <br/> Graph Validation]
end
%% LLM Layer
Groq[Groq LLM APIs]
%% Connectors
GH & JI & GD & GS & SL & SF --> Pipe
Inf -->|Deterministic Graph Writes| CogKG
%% Retrieval flow
User([User Query]) --> Orch
Orch <-->|Multi-Hop Graph Navigation| CogKG
Orch <-->|Session Context & Personal Memory| CogMem
Orch -->|Validate & Fix Schema| SelfCorr
SelfCorr -->|Prune / Merge Nodes| CogKG
Orch -->|Structured Context Window| Groq
Groq -->|Grounded Generation| Orch
Orch --> Output([Context-Rich Answer])
style CogKG fill:#eff6ff,stroke:#2563eb,stroke-width:2px
style CogMem fill:#eff6ff,stroke:#2563eb,stroke-width:2px
style Orch fill:#fef08a,stroke:#eab308,stroke-width:2px
Cognee is an open-source framework designed to implement GraphRAG and semantic memory structures for AI applications. Within Hyperspace, it serves as the storage, grounding, and retrieval engine.
Unlike standalone vector databases or rigid native graph databases, Cognee creates a hybrid topology. It maps text chunks into standard multi-dimensional vector spaces while simultaneously extracting and anchor-linking those chunks to an explicit, typed semantic entity graph.
Within Hyperspace, Cognee acts as both the long-term enterprise memory store and the short-term transactional interaction log.
To reason across domains, Hyperspace normalizes all platform data into a tightly defined, interconnected Graph Schema inside Cognee. This enables cross-platform path tracing (e.g., tracing a line from a Salesforce Account to a Slack thread to a GitHub commit).
classDiagram
class Company {
string name
string industry
}
class Account {
string accountId
string tier
}
class Repository {
string repoName
string branch
}
class PullRequest {
int prNumber
string status
}
class Issue {
int issueId
string priority
}
class Document {
string docId
string source
}
class SlideDeck {
string fileId
}
class Channel {
string channelName
}
class Message {
string timestamp
string text
}
Company "1" --> "1..*" Account : HAS_ACCOUNT
Company "1" --> "1..*" Repository : HAS_REPO
Repository "1" --> "1..*" PullRequest : HAS_PR
Repository "1" --> "1..*" Issue : HAS_ISSUE
Issue "1" --> "1..*" Message : DISCUSSION_IN
PullRequest "1" --> "1..*" Issue : RESOLVES
Account "1" --> "1..*" Document : AGREEMENT_DOC
Document "1" --> "1..*" SlideDeck : REFERENCES
Message "1" --> "1..*" PullRequest : MENTIONS
- GitHub: (Company)-[:HAS_REPO]->(Repository)-[:HAS_PR]->(PullRequest)
- Jira & Slack: (Issue)-[:DISCUSSION_IN]->(Channel)-[:CONTAINED]->(Message)
- Salesforce & Google Docs: (Account)-[:AGREEMENT_DOC]->(Document)-[:MENTIONS]->(Issue)
Populating and maintaining a real-time Knowledge Graph requires balancing large-scale structural text-parsing with high-frequency event synchronization. Hyperspace handles both in a single pipeline.
- Extraction: Documents (PDFs, Google Docs, Slides) are systematically read using Docling and pymupdf to maintain absolute structural layouts, tables, and document sections.
- Entity & Relation Extraction: Rather than performing blind text chunking, chunks are passed through fine-tuned BERT-based feature extractors and fast LLMs. They isolate concrete enterprise entities and their explicit semantic relationships.
- Graph Initialization: These extracted nodes and relationships are written into Cognee, forming the initial Hyperspace graph.
To ensure the graph doesn't drift from corporate reality, Hyperspace runs a stateless cron system that tracks platform changes without requiring computationally heavy graph rebuilds.
[Target Platform API] ──(Every 30 Mins)──> [Compute Webhook/API Diff]
│
▼
[Isolate Changed Items]
│
▼
[Update/Upsert Specific Nodes]
│
▼
[Commit to Cognee KG]
- For GitHub/Jira, Hyperspace polls API audit logs every 30 minutes to capture new commits, PR merges, or status changes.
- It runs a local diff evaluation against the current known state.
- It executes surgical upsert operations inside Cognee—modifying edge states (e.g., changing a PR status node from OPEN to MERGED) without altering historical context.
When a question hits Hyperspace, retrieval is executed as an agentic, iterative state machine built with LangGraph. Rather than relying on a single vector database search, the system actively queries the graph structure multiple times to build context.
stateDiagram-v2
[*] --> ExtractEntities: User Query Input
ExtractEntities --> ParallelSearch: Identify Initial Keywords
state ParallelSearch {
[*] --> SemanticVectorSearch
[*] --> CogneeGraphLookup
}
ParallelSearch --> EvaluateContext: Merge via Reciprocal Rank Fusion (RRF)
state "Is Context Complete?" as DecisionNode
EvaluateContext --> DecisionNode : Check Missing Edges
DecisionNode --> CollectNeighborNodes: No (Missing Connections Found)
CollectNeighborNodes --> EvaluateContext: Multi-Hop Graph Traversal
DecisionNode --> GenerateResponse: Yes (Context Grounded)
GenerateResponse --> [*]
- Query Deconstruction: LangGraph processes the incoming query and identifies primary target entities.
- Parallel Hybrid Search: Hyperspace triggers a dense vector similarity lookup across document chunks while simultaneously running a structural entity match inside the Cognee Graph.
- Reciprocal Rank Fusion (RRF): The results are combined mathematically, evaluating both text similarity and structural node connectivity weights to bring high-signal matches to the top.
- Active Multi-Hop Traversal: If the state engine evaluates that information spans across tools, it navigates the graph edges (e.g., following Issue #4 to PR #5 to Commit Node) to pull hidden neighboring nodes into the final prompt context.
- Fast Inference Generation: The contextualized knowledge bundle is passed to the Groq LLM API for immediate execution.
Left unchecked, distributed human inputs cause graph structures to degrade over time. Different teammates refer to the exact same entities using varying terminology across platforms:
[Slack Message]: "Let's update the payment flow." ──> Entity: payment flow
[Jira Ticket]: "Refactor StripGateways module." ──> Entity: StripGateways
Without automated graph maintenance, these items remain isolated, breaking multi-hop search capabilities.
Hyperspace runs an ongoing background asynchronous evaluation loop using LangGraph to guarantee data integrity:
graph LR
A[Scan Local Sub-Graphs] --> B[LLM Entity Resolution]
B --> C{Match Found?}
C -->|Syntactic/Semantic Overlap| D[Merge Nodes]
C -->|Dangling Edge| E[Prune / Link to Global Anchor]
D & E --> F[Commit Cleaned State to Cognee]
- Entity Resolution & De-duplication: The background task evaluates newly created neighbor nodes for semantic overlap. If it determines that
payment flowandStripGatewaysrepresent identical code components, it safely merges them into a single global entity node while retaining both source edge attributions. - Orphan and Dangling Edge Pruning: If a Slack message or Jira ticket referencing a temporary task is deleted, Hyperspace clears the corresponding node while archiving the historical connection paths, keeping storage performance optimized.
Beyond serving as a static enterprise knowledge base, Cognee is actively utilized as a dynamic memory layer during customer and internal user chats within Hyperspace.
When a user interacts with the system, they frequently provide implicit and explicit context about their role, preferences, or ongoing tasks (e.g., "I am a frontend developer," or "Only show me Python examples."). If Hyperspace cannot remember this context, the user experience degrades rapidly.
How the Memory Pipeline Works:
- Real-Time Extraction: As the user chats, the LangGraph orchestration layer uses a parallel extraction node to identify Person-Specific Information (PSI).
- Graph Linking in Cognee: These details are written directly into Cognee's Memory module, creating or updating a dedicated user node. Hyperspace establishes edges like
(User)-[:PREFERS]->(Language {name: "Python"})or(User)-[:WORKS_ON]->(Repository {name: "frontend-app"}). - Contextual Retrieval: In future turns, when the user asks, "What are the open bugs in my current project?", LangGraph first queries Cognee's memory. It resolves "my current project" by traversing the
WORKS_ONedge, instantly grounding the subsequent vector and graph searches to the correct repository.
This mechanism ensures Hyperspace doesn't just know the company's data—it deeply understands the specific user querying it, enabling hyper-personalized and context-aware responses.