You have all the data to run your business. It’s just fragmented, siloed and out of sight.

Stage 1Multi-modal extraction

Connect directly to enterprise sources with no disruption to live operations.

• Enterprise databases and APIs

  Native read-only connectors for PostgreSQL, Oracle, SQL Server, SAP, Salesforce and custom REST or GraphQL endpoints.

• Files and semi-structured data

  Continuous ingestion of CSV, XLSX, JSON and XML drops from internal file servers or cloud buckets (Amazon S3, Azure Blob Storage, Google Cloud Storage).

• Document parsing and semantic extraction

  Text is extracted from contracts, building codes, environmental permits and inspection certificates, then passed through specialised entity-extraction pipelines.

Stage 2Entity resolution and deduplication

Remove duplicate records and conflicting naming conventions across operational tools.

• Fuzzy and contextual disambiguation

  Records that describe the same real-world entity under different names are resolved into one, for example “ABC Concrete Limited” in accounts and “ABC Pouring” in the site logs.

• Deterministic matching rules

  Tax identifiers, employee IDs, purchase order numbers and asset barcodes are cross-referenced, so a shared hard key settles identity outright.

• Temporal state tagging

  Every ingested data point receives an immutable timestamp, so the system preserves the operational state at the exact moment of capture.

# Example: two names, one supplier
Accounts system  "ABC Concrete Limited"  tax ID TX-4471
Site log         "ABC Pouring"           order PO-40213 → tax ID TX-4471
Result           One supplier: ABC Concrete  14 Mar 2026, 08:12

Stage 3Semantic ontology mapping

Turn flat records into meaningful, connected knowledge.

• Schema decoupling

  Data is mapped to your domain ontology rather than to source database schemas. When a source system changes its tables, only the mapping changes; your operational logic stays the same.

• Active relationship binding

  Tasks link to people, assets to contracts, permits to physical locations, and material batches to compliance standards.

• Constraint ingestion

  Statutory rules, working-hour curfews and supplier SLAs are bound to the entities they govern as active constraints that planning can test against.

Stage 4High-performance graph commit

Feed downstream simulation and agent systems in real time.

• Continuous event streaming

  High-throughput writes support live IoT streams, equipment telematics and high-frequency schedule updates.

• Tamper-evident ledger

  Every write, update and soft deletion is recorded in an immutable temporal ledger for regulatory audit and compliance verification.

• Real-time notification bus

  New data immediately triggers anomaly checks, scenario recalculation or webhook alerts the moment it is committed.

Supported formats and connectors

Source systems, standards and the mechanism used to bring each one in.

Security and sovereignty from the first byte

Ingesting sensitive corporate data needs safeguards from the moment it leaves the source.

• Encryption in transit

  All data extracted across internal and external networks is encrypted with TLS 1.2 or 1.3 and forward secrecy.

• No model training on your data

  Ingested data, contracts and financial figures are never sent to public LLMs or used in third-party training. External agents see data only through MCP connections you approve.

• Air-gapped and private-network collectors

  Deploy self-contained ingestion collectors inside your private VPC or a physically isolated on-premises network, so data is processed and resolved behind your firewall.

• Credential isolation

  Database credentials and API tokens are held in your enterprise secrets vault, and none are written to application logs.