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Context IntelligencePrimary Engagement

Business Knowledge Graph & Context Intelligence

Decode your business primitives — docs, code, contracts, conversations — into a living context graph with NLP and voice-driven semantic queries.

"Your CRM, ERP, codebase, contracts, and Slack threads hold the answers — but no one can ask them as one question. Vouchstone builds the context graph that makes your entire business queryable, voice-ready, and grounded in operational reality."

The reality

What you are actually dealing with

  • Truth about a single customer, product, or decision scattered across 8+ systems and file types
  • Executives ask plain-English questions; analysts spend days reassembling data from siloed tools
  • PDFs, contracts, and legacy code hold critical business logic no one has mapped
  • Off-the-shelf chatbots hallucinate — they read documents, not your operational reality
  • No semantic layer connects your metrics, entities, and business relationships into one queryable model
  • Existing data warehouses capture rows and columns, not business context — contracts, obligations, decisions, lineage
Vouchstone countermove

How we ship it

  • Context graph that maps business primitives: customers, contracts, products, metrics, decisions, code modules, and their relationships
  • Multi-modal ingestion: structured data, PDFs, contracts, codebases, Slack/Teams threads, call transcripts — all decoded into graph entities
  • NLP and voice-driven semantic queries: ask questions in plain English or speak them — grounded answers with citations and reasoning paths
  • Governed semantic layer: metrics defined once, consumed everywhere — BI tools, AI agents, embedded apps, all from one truth
  • Hybrid retrieval engine: graph reasoning + vector similarity + structured SQL — picks the right strategy per question
  • Every answer is traceable: shows the nodes, edges, source rows, documents, and the reasoning path
  • Drift detection: alerts when underlying systems diverge from the graph — stale entities, changed schemas, broken lineage
Reverse SLA

What we owe you when we miss

Most SI contracts only penalise you for falling behind on payment. Our Reverse SLA flips that - when we miss a named milestone, parity threshold, or budget band, we owe you in credits or refund.

Coverage

Named entities + relationships in scope; missing scope refunds proportionally

Groundedness

Every answer cites source rows / documents; ungrounded answers blocked by construction

Freshness

Named refresh SLA per source system; missed refresh credits

Semantic Consistency

Same question, same number across every consumption channel — verified by parity tests

What you walk away with

Context Graph + Semantic Query Console

A bounded-context knowledge graph with documented schema, change-data-capture from all sources, governed semantic layer with metric definitions, NLP/voice query console, drift dashboard, and lineage maps from source systems to graph entities to consuming dashboards and agents.

Side-by-side

Big SI playbook vs. Vouchstone

Big SI

12-month "AI strategy" deck; then a chatbot bolted onto a PDF library. Semantic layer project runs separately, never connects to the knowledge graph.

Vouchstone

8–16 weeks to working context graph with semantic layer, hybrid retrieval, voice-ready NLP queries, and grounded Q&A — all one system.

Compliance evidence auto-generated

Domains your audit + compliance teams care about

Every action signed to the ledger; every signed action chained into a regulator-ready evidence pack matched to the framework controls below. One-click export, OCSF-formatted for your SIEM.

SOC 2 Type 2GDPRHIPAA (if PHI in scope)SOX 404 (financial metrics)CCPA

Ready to start?

Five-minute intake. Sixty-second response with a named lead, a draft scope, and a price band. No sales call needed before you see what we propose.