Internal Knowledge Copilots
Narrow, team-specific copilots grounded in tribal knowledge — not one generalist bot that knows a little about everything. Built on a living context graph of your docs, code, contracts, and conversations.
"Your CRM, ERP, codebase, contracts, and Slack threads hold the answers — but no one can ask them as one question, and a single company-wide chatbot just hallucinates across all of it. Vouchstone builds one context graph, then grounds a narrow, team-specific copilot on top of it for each team that needs one."
What you are actually dealing with
- Truth about a single customer, product, or decision scattered across 8+ systems and file types
- A generalist company chatbot answers everything shallowly and nothing precisely — teams stop trusting it
- 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
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
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.
Big SI playbook vs. Vouchstone
12-month "AI strategy" deck; then a chatbot bolted onto a PDF library. Semantic layer project runs separately, never connects to the knowledge graph.
8–16 weeks to working context graph with semantic layer, hybrid retrieval, voice-ready NLP queries, and grounded Q&A — all one system.
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.
Ready to start?
Four questions. An instant computed report — recommended tier, rollout timeline, and price band. No sales call needed before you see what we propose.
Other flagship engagements
Legacy, ERP & Data-Warehouse Migration
Specialist agents grounded in a knowledge graph of source and target schemas do mapping, CDC pipeline building, and UDF conversion deterministically — COBOL, ASP.NET, Oracle, mainframes, and warehouses migrated with row-level parity proofs.
RPA Replacement in Finance Ops
AP invoice automation and cost anomaly detection. Every approval checked against exact graph facts — vendor, contract clause, policy — and written as a signed workflow trace, not a brittle screen-scraping bot.
Compliance & Audit Evidence Automation
SOC 2 evidence collection, PII classification scanning. Controls mapped to policies; every audit run is signed and traceable back to source.