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Enterprise Context Engine by KPI Partners

Agents are only as accurate as their context.

Models know the world. Your agents need to know your business: its definitions, rules, relationships and history. We build the governed context and ontology layer that makes agents accurate and trusted, on AWS, Microsoft, Google Cloud, Databricks or Snowflake.

Book a Context Readiness Assessment Start with a Context Sprint

The knowledge gap

General knowledge gets an agent started. Your knowledge makes it right.

Agents built only on what models already know guess at your definitions and join your data the wrong way. Accuracy and trust come from access to what only your organization knows.

What models already know

Frontier models bring general, multipurpose knowledge
Specialized models bring common domain knowledge

Agents need both to be accurate and trusted

What only your organization knows

Tacit knowledge held by experts: heuristics, exceptions, judgment
Explicit knowledge spread across schemas, BI, documents and policies

The context layer

Three parts that give agents the full picture

Semantics tells agents what things mean. Operational state tells them what is true right now. Provenance tells everyone where an answer came from and what an agent did with it.

Semantics

  • Ontology and knowledge graph
  • Governed metrics
  • Business rules and policies
  • Ownership and glossary

Operational state

  • Resolved business entities
  • Right-time data products
  • Documents and organizational knowledge
  • Agent memory

Provenance

  • Lineage of data
  • Processes and decisions
  • Agent actions
  • Outcomes, fed back as learning
Where they meet, agents getExecutionGovernanceBehavior they can explain

The context journey

Fourteen capabilities, delivered in the order that works

We classify before we define, and validate before anything reaches an agent. Each phase must hold before the next one adds value.

Phase 1

Assess

Which agent decisions need which knowledge?

  1. 01Context Strategy & Use-Case Scoping
  2. 02Context Maturity Assessment

Phase 2

Model

Is the business classified and defined?

  1. 03Knowledge Capture
  2. 04Taxonomy & Business Glossary
  3. 05Ontology & Knowledge Graph
  4. 06Semantic Layer & Metrics

Phase 3

Build

Can agents reach the context?

  1. 07Agent-Ready Data Products
  2. 08Retrieval & Memory
  3. 09Context Serving (MCP)

Phase 4 · validation gate

Trust

Is the context right, allowed and traceable?

  1. 10Rules, Policies & Access
  2. 11Provenance & Lineage
  3. 12Context Evals & Answer Verification

Phase 5

Activate & Run

Does context reach agents and stay current?

  1. 13Agent & Channel Activation
  2. 14ContextOps

One semantics, every channel

Define it once. Every dashboard, app and agent gets the same answer.

The same governed definitions serve traditional BI, analytics embedded in business applications, and agents, so “revenue” means one thing wherever it is asked.

Traditional analytics

Reliable, consistent insight through dashboards and self-service tools.

Embedded analytics

Actionable insight built into the business applications people already use.

Agentic AI and agentic analytics

On-demand answers and automation through conversational and intelligent agents.

How the pieces fit

Agents ask the knowledge graph first, then fetch governed data

Agents connect as MCP clients. They query the semantics to find the right steps and sources, then retrieve data from certified data products on your lakehouse.

1The agent queries the knowledge graph to identify the right steps, definitions and sources.
2It retrieves the data from governed MCP resources, with access, masking and lineage applied.

How to engage

Start with one domain, scale to the enterprise

Four engagements follow the journey. Each ends with exactly what the next one needs.

Assess

Context Readiness Assessment

4 weeks · fixed fee

A context maturity score, one quick win and a 90-day roadmap on the platform you already run.

Prove

Context Sprint

4 to 6 weeks · fixed price

One context domain shipped for one agent: glossary, ontology slice, semantic model, MCP serving and measured accuracy.

Scale

Enterprise Context Layer

10 to 14 weeks · milestones

Ontology and knowledge graph across domains, certified data products, policies, provenance and evals.

Run

Managed Context

Ongoing · subscription

Ontology versioning, drift and accuracy monitoring, and new terms and rules onboarded as the business changes.

Quick offers

Fixed-scope offers that solve one context problem in weeks

Everything they produce is reused when you move into a full engagement.

Assess1 to 2 weeks

Agent Knowledge Mapping Workshop

Agent decisions mapped to the definitions, rules and documents they need, with tacit gaps flagged.

Model3 weeks

Business Glossary & Taxonomy Sprint

A governed glossary for one domain with owners, synonyms and column mappings.

Model4 weeks

Ontology Design Sprint

A core ontology for one domain, built from your industry’s standard model, with entity resolution rules.

Model4 to 6 weeks

Semantic Layer Modernization

BI semantic models moved to platform-native semantic views or metric views, with parity proven.

Build4 weeks

Agent-Ready Data Product Starter

Two to three certified data products with owners, SLAs and semantic descriptions.

Build4 to 6 weeks

Knowledge Graph & GraphRAG Pilot

A graph over one domain with graph-based retrieval, compared against vector-only answers.

Build3 to 4 weeks

MCP Context Server Starter

Your ontology, metrics and data products exposed to agents through governed MCP servers.

Trust3 weeks

Context Access & Policy Baseline

Row, column and PII policies applied to context and retrieval, tested per persona.

Trust3 to 4 weeks

Lineage & Decision Provenance

Lineage from source to answer, plus decision logs for agent actions.

Trust4 weeks

Agent Accuracy Uplift

Golden questions, verified answers and before-and-after accuracy for one agent.

Run · existing semantic layers2 weeks

Context Health Check

An audit of your current semantic layer or ontology for drift, gaps and accuracy, with a plan to fix them.

Your platform, natively

Every platform now auto-generates context. We make it governed.

Platform auto-context is a strong start, but auto-context is not governed context. We build on each platform’s own semantics, catalog and graph services, then verify, secure and evaluate the result.

Databricks

Unity Catalog metric views and semantics feeding Genie Ontology

Managed MCP, Genie One, MLflow evaluation

Snowflake

Semantic Views and Horizon Context, served to Cortex Agents

Cortex Search, Snowflake MCP Server, CoWork

Microsoft

Fabric IQ ontology on OneLake, governed by Purview

Power BI semantic models, Foundry IQ

AWS

SageMaker Catalog semantics with Amazon Neptune

Bedrock Knowledge Bases GraphRAG, AgentCore

Google Cloud

Knowledge Catalog with the Looker semantic layer

BigQuery, Spanner Graph, Agent Search

Industries

We start from your industry’s own models, not a blank page

Published industry standards give the ontology a head start. We extend them with your terms, rules and expert knowledge.

Insurance

ACORD data standards

Policy, coverage, claim and party context for claims triage and underwriting agents.

Banking

FIBO · BCBS 239

Consistent risk data aggregation and customer graphs for AML investigation.

Financial Services

FIBO · ISO 20022

Finance metrics and entity models that reconcile for FP&A and close agents.

Healthcare

HL7 FHIR · OMOP · SNOMED CT

Encounter, provider and payer context for revenue cycle and prior authorization.

Pharma & Life Sciences

ISO IDMP · CDISC · MedDRA

Product, study and safety knowledge for commercial and regulatory agents.

Retail & CPG

GS1

One product and customer truth for merchandising and replenishment agents.

Manufacturing

ISA-95

Asset and plant hierarchies for maintenance, quality and supply agents.

Energy & Utilities

IEC CIM

Network and asset models for outage and inspection agents, read-only to the grid.

Airlines & Aviation

IATA AIDM · ATA iSpec 2200

Flight, crew and aircraft context for operations and MRO agents.

Media & Entertainment

EIDR · DDEX

Title, rights and territory graphs for availability and yield agents.

Case studies

Context we have built on Microsoft, Databricks and Snowflake

In each engagement the accuracy came from the context layer: shared definitions, an ontology or semantic model, and the company’s own terms, built once and served to every AI experience.

Microsoft Fabric · Finance

Balance to invoice in one conversation

A global professional services firm needed Finance to explain any number without navigating several systems.

Context we built

  • A Finance ontology over the business
  • An AI-enriched semantic layer on P&L, balance, GL, AP and AR semantic models
  • Fabric Data Agents for P&L, GL, AP, AR and client revenue, coordinated by an orchestration agent
Finance traces a consolidated balance to its source invoice, and every answer cites its source evidence, in Teams, Power BI and Excel.
Read case study

Databricks · Marketing

One governed source for marketing performance, ready for AI

A global professional services firm needed one trusted view of the pipeline and revenue marketing generates, across Eloqua, Cvent, On24, CRM and Reltio MDM.

Context we built

  • Agreed definitions for MQL, SQL, and sourced versus influenced pipeline
  • Canonical KPI logic in Unity Catalog metric views, with business definitions in Unity Catalog Pages
  • Separate, governed paths for Databricks Genie and Power BI Copilot, with KPI parity and AI answers tested before release
43 KPIs defined once, behind three persona dashboards and natural-language answers traceable to source.
Read case study

Snowflake · Sales and Finance

Accurate, plain-English answers in the company’s own language

A large beverage manufacturer’s sales and finance users needed fast answers, but generic models did not understand the company’s terms, KPIs and metrics.

Context we built

  • A data dictionary of company terms and KPIs
  • A tailored few-shot SQL library, code templates and intent-based prompts
  • A GenAI-friendly schema with joins kept in configuration, plus a self-correcting answer check
99%+ accurate responses, and 100% on the client’s 150-question validation set, in Teams, a web app and a chatbot.
Read case study

Why KPI Partners

Context is a data problem first. Data is where we started.

A data, analytics and AI consultancy since 2006, with 600+ consultants and 300+ enterprise customers. We turn platform auto-context into governed, verified context, and leave behind a unified model, a context library and proof of parity.

50% to 85%answer accuracy after context work on Databricks
24% to 86%answer accuracy after context work on Snowflake

Building agents too? See Agentic AI Services and DomainIQ Agents by KPI Partners.

Find out how ready your context is in four weeks.

The Context Readiness Assessment maps your agent use cases to the knowledge they need, scores your semantics, operational state and provenance, and delivers a quick win and a 90-day roadmap.

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