Agentic Knowledge Platform

Unify enterprise knowledge into an AI-ready foundation, then design, govern, and scale intelligent agents as enterprise products.

Agentic_Knowledge_Platform_Offering.docx · Original project source · 4 pages

Client pain points

Fragmented Enterprise Knowledge. Knowledge is scattered across documents, data platforms, collaboration tools, and internal systems, making trusted information difficult to find and preventing AI systems from reaching a unified foundation.
Unreliable AI Copilots. Organizations struggle to ground assistants in accurate enterprise knowledge, leading to hallucinations, inconsistent outputs, and limited adoption.
No Agent Operating Model. Enterprises lack the product framework required to design, govern, and scale agent systems, leaving prototypes stalled with unclear ownership and lifecycle management.
Unmanaged Multi-Agent Architectures. No clear operating model governs multi-agent systems interacting with enterprise data platforms.
Institutional Knowledge Dependency. Critical knowledge remains embedded in individual teams and employees, creating single points of failure.

Current solution

Ad-hoc experimentation without product discipline or lifecycle management.
Keyword-based search rather than semantic understanding.
Disconnected SharePoint sites, wikis, document drives, and BI tools.
Copilots without access to a unified knowledge foundation.
Traditional product frameworks not designed for autonomous systems.
Limited search that cannot connect related information across systems.

“Blend launches the Agentic Knowledge Platform, enabling enterprises to unify knowledge into an AI-ready foundation and design, govern, and scale intelligent agents as enterprise products.”

Two connected layers

01

Knowledge Foundation

A governed, semantic knowledge layer that connects structured and unstructured enterprise sources.

02

Agent Product Management

An enterprise operating model for agent architecture, orchestration, lifecycle, safety, and outcomes.

Knowledge Foundation

Unified Enterprise Knowledge Layer — Consolidate enterprise systems into a centralized knowledge layer.
Semantic Knowledge Modeling — Apply metadata, taxonomies, and semantic relationships for context-aware retrieval.
AI-Ready Retrieval Infrastructure — Ground employee and copilot answers in accurate enterprise knowledge.
Enterprise Data Platform Integration — Connect structured data platforms with documents, reports, and collaboration tools.
Governed Knowledge Access — Apply role-based controls, audit mechanisms, and enterprise data policies.

Agent Product Management

Agent Operating Model — Define agent capabilities, tools, outcomes, and human workflow boundaries.
Multi-Agent Orchestration — Establish reusable coordination patterns across the knowledge layer.
Agent Lifecycle Management — Support deployment, monitoring, evaluation, retraining, and improvement.
Agent Governance & Safety — Define decision boundaries, compliance controls, and safety frameworks.
Performance Measurement — Track agent performance, retrieval accuracy, and business outcomes.

Where the offering lands

Presidio

Extend its AI governance framework and enterprise assistant by organizing knowledge into a structured retrieval layer, then scaling agents with lifecycle governance and coordination.

CDW

Combine Golden Contact ID data with sales insights, product documentation, and marketing analytics, then deploy governed agents across customer-facing functions.

Chewy

Integrate Snowflake data with internal documents and marketing insights to support AI-driven analytics, decision support, and enterprise search.

ALM

Define how AI agents interact with its governed Snowflake environment, including decision boundaries and lifecycle management.

What needs to be built

Knowledge Foundation Layer

Enterprise knowledge ingestion framework
Semantic knowledge models
AI retrieval layer
Governance and security framework

Agent Product Layer

Agent operating model framework
Multi-agent orchestration patterns
Agent lifecycle framework
Performance measurement framework

Ideal project profile

$1.5M–$3.5MProject revenue

6–12 monthsPhased duration

80%+Knowledge sources consolidated

2+ functionsMulti-agent patterns deployed

Team: Product Manager, AI Architect, AI Engineers, Data Analyst, Data Engineering Manager, QA Analyst, and Project Manager.

Technologies

Data: Snowflake, Databricks, Microsoft Fabric

LLM: OpenAI, LangGraph

Knowledge: Vector databases, semantic search, graphs

Cloud: AWS, Azure, GCP

Integration: SharePoint, OneDrive, Salesforce, APIs

Governance: Catalog, observability, orchestration

Success criteria

Generate 4+ opportunities in 12 months and $4M+ in incremental contracted revenue.
Unify 80%+ of enterprise knowledge sources.
Deliver assistants with measurably higher grounding accuracy.
Put an agent operating model with governance and performance KPIs into production.
Deploy multi-agent orchestration patterns across at least two business functions.