FastAIContext

The Problem

Agents without context are flying blind.

They don't know which account is at risk of churning. They can't see the renewal history, the key contacts, or the open issues. They have no idea what policies govern their actions. Every agent call starts from zero — no memory, no structure, no governance.

🧠

The Context Gap

Every agent team builds the same bespoke context plumbing — ungoverned, unauditable, inconsistent across business units.

No Entity Awareness
Agents can't see the account's renewal history, key contacts, health score, or open issues — they start from zero every time

No Policy Governance
Agents offer discounts, make commitments, or escalate without knowing your business rules, approval chains, or compliance constraints

No Situational Awareness
Is that deal closing this week? Is the customer mid-escalation? Is renewal at risk? Agents operate without any temporal context

No Audit Trail
When an enterprise asks "why did the agent offer that discount?" — there's no answer. No trace, no rationale, no accountability

💸

The Cost to Your AI Programme

Every team builds the same bespoke context plumbing — ungoverned, unauditable, inconsistent across business units. For Chief AI Officers, this means ballooning infrastructure costs and no single pane of glass for context governance across your agent fleet.

Duplicated effort · Governance gaps · Zero auditability

✦

FastAIContext: A Curated Context Space

One query. Four layers of structured, governed, relevant context — assembled in real time. Policy engine governs agent actions before they happen. Full decision traces make every resolution auditable. Context improves with every interaction automatically.

Build once · Govern centrally · Enriched continuously

Four-Layer Architecture

From Raw Data to Agent Context in Four Layers.

FastAIContext is a purpose-built stack — from entity resolution at the bottom to a standards-based MCP interface at the top. Each layer builds on the one below.

AGENT INTERFACE → CONTEXT ENGINE → ONTOLOGY → SEMANTIC

Layer 04 · Agent Interface

MCP Server

5 MCP tools · Streaming transport · Any MCP-compatible agent connects once

↕

Layer 03 · Context Engine

Policies

Business rules · Approval chains · Compliance constraints

Layer 03

Knowledge

Domain docs · Playbooks · Hybrid vector + BM25 search

Layer 03

Situational State

Active deals · Escalations · Temporal context

Layer 03

Resolution

Conflict handling · Priority rules · Transparency

↕

Layer 02 · Ontology Graph

Knowledge Graph

Entity relationships · Org hierarchies · Customer connections · Domain concepts as first-class graph nodes

↕

Layer 01 · Semantic Layer

Multi-Source Entity Resolution

Probabilistic matching · Canonical records · Warehouse-first ingestion + API connectors + direct database access when needed

Agent Layer

Context Engine

Ontology

Semantic

Core Capabilities

Everything Your Context Layer Needs.

Six capabilities, one coherent system — spanning entity resolution, knowledge management, policy enforcement, and real-time situational awareness, all accessible through a single MCP interface.

Entity Resolution

Multi-source, probabilistic entity matching that creates canonical business records from warehouses, APIs, and production databases. The same customer is one entity — everywhere, for every agent.

Layer 01 · Semantic · Probabilistic matching

Knowledge Graph

Purpose-built graph database that captures organisational hierarchies, customer relationships, and domain concepts as first-class entities with typed edges and traversable paths.

Layer 02 · Ontology · Graph Database

Policy Engine

Declarative business rules, approval chains, and compliance constraints that agents evaluate before acting — not after. Policies are versioned, auditable, and conflict-aware.

Layer 03 · Context Engine · Declarative rules

Situational State

Real-time temporal context — active deals, ongoing escalations, pending approvals, current campaigns — so agents know what's happening now, not just what's been synced to the warehouse.

Layer 03 · Context Engine · Temporal awareness

Hybrid Search

Hybrid vector + keyword retrieval for domain knowledge, documentation, playbooks, and unstructured business context. Semantic and keyword search combined for maximum recall.

Layer 03 · Hybrid Search · Vector + BM25

Conflict Resolution

When policies clash, rules overlap, or context is ambiguous, the resolution engine applies priority hierarchies and surfaces decisions transparently — agents never silently pick the wrong rule.

Layer 03 · Context Engine · Priority rules

Context Layers

MCP Tools

Hybrid

Vector + BM25 Search

Real-Time

Situational State

Agent Interface

Five MCP Tools. One Integration.

FastAIContext speaks Model Context Protocol natively. Any MCP-compatible agent — Claude, custom builds, multi-agent frameworks — connects once and gets full business context.

✓Standards-Based — Built on the open MCP specification. No proprietary SDKs or vendor lock-in
✓Streaming Transport — Real-time, streaming connections from any agent runtime with low-latency context delivery
✓Production-Grade Server — Fully typed MCP server with schema validation, error handling, and observability built in
✓Zero Agent-Side Logic — All context intelligence lives server-side. Agents stay thin and focused

resolve_entity

Resolve an ambiguous entity reference to a canonical business record with full cross-source identity and confidence scores.

query_graph

Traverse the knowledge graph to find relationships, hierarchies, and connections between resolved entities.

get_context

Retrieve unified context — policies, knowledge, and situational state — for a given entity or decision point.

check_policy

Evaluate a proposed action against business rules, compliance constraints, and approval requirements before execution.

search_knowledge

Hybrid vector + BM25 search across domain documentation, playbooks, SOPs, and unstructured business context.

Semantic Layer

Warehouse-First. API & Database When Needed.

FastAIContext starts with your data warehouse as the primary source of truth — but connects to production APIs and databases when real-time or operational context demands it. Your data team sets the rules.

✓Multi-Source Matching — Probabilistic entity resolution across warehouse tables, CRM exports, API responses, and production databases
✓Canonical Records — Every matched entity becomes a single canonical record with lineage back to every source
✓Confidence Scoring — Every resolution carries a confidence score agents can use for decision-making
✓API & Database Connectors — When real-time data is needed, connect directly to REST APIs, GraphQL endpoints, or production databases alongside warehouse sources
✓UI-First Configuration — Configure entity resolution rules and data source connections through the UI, not YAML files. AI assistant helps with mapping

🔗Entity Resolution

● 4 Sources Linked

CANONICAL ENTITY: ACME CORP

📦 Snowflake dim_customersCONF: 0.97

💼 Salesforce AccountCONF: 0.94

🎧 Zendesk organizationsCONF: 0.89

🔌 HubSpot API REST /companiesCONF: 0.91

GRAPH RELATIONSHIPS

→ 3 Contacts → 2 Active Deals → 1 Escalation → 5 Policies

LAST SYNC2 min ago · Incremental · 0 conflicts

Context Engine

Policies, Knowledge & State — Unified.

The Context Engine unifies everything agents need to make informed decisions — business rules, domain knowledge, temporal state, and conflict resolution — in one query.

✓Declarative Policies — Business rules defined once and enforced consistently across all agents via check_policy
✓Hybrid Knowledge Retrieval — Combined vector + keyword search for domain docs, SOPs, playbooks, and unstructured content
✓Temporal State Tracking — Active deals, pending approvals, ongoing incidents — real-time state agents can query
✓Priority-Based Resolution — When policies conflict, the resolution engine applies hierarchy rules and surfaces the decision
✓Unified via get_context — One MCP call returns the full context for any entity or decision point

⚡Context Response

● get_context("acme-corp")

POLICIES (3 ACTIVE)

✔ Discount max 15% without VP approval
✔ No contract changes during escalation
✔ GDPR data handling required (EU customer)

SITUATIONAL STATE

⚠ Active escalation (P2) since Mar 3
📋 Renewal deal closing in 12 days
👤 New champion onboarded last week

KNOWLEDGE (TOP HITS)

📄 Enterprise renewal playbook (0.94)
📄 Escalation handling SOP (0.91)
📄 GDPR compliance checklist (0.87)

RESOLUTION1 conflict resolved · "No contract changes" overrides discount policy

The ContextPackage

One call. Everything your agent needs.

Nothing it doesn't. Assembled in real time. Cached intelligently.

{ "entity": { ← the account "id": "ctx_account_acme_corp", "name": "Acme Corp", "arr": 240000, "health_score": 0.72 }, "policies": [{ ← what governs this interaction "name": "discount_policy_v2", "effect": "BLOCK", "condition": "discount_pct > 0.20 requires approval" }], "situation": { ← what changed recently "arr_trend": "+20%", "open_escalations": 1, "renewal_date": "2026-04-15" }, "knowledge": [ ← curated fragments, ranked for this task { "content": "Renewal at risk due to support delays", "confidence": 0.84 }, { "content": "Champion contact is VP of Engineering", "confidence": 0.91 } ], "executive_summary": "Acme Corp is up for renewal in 37 days. ← ready-to-use paragraph ARR has grown 20% YoY. One open escalation (support SLA breach) poses renewal risk. Discount requests above 20% require manager approval.", "completeness_score": 0.81 }

Context Layers

<2s

Assembly Time

MCP Call

100%

Auditable

Decision Traces

The audit trail regulators and enterprises need.

Every resolution logs which fragments were provided, which were ranked out and why, what the agent did, and which policies were respected or overridden. When an enterprise asks "why did the agent offer that discount?" — you have a complete answer, down to the individual knowledge fragments the model saw.

✓Full Audit Per Resolution — Pre-rank and post-rank fragment lists, confidence scores, and drop rationale for every context assembly
✓Policy Compliance Tracking — Which policies were evaluated, respected, or overridden — with the agent's action outcome logged
✓Searchable & Linkable — Every trace is indexed, queryable, and linkable from external governance tools and compliance platforms

Decision Trace — renewal_prep / Acme Corp / CSM Agent

Pre-rank fragments8

Post-rank fragments4

Dropped4

Rationale: "Excluded 2 fragments older than 90 days and 2 flagged as low-confidence (<0.6). Retained renewal risk insight and champion contact as highest relevance for task type renewal_prep."

Outcomecompleted

Policies respecteddiscount_policy_v2

Completeness0.81

TRACE IDctx_trace_a7f2c · 1.4s · Searchable · Linkable

Knowledge Flywheel

Context that improves with every interaction.

FastAIContext is not a static knowledge base. Every agent interaction is automatically mined for new insights. Documents are classified and extracted. Messages are resolved to entities. Expired knowledge is archived. The system gets smarter without manual curation.

Connect Sources

↓

Agents Use Context

↓

Auto-Extraction Captures Insights

↓

Knowledge Base Grows

↓

Next Agent Gets Richer Context

↺ CONTINUOUS LOOP

📥

Auto-Extraction

Documents, Slack messages, emails, and agent interaction transcripts are automatically parsed, classified, and resolved to business entities in the ontology.

Documents · Slack · Email · Transcripts

📊

Confidence Scoring & Expiry

Every knowledge fragment carries a confidence score. Low-confidence fragments are demoted. Outdated fragments expire automatically. Quality improves over time.

Scored · Time-decayed · Self-cleaning

🧠

Schema-Governed Memory

Extracted knowledge is structured against your business ontology — not dumped into a flat vector store. Every insight is linked to entities, typed, and queryable.

Ontology-linked · Typed · Queryable

Data Sources

Warehouse-First. API & Database When Needed.

Start with the data platforms where your business entities already live. When real-time or operational context is required, FastAIContext connects directly to production APIs and databases alongside your warehouse.

Data Warehouses

Databricks

Unity Catalog, Delta tables, direct lakehouse access

Snowflake

Stages, views, and secure data sharing

BigQuery

Datasets, federated queries, ML integration

Azure Synapse

Dedicated pools, serverless SQL, Spark

APIs & Production Databases

REST APIs

CRM, ERP, support tools, custom services

GraphQL

Typed queries against any GraphQL endpoint

SQL Databases

Direct production database access when needed

Custom Connectors

Extensible connector framework for any data source

Warehouse sources serve as the canonical foundation. API and database connectors layer on real-time operational data — your data team controls which sources feed each entity type.

Connector Library

29 Pre-Built Connectors. Zero Glue Code.

Powered by the FastAIAgent connector library — the same production-grade connectors used across the FastAIFoundry ecosystem. Databases, CRMs, cloud storage, messaging, document processing, and web — each connector is a reusable, domain-level resource for entity ingestion.

PostgreSQL

MySQL

Oracle

Azure SQL

SQLite

MongoDB

Elasticsearch

Redis

Amazon S3

Azure Blob

Google Drive

SFTP

Slack

Microsoft Teams

Salesforce

Dynamics 365

Gmail

Outlook

SMTP Email

Apache Kafka

REST API

GraphQL

PDF Reader

Word Reader

Excel / CSV

Web Scraper

Web Crawler

🔌

Shared Connector Ecosystem

The same 29 connectors used in FastAIAgent are available natively in FastAIContext. Configure once, use for entity ingestion — no separate integration work required.

FastAIAgent shared · Zero duplication

🔄

Connector-to-Entity Pipeline

Any connector can feed the entity resolution pipeline. Pull customer records from Salesforce, support tickets from Zendesk, or transaction data from PostgreSQL — all resolved into canonical entities automatically.

Any source → Canonical entity

🔒

Encrypted Credentials

All connector credentials are secured with AES-256-GCM encryption. Secrets are never stored in plaintext and are decrypted only at execution time within your network perimeter.

AES-256-GCM · Zero plaintext storage

Platform

Production-Grade Infrastructure. Purpose-Built.

Every component is chosen for its role in the context stack — from async API handling and graph traversal to hybrid search and real-time state management.

Async Backend API

High-performance async API handling entity resolution, context queries, warehouse ingestion, and API connector orchestration with full type safety.

Backend · API Layer

MCP Agent Server

Production MCP server exposing five tools over streaming transport. Schema validation, error handling, and agent identity scoping built in.

Agent Interface · MCP

Configuration UI + AI Assistant

Modern web UI for entity mapping, policy authoring, and graph exploration. Built-in AI assistant for guided setup — UI-first, not YAML-first.

Frontend · AI-Assisted

Graph Database

Purpose-built graph storage powering the ontology layer. Typed nodes, relationship traversal, and complex query support for business structure navigation.

Layer 02 · Knowledge Graph

Entity Store + State Cache

Relational storage for canonical entities and policy definitions. In-memory cache for session state and real-time situational context tracking.

Storage · Cache · State

Hybrid Search Engine

Combined vector + keyword search for domain knowledge retrieval. Semantic understanding meets keyword precision in a single query interface.

Layer 03 · Hybrid Search

Container

Based Deployment

EU-Hosted

Infrastructure Ready

MCP

Standards-Based Interface

Self-Hosted

Full Data Sovereignty

Deployment

Self-Hosted. Your Infrastructure. Your Data.

FastAIContext deploys on your infrastructure via container orchestration. No data leaves your environment — every entity, policy, and context query stays within your security perimeter.

🐳

Container-Based Deployment

Full stack up in minutes on any Linux server. All services — backend API, MCP server, UI, graph database, search engine, and cache — orchestrated with a single configuration. No complex infrastructure required.

EU-hosted ready · Air-gap capable

🔐

Complete Data Sovereignty

Entity resolution, knowledge graphs, and policy evaluation all run locally. Warehouse credentials and API keys never transit third-party services. All connector traffic stays within your network perimeter. Meets data residency requirements for financial services, healthcare, and government.

Zero data egress · On-premise · GDPR aligned

Observability Built In

Structured logging, MCP tool call tracing, entity resolution audit trails, and context query metrics — ready for your existing monitoring stack.

Logs · Traces · Metrics

Enterprise Auth Ready

SSO integration, role-based access to ontology management, policy authoring permissions, and MCP tool scoping per agent identity.

SSO · RBAC · Agent scoping

AI-Assisted Configuration

Built-in AI assistant guides your data team through entity mapping, policy authoring, and ontology design — UI-first, not YAML-first.

AI-assisted · UI-first · Guided setup

Part of the FastAIFoundry Ecosystem

Context that Powers the Entire Stack.

FastAIContext is the knowledge layer within the broader FastAIFoundry product family. Build agents in AIAgent. Consume them in AIWorkspace. Ground them in AIContext. Govern them with AIShield.

FastAIAgent

Build, test, deploy, and run intelligent agents with visual workflows, knowledge bases, and full lifecycle management.

Live

FastAIWorkspace

Thin-client consumption portal for business users. Run agents, view results, and manage work — no technical skills required.

Live

The curated context space. Entity graph, policy engine, knowledge flywheel, and decision traces that make agents organisation-aware.

You are here

FastAIShield

AI Governance and Compliance. Risk assessment, audit trails, and regulatory readiness for EU AI Act and beyond.

Live