An agent asks for revenue by region. Three systems hold three different definitions. Nobody catches the drift until the board pack is being printed. Axiom runs on Bedrock AgentCore and reads business definitions directly from Amazon DataZone, so the number the agent returns is the one your CFO already defends. Six to eight weeks to production in your AWS account.
Three engines under one vocabulary. Lakes through S3 Tables and S3 Files. Relational stores by pushdown to Aurora and Redshift. SaaS APIs and knowledge bases registered through DataZone. Every store carries the same definition of 'customer'. Your team owns all of it the day we hand over.
Your AI agents don't crash. They don't throw errors. They return answers that look right, pass review, and quietly drive wrong decisions for weeks.
These aren't edge cases. They're the default behavior of any AI agent querying fragmented enterprise data without a shared definition of what the data means.
MCP connects your agents to data. It tells them where things live. It doesn't tell them what things mean.
Your agents can reach every database, every API, every warehouse. They still don't know that "revenue" means three different things in three different systems. They still can't tell the difference between a customer, an account, and a counterparty.
The missing piece isn't connectivity. It's a layer of shared meaning that sits between your agents and the data they query. A semantic layer that defines what entities are, how they relate, and what questions are safe to answer.
That's what Axiom builds.
Axiom is three layers, deployed as one system, served through MCP.
Axiom runs on Bedrock AgentCore and reads business glossaries and data taxonomies directly from Amazon DataZone. Your data catalog becomes the source of truth for every AI and BI query. Hallucinations get blocked at the schema, not patched at the prompt.
Axiom routes each request to one of three specialized engines, then delivers clean, unified data to the presentation layer. SaaS APIs and knowledge bases get queried with the exact same vocabulary as your internal warehouse.
Ask: "What was last quarter's revenue by region?"
Now ask: "Which customers placed orders in Q4 but haven't reordered?" Watch the semantic layer resolve "customer" across your CRM, billing, and support systems into a single entity. Watch the kinetic layer route across three databases. Watch the dynamic layer enforce that the requesting agent has permission to see customer purchase history. Same architecture. Different domain. Same governed answer.
This is what separates "the SQL executed" from "the answer is right."
These six capabilities work together because they were designed for agentic AI on fragmented enterprise data, not retrofitted from a BI tool or a data lake.
Schemas change. Columns get renamed, tables get restructured, relationships shift. Most systems break silently. Axiom detects schema changes proactively and updates semantic mappings before your agents query stale definitions.
You can see what every agent is asking, which definitions they're using, which data sources they're hitting, and whether the answers are consistent. When agents become autonomous, knowing what they're doing isn't optional.
Not a wrapper around a REST API. Native MCP endpoints that serve semantic context to any MCP-compatible agent. Connect once, query governed data across every source system.
Customer in CRM, account in billing, counterparty in finance. Axiom resolves them to a single entity with source-specific attributes preserved.
One definition of revenue. One definition of churn. One definition of active user. Enforced at query time, not in documentation nobody reads.
Every answer traces back to its source data, through the semantic definition, through the governance policy. For compliance, for debugging, for trust.
AWS builds the infrastructure. Axiom is the modular, serverless architecture that turns those services into a centralized semantic engine for agentic AI.
Every component runs in your AWS account, every config is yours to modify, and the operational overhead lands in services you already pay for.
The reasoning engine. AgentCore Runtime executes inside Firecracker microVMs (the same isolation used by Lambda). AgentCore Gateway routes requests through the semantic layer. AgentCore Identity brokers IAM and OAuth 2.0 for outbound authorization. AgentCore Observability streams Compass and CloudWatch telemetry to the AG-UI dashboard with zero overhead.
The source of meaning. Business terms map deterministically to physical schemas and join keys. The DataZone API gets cached as a parquet file in S3 (mounted on microVM wake-up), with EventBridge refreshing the cache on catalog change. Built for scale without hammering the control plane.
Axiom exposes its semantic engine to Amazon Quick over MCP. Quick stays the BI tool your team already knows; Axiom turns it into a deterministic, enterprise-verified interface. The AG-UI protocol streams the agent's reasoning live so users see why the answer is right, not just what.
Apache Iceberg tables via Amazon S3 Tables for managed lakehouse analytics. S3 Files mounted directly inside AgentCore microVMs for raw parquet access via standard file paths. DuckDB pushes predicates to cloud storage; Polars zero-copies via Apache Arrow for advanced logic. Native processing speed on raw data with no staging.
Glue keeps schema versioning current so semantic definitions never go stale. For relational stores, Axiom pushes compute down to Aurora, RDS, Redshift, EC2 databases, or on-prem read replicas. The native database engine runs the query. Axiom routes, scopes, and authorizes.
Cognito or any standard enterprise OAuth provider validates every inbound request before AgentCore touches a byte of data. Paired with AgentCore Identity outbound, you get zero-trust at both the application layer and the data layer.
Here's what your team owns after a Axiom deployment. Not what we manage for you. What you run independently.
Entity definitions, metric logic, relationship maps for 2-3 priority data domains. The single source of truth your agents query.
Connection configs, schema registry, and version tracking across your source systems. Your data stays where it is.
Query policies, access governance, cost controls configured for your agent ecosystem.
Production-ready endpoints your agents connect to immediately. Standard protocol, governed access.
Visibility into what your agents are querying, what definitions they're using, and whether answers are consistent.
Proactive monitoring of source schema changes with automated semantic model updates.
Timeline: 6-8 weeks. Scoped to 2-3 data domains. Not a transformation program. A bounded deployment that proves the architecture works on your data before you expand.
After handover, your team runs it. We're available for expansion to additional domains, but you're not dependent on us for the system to function.
Your enterprise runs on AWS (or is mid-migration)
You have AI agents querying operational data across 3+ source systems
Your teams disagree on metric definitions (revenue, churn, active users)
You've seen AI demos work on clean data and fail on your actual schemas
You need governed agent access, not another dashboard tool
You have one data source and no cross-system complexity
You're looking for a BI platform or a replacement for your warehouse
Your AI initiative is still in strategy phase with no agents deployed
You need a data migration before your data is queryable at all
We'd rather tell you this page isn't for you than waste your time on a solution that doesn't match your problem.
The model doesn't matter.
The meaning layer does.
That's the next decade of enterprise AI.
NHS Wales had a public-health dataset spread across multiple systems. Policy makers were waiting days for answers. We built the cloud-native semantic platform in two weeks. Answers now land in two minutes, with 90% better data accuracy and HIPAA-grade lineage on every query.
Gartner now classes semantic layers as critical infrastructure for agentic analytics in 2026. By 2028, the analyst expects 60% of projects without one to fail. The model is commodity. The data is the given. The semantic layer is what makes the answers trustworthy.
A leading premium wildlife stock footage platform built agentic AI inside their content pipeline with the orchestration layer in their AWS account.
Read use case →A regional agricultural cooperative in the U.S. Midwest deployed production AI with the controls documented and the platform running in their account.
Read use case →Award-winning LMS provider for enterprises and mid-size organizations modernized a regulated edtech platform with delivery the C-Suite could brief the board on.
Read use case →One of the largest cinema networks in the U.S. integrated AI on Connect with per-tool allow-lists and audit trails ready for live regulator review.
Read use case →SMS campaign automation platform for e-commerce and restaurant brands shipped agentic AI on the data layer with the orchestration layer running inside their AWS account.
Read use case →The Axiom Workshop is how you find out whether this is the right approach for your data. Not a sales pitch. Not a maturity assessment. A working session with engineers who've built semantic layers on AWS.
Walk away with a draft entity model either way.
AWS Premier Tier Services Partner
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