Every operational system was built separately. The same customer lives in each one under a different name. Engineers can reconcile that in their heads. AI agents can't. Axiom is the semantic layer that does the reconciliation in six to eight weeks, deployed in your AWS account.
Trusted by teams shipping on AWS
The instinct is to consolidate every database into one warehouse. That's two years of work for what the semantic layer solves in eight weeks. Axiom defines what a customer is and maps it across whichever columns hold it today. Source systems stay where they are.
Axiom is in production in 6 to 8 weeks across 2 to 3 priority data domains. Bounded scope, fixed fee, exit criteria agreed before work starts.
Built for organisations with 3 or more source systems modelling the same business entities under different names. CRM, billing, claims, support, underwriting. One entity model across all of them.
Three connected layers, deployed as one system. Semantic defines the entities. Kinetic maps them to physical columns and tracks lineage. Dynamic enforces access and logs every query.
A consolidated data store sounds clean and takes two years. Agents need a working interface in eight weeks, not a transformation programme they will outlive. Axiom is the interface: a semantic catalog, a query API, and MCP endpoints over every source system you already run.
The semantic layer is infrastructure you own. Amazon DataZone, Bedrock, Glue, Lambda, MCP endpoints, CloudWatch, IAM. All deployed into your AWS account under your governance and your audit trail. When the engagement ends, your data engineers run the catalog, extend it to new domains, and add entities without us in the room.
Axiom is a sequenced rollout, not a delivery sprint. Every stage produces a checkable artefact: a confirmed entity map, a tested query API, a wired-in observability layer. By the final stage, your engineers own the runbook and Armakuni is already out of the room.
AWS Glue Crawlers connect to all in-scope source databases. Automated schema discovery compresses weeks of manual mapping into days. Your data engineers confirm what each source column actually represents and which system is authoritative per entity type. 2 to 3 priority data domains agreed.
Canonical entity model built in Amazon DataZone. Customer, policy, claim, transaction defined once with their properties and relationships, independently of any database. Bedrock provides the semantic reasoning engine that translates agent queries against the entity model.
Physical column mappings configured in AWS Glue with full lineage tracking. Lambda handles query routing and execution across the source systems. Glue Crawlers run on a schedule to detect schema changes and surface them for human review before the catalog updates.
MCP endpoints deployed per domain, read-only at the API level. CloudWatch dashboards configured for query volume, entity hit rates, resolution confidence and full audit trail. AWS IAM enforces entity-level access. A customer service agent reaches Customer and Policy data, never Claims, regardless of how the query is phrased.
Integration tested with the client's AI agents on live data. Runbook handed to your data engineering team: how to add entities, update mappings, extend to new domains, debug a low-confidence answer. Full ownership transferred. Armakuni access removed.
Axiom wires AWS primitives you are already paying for into a single semantic-layer surface. No new runtime. No new vendor licence. The catalog, query API, and endpoints all run in your account from day one.
The authoritative entity definitions and governance. Customer, policy, claim defined once with their properties and relationships, independent of any database.
The semantic reasoning engine. Translates agent queries against the entity model and returns governed, definition-aware answers, not raw SQL guesses from column names.
Physical column mappings with full lineage. Crawlers run on a schedule to detect schema changes and surface them for human review before the catalog updates.
Query execution and routing across the source systems. Each entity query resolves to the right physical columns under the right governance per query context.
Read-only MCP endpoints per domain so any MCP-compatible agent connects immediately. CloudWatch logs every query with confidence score, entity hits, and full audit trail.
Entity-level access controls enforced at the API. A customer service agent can reach Customer and Policy. It cannot reach Claims regardless of how the query is phrased. Hard constraints, not documentation.
A leading U.S. drug testing network operator needed a team that had expertise across different domains and was enthusiastic enough to iterate on a moonshot project. [Armakuni] had a long term vision… I wanted people who were willing to say 'yes' and deal with the questions. Armakuni came highly recommended by AWS.
Armakuni has done a phenomenal job with our team, they are our go-to partner for 2026 for all data and AI initiatives.
Natural language querying of complex marketing analytics now available to non-technical users for the first time. A CEO or marketing lead can ask a plain-language question about campaign performance or customer lifetime value and receive an accurate, visual answer without touching a report or writing a query. The usability barrier that was limiting market expansion has been removed.
Cross-region, cross-account consolidation proven. A multi-region healthcare care management platform provider has a validated architecture for unifying US and Canada data into a single pipeline. New client databases and new regions can now be onboarded into an established pattern.
40% higher ROI on targeted campaigns because behavior-driven segments now go to email and ad platforms automatically, replacing the broad audiences that wasted spend on customers unlikely to convert.
Median trajectory across recent Axiom engagements where teams ran the three-layer architecture and the catalog was deployed in their AWS account.
A semantic layer that lets AI agents query fragmented enterprise databases accurately. Three connected layers, deployed as one system, in production across 2 to 3 priority data domains. The catalog, query translation API, MCP endpoints, observability dashboards, and runbook all deploy in your AWS account.
Output: a working Axiom instance in your AWS account, owned by your data engineering team after handover.
A scoped engagement to validate fit and produce the agreed entity map before a full Axiom build. Glue Crawlers connect to your source databases, automated schema discovery runs, your data engineers confirm the authoritative source per entity type. The 2 to 3 priority domains for the build are agreed.
Output: a confirmed entity map, scoped Axiom roadmap, and an honest assessment of whether the architecture fits your data complexity.
A working session with engineers who have built semantic layers on AWS. Bring 2 or 3 of your highest-friction data domains. We sketch the entity model, identify the source-system fragmentation, and tell you whether Axiom is a 6-to-8 week build for your environment or whether the foundations need work first.
A 60-minute call with the solution architect who would run the engagement. Walk through your fragmentation, your AI-agent ecosystem, and the questions your agents are getting wrong today.
Thirty-year COBOL cores, monthly regulator cycles, and an AI roadmap the CTO already signed. The orchestration layer keeps every AI action audit-ready for the PRA, FCA, and SEC.
PHI cannot leave the account. Guardrails and SCPs keep every Bedrock call inside HIPAA boundaries. Armakuni pods have shipped against claims, scheduling, and EHR-adjacent systems without a single cross-account spill.
Peak-season refactors where a bad deploy costs a weekend of revenue. Canary gates inside Step Functions hold agents to the same release calendar your SREs already run.
Systems where a bad change takes a production line offline. Inspector and Config drift checks run before any Armakuni-deployed agent touches a service that talks to the shop floor.
Billing stacks that cannot go dark. We target the adjacencies first, leave the core for last, and keep the whole carrier graph visible in CloudWatch the entire time.
A leading U.S. drug testing network operator built a privacy-first IoT sensor system with AI/ML anomaly detection running over court-mandated data flows.
Read use case →
A digital lead generation and remarketing platform for the insurance industry stood up an AWS Landing Zone, governed Data Lake, and GenAI-powered BI with click-level analytics.
Read use case →A marketing analytics and attribution SaaS platform for e-commerce shipped natural-language querying over analytics data with AI-driven enrichment running on top.
Read use case →A multi-region healthcare care management platform provider replaced fragmented ETL with an AWS-native Redshift pipeline sized for compliance-ready BI.
Read use case →GORUCK unified eCommerce, events, and loyalty data into one warehouse with ETL and segmentation pipelines feeding the customer data platform.
Read use case →SMS campaign automation platform for e-commerce and restaurant brands moved their orchestration and analytics layer into their own AWS account, with the catalog owned by their data engineering team.
Read use case →
Award-winning LMS provider for enterprises and mid-size organizations, the identity platform, One of the largest cinema networks in the U.S., SMS campaign automation platform for e-commerce and restaurant brands. Specific numbers. Named stakeholders.
All case studies →BFSI, healthcare, retail, manufacturing, telco. Constraints by industry. Response by orchestration.
All industries →
Engineering essays, AK Way deep-dives, AWS Premier Partner specializations.
Read insights →Active AWS competencies and service-delivery designations behind data and analytics work.
Half-day Discovery on your real data. We connect Glue Crawlers to your source systems, walk through the fragmentation, and tell you honestly whether Axiom is a 6-to-8-week build or whether the foundations need work first.
Your first conversation is with the solution architect who would run the engagement.