AWS Premier Partner · Data Engineering

Your data is fragmented across CRM, billing, claims and ten more systems.|AI agents can't reason across them. The semantic layer can.

Every operational system was built separately, by different teams, at different times. The same business entity lives in each one under a different name and format. Engineers know how it fits together. AI agents don't. Armakuni builds the semantic layer that fixes this. One accurate interface across every source system, deployed in your AWS account in 6 to 8 weeks.

Engineers know how it fits together. AI agents don't.

Talk to a Solution Architect → See Axiom →

Trusted by teams shipping on AWS

The Core Insight

You don't need to consolidate your databases.|You need one layer that speaks for all of them.

The instinct is to clean the schemas, build a unified data store, run a two-year consolidation programme. That's the wrong fix for an AI-agent problem. Your agents don't need a rewrite of every source system. They need a layer that defines what an entity is, maps it to the columns wherever they live, and gives every agent one accurate interface to ask through. The source systems stay where they are. Nothing moves. Nothing gets rewritten.

6-8 wks

To Production

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.

Armakuni engagement model

Source Systems Unified

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.

Armakuni production studies

Semantic, Kinetic, Dynamic

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.

Axiom architecture

The Reframe

Most consultancies want to consolidate your databases.|We build the layer that speaks for them, without changing one of them.

A consolidated data store sounds clean and takes two years. The agents need a working interface in eight weeks, not a transformation programme they will outlive. Axiom is what that interface looks like: a semantic catalog, a query API, and MCP endpoints sitting on top of 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.

How Axiom Lands

Five stages.|Each one earns the right to the next.

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.

Stage 01

Discovery

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.

Stage 02

Semantic Layer

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.

Stage 03

Kinetic Layer

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.

Stage 04

Dynamic Layer

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.

Stage 05

Handover

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.

What Runs Inside Axiom

Six AWS services do the heavy lifting. Your team learns the ones they didn't already know on the way in.

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.

Amazon DataZone

The authoritative entity definitions and governance. Customer, policy, claim defined once with their properties and relationships, independent of any database.

Semantic layer · Governance

Amazon Bedrock

The semantic reasoning engine. Translates agent queries against the entity model and returns governed, definition-aware answers, not raw SQL guesses from column names.

Semantic layer · Query reasoning

AWS Glue Catalog + Crawlers

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.

Kinetic layer · Mappings + change detection

AWS Lambda

Query execution and routing across the source systems. Each entity query resolves to the right physical columns under the right governance per query context.

Kinetic layer · Query execution

MCP Endpoints + CloudWatch

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.

Dynamic layer · Interface + observability

AWS IAM

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.

Dynamic layer · Access control

Armakuni has done a phenomenal job with our team, they are our go-to partner for 2026 for all data and AI initiatives.

Brandon Hoffman · CEO, MT Lead Media · MT Lead Media

What customers say

Engineering discipline applied across every offering.

Here's what it built.

Modernization

Live BBC donation platform.

Armakuni was a game-changer for Comic Relief; they built us a new donation platform, on time and on budget while also helping us break the GBP 100 million mark for the first time.

Cloud Native Delivery

Global banking, delivered cloud-native at scale.

HSBC adopted cloud-native delivery through the AK Way. Automated pipelines, continuous testing, and engineering discipline applied at global-bank scale.

Enterprise Banking

Global investment and commercial banking on AWS.

JPMorgan Chase is one of five of the world's largest financial institutions Armakuni has delivered AWS engineering work for. Proof the AK Way holds at systemically important scale.

Media & Entertainment

Global sports and media brand, engineered for scale.

Red Bull is part of Armakuni's media and entertainment portfolio. Proof the AK Way extends beyond financial services into global consumer brands.

AI Transformation

Home services marketing intelligence, built on AWS.

Ryno's internal team had already tried and failed to reach production quality on AI call analysis. Armakuni's PoC was production-ready in under two weeks of a four-week window.

Engineering Transformation

Global retail and commercial banking, engineered the AK Way.

Santander transformed its engineering teams through "The Engineering Way." Everything as code, automated testing, and a high-velocity cloud-native delivery force built on the AK Way.

Pods

Real-time vehicle-tracking platform for finished-vehicle logistics.

45% increase in on-time vehicle deliveries, replacing a reactive model where delays were only discovered after they'd already cost the customer.

The Payoff · Axiom Rollout Timeline

Six to eight weeks.|Three layers in production.|Owned by your team at handover.

Median trajectory across recent Axiom engagements where teams ran the three-layer architecture and the catalog was deployed in their AWS account.

Week 2

Schema mapped

Glue Crawlers connected, entity mappings confirmed, 2-3 domains agreed

Week 5

Catalog live

DataZone entity model published, Bedrock query API tested per entity

Week 7

Endpoints serving

MCP endpoints deployed, CloudWatch dashboards live, IAM access enforced

Week 8

Owned by your team

Runbook handed over. Armakuni access removed. Catalog extends without us.

What We Build

Axiom is the product.|The Discovery is how you find out if it's right for your data.

Productised solution · 6-8 weeks

Axiom

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.

Explore Axiom →

Compass scan: codebase assessment across 5 layers

What Axiom delivers

Three layers, one system. Semantic for entity definitions. Kinetic for physical mappings and schema-change detection. Dynamic for access control and observability.

MCP-native interface. Read-only endpoints per domain. Bedrock Agents, Claude, or any MCP-compatible agent connects immediately. No new runtime.

Yours after handover. DataZone catalog, Glue mappings, Lambda functions, MCP endpoints, dashboards, runbooks. All transferred to your team. Armakuni access removed.

Discovery engagement · 1-2 weeks

Axiom Discovery

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.

Explore Discovery →

Evolve: governed agentic modernization with orchestration layer

What Axiom Discovery delivers

Real schemas, not a demo. Crawlers connect to your actual source databases. The entity map covers the columns your agents will query in production, not a sanitised sample.

Authoritative source per entity. Customer in CRM, account in billing, counterparty in finance. Discovery determines which system is authoritative per query context.

Roadmap, not a slide deck. You leave with a scoped Axiom build, the priority domains chosen, and the fit assessment honest enough to walk away from if the architecture isn't right.

Workshops · See the semantic layer on your data

Half a day. Your data.|One real answer: is Axiom right for you?

Axiom Workshop

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.

Half dayFree

HALF DAYFREEBRING YOUR DATA

Solution Architect Discovery

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. Leave with a one-page assessment and a clear next step, whether that is Axiom or something else.

1 hourFree

1 HOURFREEARCHITECT-LED

Where the Semantic Layer Lands

Same architecture. Different access boundaries per sector.

→

Core banking · Payments · Capital markets

BFSI

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.

→

Payers · Providers · Life sciences

Healthcare

PHI cannot leave the account. Guardrails and SCPs keep every Bedrock call inside HIPAA boundaries. Evolve has modernized claims, scheduling, and EHR-adjacent systems without a single cross-account spill.

→

Omnichannel · Supply chain · Marketplaces

Retail

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.

→

MES · ERP adjacencies · OT-IT bridges

Manufacturing

Systems where a bad change takes a production line offline. Inspector and Config drift checks run before any Evolve agent touches a service that talks to the shop floor.

→

BSS · OSS · Network platform

Telco

Billing stacks that cannot go dark. Evolve targets the adjacencies first, strangles the core last, and keeps the whole carrier graph visible in CloudWatch the entire time.

Customers running on the semantic layer.|Different sectors. Same architecture.

Award-winning LMS provider for enterprises and mid-size organizations

Award-winning LMS provider for enterprises and mid-size organizations earned C-Suite trust on a regulated edtech platform with the AK Way governance documented for the regulator.

Read use case →

Gavel modernised a live video auction platform with microservices migration and multi-gateway payments shipping in production.

Read use case →

One of the largest US payment technology and prepaid solutions companies

One of the largest US payment technology and prepaid solutions companies rebuilt a GLBA- and HIPAA-compliant document platform with policy-as-code on every AWS service.

Read use case →

One of the largest cinema networks in the U.S.

One of the largest cinema networks in the U.S. turned a high-speed application-integration challenge into a success story with AK Way speed and partnership.

Read use case →

Marketing automation platform for e-commerce and restaurant brands

SMS campaign automation platform for e-commerce and restaurant brands shipped governed agentic modernization with the orchestration layer transferred to their team on exit.

Read use case →

Real outcomes, real customers

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 →

Industries

Where this lands by sector

BFSI, healthcare, retail, manufacturing, telco. Constraints by industry. Response by orchestration.

All industries →

Insights

How we think about modernization

Engineering essays, AK Way deep-dives, AWS Premier Partner specializations.

Read insights →

AWS Premier Tier Services Partner

Named on the SOW. Specialized where it matters.

Active AWS competencies and service-delivery designations behind modernization work.

AWS
Premier

AI Services
Comp.

Migration
Comp.

DevOps
Comp.

Well-Arch.
Partner

EKS
Delivery

Lambda
Delivery

CFN
Delivery

+30 more

Common questions

What CIOs ask before booking a Discovery Sprint.

How long is a typical engagement?

Compass is two to three weeks at a fixed fee, scoped on portfolio size. Evolve is eight to twelve weeks at fixed scope per workload. Most customers run Compass standalone first, commit to Evolve once the roadmap is in hand.

What does the team shape look like?

Evolve runs as a five-to-seven-person pod: solution architect, two senior engineers, an AI/agent specialist, and an AK Way practice owner. Sized to your codebase complexity, not bench utilization.

Do we need to already be on AWS?

No. Compass runs against any cloud or on-premise stack. Evolve targets AWS for the agentic orchestration layer (Bedrock, Step Functions, IAM, CloudWatch), but the assessment itself is cloud-agnostic.

How does pricing work?

Compass is fixed fee, scoped on portfolio size. Evolve is fixed scope per workload, MAP-eligible. AWS Migration Acceleration Program funding typically offsets a meaningful share of engagement cost.

What does success look like?

Median outcomes across recent Evolve engagements: 60 percent faster deployment by week four, two-point-one million dollars in first-year savings by week eight, four-point-two times deploy frequency by exit. Tracked against your baseline DORA metrics from day one.

Discovery

Bring two or three of your data domains.|We'll sketch the entity model on your real schemas.

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 for your environment or whether the foundations need work first. The first conversation is with the solution architect who would run the engagement.

For your CTO/COO

Book a Discovery Sprint →

For your engineering lead

Your first conversation is with the solution architect who would run the engagement.