Data Warehouse Modernization for AI-Ready Data, Delivered on a Fixed Date

  • Agent-readable schema
  • Lineage to source
  • Freshness targets
  • Date in the SOW
  • AWS Premier Tier

Your AI roadmap probably assumes a warehouse that can answer an agent correctly, show where the answer came from and say how current it is. Most legacy warehouses manage none of that without a person checking. Mactores rebuilds the warehouse on AWS against a completion date fixed in the SOW and a fee settled before build, with agents doing most of the groundwork, so your data is fit for agents before the first one goes live. If the delay is ours, the cost is too.

Agents on the roadmap?

Learn what your warehouse would need to support them, before a pilot shows you the hard way.

A short call covers what an agent needs from your data and how long it takes to get there.

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AWS Partner Network
Premier Tier
AWS Specialization
Agentic AI
Consulting Competencies
7 Competencies
Service Validations
17 Validations

60–70%

share of engagement effort carried by agents, not billable analysts

12 wks

median time to production, measured over every Mactores engagement

21

case studies published under real customer names

200+

AWS-certified FDEs

2008

when Mactores began delivering production work on AWS

Top Mactores Clients

All 21 case studies →
Customers include Safaricom, Synaptics, Flipboard, Poshmark, Seagate, HP, Adani, DocuSign, KlearTrust, Tilia, Sterne Kessler and Total Expert.

An analyst who spots an odd number in a dashboard pauses and asks around. An agent does not. It queries, treats the result as fact and acts on it: it files a forecast, flags a transaction or answers a customer. That raises the bar for the warehouse underneath. Columns need business meaning, every figure needs a traceable origin, freshness needs a published target, and permissions have to follow an agent the way they follow an employee. Mactores modernizes the warehouse to that bar, with one team of FDEs from the first inventory to the final decommission.

Readable by machines

Tables and columns carry documented business definitions, so an agent picks the right field instead of the closest-sounding one.

Traceable to source

Any metric an agent cites can be followed back through its transformations to the system that produced it.

Fresh by commitment

Each data product ships with a stated refresh target and monitoring, so stale data is caught before an agent repeats it.

Dated and priced upfront

The completion date and the fee sit in the SOW, and any overrun we cause is ours to fund.

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Scoping, not guesswork

Leave scoping knowing your completion date, your fee and the first agent use case the warehouse will serve.

Scoping turns an AI ambition into a warehouse plan with numbers attached.

Book a scoping call

Why Does a Data Warehouse Need Modernizing Before You Deploy AI Agents?

Because agents amplify whatever the warehouse already gets wrong. Gartner expects organizations to drop six in ten AI projects through 2026 when the data behind them is not AI-ready, and it found that 63% of organizations either lack the data management disciplines AI requires or cannot tell whether they have them. A later Gartner survey of 353 data, analytics and AI leaders tied the most mature AI-ready data capabilities to as much as 65% better business outcomes.

Inside a warehouse the failure modes are concrete: a stored procedure that quietly redefines revenue, a nightly batch that makes this morning's answer yesterday's, a column named AMT_2 that three teams read three ways. People learn to work around these. Agents don't. Modernization removes them at the source, and our delivery model is what lets that happen on a fixed date. Agents absorb the repetitive 60–70% of effort, and FDEs make the calls that need judgment.
Agents · the repetitive 60–70%
FDEs · the decisions

Source discovery, schema mapping, lineage extraction, data-quality and reconciliation tests

Target architecture, business definitions, the access model for agents, cutover sequence and sign-offs

The terms in full

Read who pays when a modernization date slips.

The commitment page sets out the clause before any scoping conversation starts.

See the commitment

What Does AI-Ready Data Warehouse Modernization Include?

The work covers the warehouse itself and the layer an agent will touch. Typical starting points are Teradata, Netezza, Oracle, SQL Server, on-premises Hadoop and ageing Redshift clusters. The AWS target is chosen from the workload: usually Amazon Redshift, sometimes an Amazon S3 lakehouse on Apache Iceberg tables, often both.

  1. 01

    Inventory and profiling of sources, schemas, transformation code, pipelines, consumers and data-quality defects, so scope and fee rest on evidence

  2. 02

    Target architecture on AWS: Amazon Redshift for governed analytics, Amazon S3 with Apache Iceberg where open formats or lakehouse access matter, and AWS Glue for pipelines

  3. 03

    A semantic and metadata layer: business definitions on tables and columns, a governed catalog with lineage in Amazon SageMaker Catalog, and a freshness target for each data product

  4. 04

    Agent access designed in: individual agent identities, row- and column-level security, query guardrails, and tested interfaces such as Amazon Bedrock Knowledge Bases structured retrieval or the open-source Amazon Redshift MCP server

  5. 05

    Parallel-run reconciliation, a staged cutover, and retirement of the legacy warehouse with its licences and hardware

Looking for Redshift migration mechanics, such as Teradata script conversion, distribution keys, or RG versus Serverless? Those sit on our AWS Redshift migration consulting page. This page is about the end state an AI program needs, and it belongs to the wider Data Platform Modernization practice.


Where are you starting?

Tell us the warehouse you run today and the first agent you want to point at it.

The source platform sets the migration route; the agent use case defines what "ready" has to mean.

Check your migration path

What Are the Phases of Modernizing a Legacy Data Warehouse for AI?

  1. 01

    Evidence-based scoping

    Agents inventory the warehouse, read its query history and profile its data to show which tables matter, which transformations change business meaning and where quality breaks. FDEs pair that evidence with the first two or three agent use cases you plan, so "AI-ready" is defined against real questions rather than a checklist. The SOW then fixes scope, completion date and fee.

    Phase exit

    SOW signed with scope, date, fee and the agent use cases the warehouse must serve.

  2. 02

    Rebuild and describe

    The target is built on AWS, data is loaded and kept in step with the source, and pipelines are rebuilt or re-pointed. As each table lands, it gets a business definition, an owner, a freshness target and lineage back to source, all recorded in the catalog instead of in someone's memory. Your data owners approve each definition before this phase ends.

    Phase exit

    Definitions, lineage and freshness targets approved by your data owners.

  3. 03

    Reconcile and switch

    The old and new warehouses process the same loads side by side, and reconciliation checks counts, totals and key report outputs until they agree. Cutover then moves consumers in planned groups, with reports first and agents last, and rollback conditions are settled in advance.

    Phase exit

    Both warehouses reconcile; rollback conditions signed off.

  4. 04

    Agent readiness and retirement

    Before any agent goes live, its access is tested: the right rows and columns, sensible query limits, and answers compared with known results for your use cases. Performance is tuned against real usage, and the legacy warehouse is switched off, licence and hardware included, which is the step that frees money for whatever comes next.

    Phase exit

    Agent access tested against your use cases; legacy warehouse approved for decommissioning.

 
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What Phase 1 hands you

A signed scope, a completion date and a fee, tied to the agents you plan to run.

Build doesn't start until every part of it is in writing.

See how we work

What Have Mactores' Data Modernization Projects Delivered?

These are four engagements with named customers and public write-ups. They cover data lakes, a pricing platform and factory data rather than a single warehouse product, and each shows something an AI-ready warehouse needs.

None of these was a warehouse-only project, and we don't present them as one. Together they show the habits an AI-ready warehouse depends on: traceability built in, integration automated, and cutovers planned around the people and processes that can't stop. Complete write-ups are on mactores.com/stories.

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References

Want to speak with a customer whose starting point matches yours?

We arrange reference conversations, and detailed figures are available under NDA when we reach contract stage.

Request a reference

How Can You Audit What an AI Agent Pulled From Your Data Warehouse?

Once an agent's output reaches a regulator, a patient record or a financial statement, the real question is who can prove what the agent saw. A modernized warehouse answers with three records: lineage from source to column, query logs showing exactly what each agent identity retrieved, and signed acceptance evidence from every phase of the migration.

Framework
What the warehouse has to show
SOX Section 404
That financial figures drawn from the warehouse, including any an agent assembles, trace to controlled source data
SEC and FINRA recordkeeping
That required records are retained, retrievable and unaltered after migration
FFIEC guidance
That data feeding examined banking processes is governed, traceable and access-controlled
HIPAA

That PHI access, by people or agents, is limited to the minimum necessary and logged

PCI DSS

That cardholder data in analytics is masked, tokenized or excluded, and never surfaced through an agent

GDPR
That personal data of EU and UK residents can be located, restricted in use and erased on request

Agents get their own identities in the warehouse, never a shared service login, so every query they run is attributable in the query history. For banks reporting under the BCBS 239 principles, the same lineage record doubles as evidence that risk data can be traced from source to report.

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Audit scope

Share what your auditors asked for last cycle. We'll show where the answers will live.

Each framework maps to an artifact and a phase exit before build begins.

Talk through your audit scope

What Should Each Industry Prioritize When Modernizing a Data Warehouse for AI?

The contract is identical across sectors; the first use case and the first risk are not.

Financial Services

Start where agents will touch regulated numbers: finance, risk and regulatory reporting marts. Definitions are agreed with finance and compliance before any agent reads them, and lineage runs back to general ledger and core banking sources.

Explore Financial services

Healthcare & Life Sciences

Start with the access model. Claims, clinical and research data get column-level controls and de-identified views, so an agent sees only what its task requires, and every PHI query is logged.

Explore Healthcare

Internet & Software

Start with product and customer data an agent can act on, such as churn signals or usage anomalies. Parallel runs are scheduled around your release calendar, and zero-ETL integrations with Amazon Aurora, Amazon RDS or DynamoDB can retire brittle nightly jobs.

Explore Internet & Software

Manufacturing

Start with the data operations already trusts: MES, quality and maintenance records. OT and SCADA-adjacent feeds are profiled early, and plant leadership signs off cutover windows with the reconciliation results in hand.

Explore Manufacturing

Telco, Media, Entertainment, Gaming, and Sports (TMEGS)

Start with freshness. Audience and event data lose value within minutes, so streaming ingestion is sized for peak nights, and no cutover is scheduled near a launch or live event.

Explore TMEGS

The delivery model stays the same in every vertical. What changes is emphasis: governance for regulated data, continuity for operational systems, speed for product-led teams.

Start with your sector

Tell us the first agent you want in production. We'll scope backwards from it.

Forecasting, fraud flags, clinical summaries, maintenance alerts or audience insights.

See all verticals

What Does Data Warehouse Modernization Cost, and Who Carries the Overrun Risk?

About these numbers

This section explains how the fee is built. It is not a quote, and any figures here are directional. Your fixed fee is confirmed in the signed SOW once scoping ends.

Fee driver
Why it moves the price
Transformation logic
Business rules buried in stored procedures and ETL take longer to extract, define and test than straightforward table copies
Data quality
Duplicate keys, conflicting definitions and missing history must be fixed before an agent can rely on them
Consumers and use cases
More reports, pipelines and planned agents mean more interfaces to re-point, describe and test
Governance depth
Regulated data adds lineage detail, access design and evidence steps
Cutover constraints
Narrow downtime windows and many dependent systems call for more staging and rehearsal

Terabytes are a weak predictor. A small warehouse carrying years of undocumented business logic usually costs more to make AI-ready than a large, clean one. From signature onward, Mactores pays for delays it causes. Delays that originate with the customer, such as late access, pending decisions or scope changes, move to the time-and-materials terms spelled out in the SOW.

How can an existing IT budget pay for AI-ready data?

Look at what the legacy warehouse costs just to stand still. Licence and support renew every year, appliance hardware ages toward replacement, batch capacity is sized for peaks that arrive a few days a month, and a stored-procedure layer eats specialist time simply to stay running. Retiring those lines is how most programs pay for the move. Qualifying workloads may also receive AWS Migration Acceleration Program (MAP) funding; eligibility is AWS's call, and we assess it in Phase 1.

What the legacy warehouse costs today

  • Licence and support renewals
  • Hardware nearing replacement
  • Batch capacity sized for occasional peaks
  • Specialist hours keeping old procedures alive

What that budget funds next

  • Modernization, definitions and lineage
  • Agent access design and testing
  • The first production agent use case

One honest caveat: savings arrive when the old system is switched off, not when the new one goes live. Plans that keep both running "for safety" pay twice. That is why decommissioning is a phase with its own exit criteria rather than an afterthought.

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Your numbers

Bring the renewal notice and the AI business case. We'll put a fixed fee against both.

A 30-minute working session led by the FDE who would scope your warehouse.

Book a scoping call

What Is AI-Ready Data? Key Data Warehouse Modernization Terms

Working definitions for the terms used across this page.

AI-ready data
Data an AI system can use without a person double-checking it: documented in business terms, traceable to source, validated against known results, refreshed on a stated schedule, and governed by access rules that apply to agents just as they apply to people.
Agent-native
Mactores' operating model, in which agents do most engagement work by design. It is the structural reason fixed-fee, fixed-date delivery holds up commercially.
Forward-deployed engineer (FDE)
A Mactores specialist who works inside your team, makes the architecture and cutover calls, and answers personally for the delivery date. FDEs have shipped agentic AI systems to production on AWS.
Semantic layer
Business definitions, such as what "active customer" or "net revenue" means, attached to warehouse tables and metrics so agents and people interpret them identically.
Data lineage
The recorded path a value takes from its source system through each transformation to wherever it is used.
Freshness target

The maximum acceptable age of data in a table or data product, monitored so breaches are flagged before anyone relies on stale results.

Data product
A curated, owned and documented dataset built for a defined set of consumers, agents included.
Lakehouse
An architecture that keeps data in open table formats such as Apache Iceberg on Amazon S3 while supporting warehouse-style SQL and governance.
Model Context Protocol (MCP)
An open standard that lets AI agents discover and call tools and data sources through one consistent interface. AWS publishes an open-source MCP server for Amazon Redshift.
Row- and column-level security
Warehouse controls that restrict which records and fields a given user or agent identity can see.
Parallel run
Operating the legacy and modernized warehouses side by side on the same data until their results reconcile.
Staged cutover
Moving consumers to the new warehouse in planned groups with rollback conditions, instead of all at once.
Fixed-date, fixed-fee
Commercial terms that set the completion date and total price in the SOW, with Mactores funding any delay it causes.
Legacy retirement
Decommissioning the old warehouse, along with its licences, support and hardware, once the new one is proven.

Contract language

The fixed-date clause, word for word.

See exactly what Mactores owes you if the date moves.

Read the commitment

Which AWS Credentials Should a Data Warehouse Modernization Partner Have?

Prioritize credentials that AWS audits itself, since they are checked against delivered work instead of self-reported. On that measure, Mactores holds Premier Tier status in the AWS Partner Network, the top level for services firms, and its specializations include Agentic AI, alongside a Generative AI Services Competency.

Among its seven consulting competencies, three bear directly on warehouse work: Data and Analytics, Machine Learning, and Migration and Modernization. Seven of its 17 AWS Service Validations map to the services a modernization runs on: Amazon Redshift, AWS Glue, AWS Database Migration Service, Amazon EMR, Amazon Kinesis, Amazon MSK and Amazon QuickSight.

Mactores has delivered on AWS since 2008. You can confirm every credential above in AWS's partner directory without having to take our word for it.

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Agentic AI
Agentic AI Specialization
Partner tier
Premier Tier Services Partner
Key validation for this work
Amazon Redshift Delivery
Validations
17 AWS Service Validations
Team
200+ AWS-certified FDEs
On AWS
Since 2008

The Agentic AI Specialization is named in words because we hold the credential and not the badge artwork. We would rather name a credential you can verify than display a mark we cannot source. Full detail is on the partners page.

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Verify independently

Check the tier, competencies and validations in AWS's own directory.

Search for Mactores in AWS Partner Solutions Finder, or begin with the partners section of our site.

See the AWS partnership

What Do Teams Ask Most About Modernizing a Data Warehouse for AI?

What is data warehouse modernization?
It is rebuilding an existing warehouse on a current platform so it performs, scales and is governed for today's uses, which increasingly include AI agents. On AWS, that usually means moving to Amazon Redshift or an S3-based lakehouse, rebuilding pipelines, documenting business definitions and lineage, and retiring the legacy system.
What makes a data warehouse ready for AI agents?
Five things: business definitions an agent can read, lineage to source, validated accuracy, a stated freshness target, and access rules that apply to agent identities. Without them, an agent returns confident answers nobody can verify.
Should we modernize the warehouse before building AI agents?
Yes, or at least in step with them. Piloting agents on an unmodernized warehouse mostly proves the data's weaknesses. A practical route is to modernize the domains your first agent use cases need, prove them in production, then extend.
How long does data warehouse modernization take?
Across every Mactores engagement, the median from kickoff to production is 12 weeks. Your own date is shaped by how much transformation logic you carry, data quality, the number of consumers and regulatory scope, and scoping converts those into a SOW date.
Who pays if a modernization project runs late?
Mactores does, whenever the cause is on our side. The completion date and total fee are agreed in the SOW before build begins; delays that originate with you, such as access, decisions or changes after signature, move to the SOW's time-and-materials terms, and the SOW sets out how the cause of a delay is decided.
What if our legacy warehouse is messier than expected?
Scoping exists to find that out before the fee is fixed. Agents profile data quality and read the actual transformation code rather than relying on documentation. If something truly undiscoverable appears later, the SOW's rules for assigning delays settle who carries it, and Mactores' own estimating errors are never passed on to you.
Should we choose Amazon Redshift or a lakehouse on Amazon S3?
Choose on workload, not fashion. Redshift suits governed, high-concurrency SQL analytics; an Iceberg lakehouse on S3 suits open formats, very large raw data and multiple engines. Many AWS estates run both, with Redshift querying lakehouse tables directly, and FDEs recommend the mix in Phase 1.
How will AI agents query the modernized warehouse?
Through governed interfaces, never superuser access. Common AWS patterns include Amazon Bedrock Knowledge Bases structured data retrieval, which turns natural-language questions into SQL, and the Amazon Redshift MCP server for agent frameworks built on the Model Context Protocol. Each agent gets its own identity, scoped permissions and query limits.
Do you replace Snowflake or Databricks?
Not by default. If either is already part of your stack, the target design accounts for it, and the choice to consolidate or coexist is made on cost and workload evidence in Phase 1.
How do you keep AI agent access compliant with HIPAA or SOX?
By treating agents as accountable users: individual identities, least-privilege roles, row- and column-level security, masking for sensitive fields, and query logs retained for audit. Controls are tested against your use cases before any agent is switched on.
What separates this from how traditional data consultancies work?
Traditional engagements bill discovery, mapping and testing by the hour. At Mactores, agents carry 60–70% of that effort, which allows a fixed fee and makes the date enforceable. The same FDEs, not rotating analysts, stay accountable from scoping to decommissioning.
What happens to the legacy warehouse afterward?
Decommissioning follows once the new warehouse has carried production and reconciled, including its licences, support contracts and hardware. Decommissioning has its own exit criteria, because that is where the savings come from.
What information should we prepare for scoping?
A short note on your current warehouse platform, rough data volume, the tools that load and read it, the first agent use cases you are weighing, and any deadline such as a renewal. Read-only access to metadata and query history follows in Phase 1.
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Ask the FDE

Have a question specific to your warehouse? Ask the person who would answer for the date.

Thirty minutes, your questions, no sales deck.

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Point Us at the Warehouse Your First AI Agent Will Depend On

Bring the platform you run today, the agent use case you want next, and any date that matters, whether that is a renewal, a board commitment or a pilot waiting on data. The FDE who would own delivery will tell you what "ready" requires and whether your date holds.
Book a scoping call Email lets-talk@mactores.com
  1. 01

    Share your current platform, rough size, first agent use case and deadline. No RFP required.

  2. 02

    Once fit is confirmed, you get a session with the FDE accountable for your delivery date.

  3. 03

    A scoped proposal, fixed date and fixed fee included, follows within five business days.