aiDataWorks

Most enterprise data programs stall.

It happens for the same reasons every time, and AI makes each one worse.

27%

Fragmented estates

The average enterprise runs 957 applications. Just 27% of them are connected to one another.

MuleSoft Connectivity Benchmark, 2025

39%

Brittle integrations

Share of IT team time spent building and maintaining custom integrations instead of new work.

MuleSoft Connectivity Benchmark, 2025

$12.9M

Rising cost

Average annual cost of poor data quality to a single organization, before AI adds load.

Gartner, 2025

63%

Governance risk

Lack - or are unsure they have - the data management practices that AI actually requires.

Gartner, 2025

Gartner expects 60% of AI projects unsupported by AI-ready data to be abandoned through 2026. We modernize the foundation, govern it, and build the AI that runs on it - one team, from assessment to production.

From fragmented estate to AI in production

Four phases, one foundation. Assess what's fragmented, modernize what's brittle, govern cost and risk, and ship AI to production, not a pilot.

01 · Assess

Assess

You get a clear picture of what's actually running, what's fragile, and what it will cost to fix - before you commit budget.

What we do

Map every system, integration, and dependency across the estate
Score AI and data readiness against where you actually need to get to
Build the business case and sequence the work, phase by phase

You walk away with

A costed, sequenced roadmap your leadership can approve in one meeting.

02 · Modernize

Modernize

Your estate stops being hundreds of disconnected systems and becomes one platform your team can actually operate.

What we do

Migrate legacy integration platforms to the cloud without breaking what's running today
Consolidate duplicate and overlapping systems into a single source of truth
Rebuild brittle custom integrations on frameworks we maintain, not you

You walk away with

A single, cloud-native estate - cut over without disrupting the business it runs.

03 · Govern

Govern

Every dataset gets an owner, a quality rule, and a trail - so trust in your data stops being a matter of opinion.

What we do

Establish one master record for every core entity - customer, product, account
Set data quality rules that run continuously, not once a quarter
Build the lineage and audit trail your compliance team can produce on request

You walk away with

Documented, audit-ready governance across the estate - evidence, not assurances.

04 · Activate

Activate

The foundation stops being infrastructure and starts being the thing your business runs on - live, in production.

What we do

Build the analytics, dashboards, and self-service tools your teams have been asking for
Design and ship the LLM and agentic use cases your data can now support
Monitor, tune, and transfer knowledge so the work keeps running without us

You walk away with

Production analytics and agents - not a pilot, not a slide deck.

Agents are easy to launch and hard to run

Every use case we build gets an owner, a risk tier, and a review cycle before it ships. That is what keeps AI in production instead of stuck in pilot.

Use-case inventory and risk tiering

Every AI use case is catalogued and scored for risk before it gets built, so exposure is known up front rather than discovered after launch.

Decision stewardship

Named owners, approval gates, and escalation paths - so every decision a model makes has a person accountable for it.

Lifecycle management

Each use case runs the same cycle - build, manage, upgrade - so nothing ships once and is quietly forgotten.

Adoption and change management

Literacy and ownership programs that make governance something your teams actually use, rather than a document they route around.

Continuous oversight

Agents are monitored for drift against original intent, and stay audit-ready at every checkpoint - not only on launch day.

The integration stack we build on

Backbone of our data integration, governance, quality, MDM, and automation solutions.

Customer 360 / Data 360 insights, real-time data exchange across sales, marketing, and service.

Data security and compliance tooling extending our governance and privacy practice.

EHR implementation and integration, including EPIC AWS Informatica integration for healthcare data interoperability.

AI/ML engineering, lakehouse architecture, high-scale analytics pipelines.

Cloud data warehouse and data lake engineering, cost governance, analytics modernization.

Cloud-native data lakes, pipelines, and AI workloads on Amazon Web Services.

Cloud-native data lakes, pipelines, and AI workloads on Microsoft Azure.

Cloud-native data lakes, pipelines, and AI workloads on Google Cloud.

API-led integration and connectivity across systems, the backbone of our real-time data exchange work.

Enterprise AI agents and copilots built on Anthropic's Claude models, grounded in governed data.

LLM-powered automation and copilot experiences built on OpenAI's models.

Real-time, context-aware AI capabilities powered by xAI's Grok models.

AI-powered search and retrieval layered on top of governed enterprise data.

ERP and cloud applications integration, extending governed pipelines into finance and operations.

What working with us looks like

Four recent engagements, told in full - not folded into an aggregate stat.

Healthcare

100%

Applications migrated off unsupported Windows Server 2012

A Tier 1 healthcare system's applications were running on end-of-life Windows Server 2012, with passwords stored directly in server files and PHI/PII surfacing in application logs. aiDataWorks moved every application to SDK-based, PAM-secured credentials on Windows Server 2022, migrating 100% of in-scope applications off unsupported infrastructure and closing the logging exposure for good.

Read the full case study →

Insurance

Full Lineage

Across every legacy mainframe COBOL copybook

A tier 1 health insurer's Informatica Data Catalog had never been configured or smoke-tested, leaving decades of mainframe COBOL data feeding a modern Hadoop landing zone with zero governance or lineage. aiDataWorks built custom COBOL copybook scanners and brought the catalog fully live, taking lineage visibility from zero to complete coverage with automated discovery replacing manual documentation.

Read the full case study →

Healthcare

Faster root-cause resolution on recurring file failures

A tier 1 healthcare customer's Informatica, B2B, and cloud data stack needed steady hands after go-live, not another project team. aiDataWorks' L2/L3 managed services cut root-cause time on a recurring file-ordering issue from eight hours to one, reclaimed roughly 100 GB through archive cleanup, and lifted B2B processing performance by an estimated 25% through ongoing error-event cleanup.

Read the full case study →

Manufacturing

$1-2M

Estimated annual revenue unlocked, per factory

A global semiconductor manufacturer's shop-floor IIoT data sat disconnected from sensitive product and pricing metadata, leaving operators without the context to act on real-time signals. aiDataWorks joined GEMBO's IIoT edge platform with Informatica's data security stack to deliver a masked, secured pipeline into a private cloud data warehouse, unlocking an estimated $1-2 million per factory, per year in secured, real-time data access.

Read the full case study →

Why teams switch from a legacy SI

The difference isn't effort. It's how the work is structured.

Traditional SI aiDataWorks

Engagement model Traditional SIStaff aug - you manage the team aiDataWorksManaged service, one accountable team

Governance Traditional SIBolted on after go-live aiDataWorksBuilt in from day one

Time to value Traditional SIMulti-year transformation program aiDataWorksPhased, working software every phase

Data lineage & ownership Traditional SITribal knowledge, undocumented aiDataWorksFull lineage, named owners

AI readiness Traditional SIAI bolted onto ungoverned data aiDataWorksAI built on governed data

After go-live Traditional SIHands off once deployed aiDataWorksOngoing DMaaS - monitored & tuned

See how we work →

What Experts Are Saying about aiDataWorks

“The reporting cycle used to eat three weeks out of every month. Now it’s a scheduled job we barely think about, and for the first time our risk team trusts the number on the screen.”

VP of Risk & Compliance

Microfinance Lender, 3.1M Borrowers

“Our managed services team turned an eight-hour root-cause slog into something we solve in about an hour now, and reclaimed 100 GB of storage along the way.”

IT Operations Manager

Tier 1 Healthcare Customer

“Unblocking our Big Data Engineering upgrade didn’t just get the migration moving again. It cut real cost out of our AWS bill and opened up work we couldn’t touch before.”

VP of Data Engineering

Tier 1 Retailer

“We improved OEE by 30 to 40 percent without ripping out our existing shop-floor equipment, just by finally getting clean, unified data out of it.”

Plant Operations Director

Semiconductor Manufacturer

Where we do our best work

Healthcare

KPI-powered dashboards for diagnosis, clinical data, patient care quality, and business management.

Financial Services

Custom manager diagnostic dashboards for deeper market insights and portfolio management, plus BI advisory.

Insurance

Sales performance dashboards collecting data from multiple sources with real-time, interactive analytics.

Manufacturing

Data Science and Big Data-powered solutions for price optimization, demand forecasting, preventive maintenance, and fault prediction.

Retail & CPG

Recommendation engines tracking cart abandonment, purchase behavior, and cross-selling opportunities.

Public Sector

Modernized case-management and constituent-data platforms so agencies see one citizen record across departments.

CASE STUDY

Modernizing Application Credentials Off Windows Server 2012 for a Tier 1 Healthcare System

CASE STUDY

Governing Legacy Mainframe COBOL Data for a Tier 1 Health Insurer

CASE STUDY

Post-Go-Live Managed Services for a Tier 1 Healthcare Informatica Estate

CASE STUDY

Securing Shop-Floor Data in the Cloud for a Global Semiconductor Manufacturer

Questions, answered

How is aiDataWorks different from a typical Informatica reseller?

We're a Platinum Informatica partner and Informatica Migration Factory partner, but we deliver full lifecycle outcomes - assess, modernize, govern, and activate - not just licensing. Most engagements run as DMaaS, an outcome-based managed service, not time-and-materials staffing.

What does the DMaaS engagement model actually include?

Data Management as a Service is priced to business results and SLAs, not billable hours or headcount. You get flexible, on-demand expert teams and continuous run-monitor-optimize delivery, so your data platform keeps improving after go-live instead of sitting with whoever built it.

How long does a typical modernization or migration engagement take?

It depends on scope, but most engagements start with a framework-based proof of concept to validate feasibility and timeline before a full commitment.

How do you handle data security and compliance during migration?

Governance, quality, and security controls are embedded from the Assess phase onward - audit-ready compliance frameworks and policy/privacy controls are part of the Govern step of every engagement.

Do you work with organizations that aren't yet on Informatica?

Yes - we also work across Databricks, Snowflake, Salesforce, AWS, Azure, and Google Cloud, and can advise on the right platform mix before recommending Informatica specifically.

Do you offer AI capabilities beyond data management?

Yes - our Agentic AI Copilot for Informatica & Snowflake, custom LLM and agent-flow development, and purpose-built platforms like our GEM AI Manufacturing Copilot bring AI directly into daily operations, not just into the data layer.

aiDataWorks Becomes a Certified Salesforce & MuleSoft Partner

Trusted by enterprise teams

Most enterprise data programs stall.

Fragmented estates

Brittle integrations

Rising cost

Governance risk

From fragmented estate to AI in production

Assess

Modernize

Govern

Activate

Agents are easy to launch and hard to run

Use-case inventory and risk tiering

Decision stewardship

Lifecycle management

Adoption and change management

Continuous oversight

The integration stack we build on

What working with us looks like

Why teams switch from a legacy SI

What Experts Are Saying about aiDataWorks