The Cost of Leaving Data Work Disconnected

Most data teams work across disconnected tools, and much of their week goes to manual cleanup, discovery, and report maintenance instead of insight. When that fragmented work continues, the cost builds quietly and shows up across the business.

Untrustworthy Decisions

When teams can't trust the numbers, every decision carries hidden risk, and leaders hesitate to act on the insight in front of them.

Wasted Effort on Unused Reports

Hundreds of redundant, outdated reports consume engineering hours while no one has the time or visibility to decide which ones to retire.

Rising Cloud Costs

Unused storage, oversized warehouses, and inefficient queries inflate cloud bills with little visibility into where the money goes.

Slower Time to Insight

Fragmented, manual processes delay answers, and opportunities pass to faster-moving competitors.

Growing Technical Debt

Disconnected tools and tribal knowledge pile up and make every future change more difficult, risky, and expensive.

One Workspace for Your Entire Data Lifecycle

You're accountable for whether or not the business can trust its data and for a cloud bill that keeps growing. MPH for Data distills 20 years of enterprise data transformation experience into repeatable, AI-powered accelerators. Your team sees what to fix, consolidate, and modernize in one place and then acts on it.

Watch a two-minute overview of MPH for Data.

Built for Your Existing Stack

MPH for Data layers intelligence on top of the platforms you already run, such as Snowflake and Databricks, which protects prior investments.

No-to-Low-Code Accelerators

Proven accelerators compress work that once took specialists weeks, and teams reach value twice as fast as they would with traditional manual delivery.

Baselines in Hours

Your leaders receive measurable baselines and prioritized recommendations in hours instead of months.

People in Control

AI agents never make changes silently. Low-confidence outputs go to a person for review, cutting manual effort while your team keeps control.

Three Steps to a Clear Starting Point

Your path to MPH for Data starts with a conversation about your environment and ends with an objective view of where you stand.

1

Talk to a data expert

Book a personalized walkthrough with our team. The session focuses on the accelerators most relevant to your environment and priorities and leaves room for your questions throughout.

2

See your own scenarios

The demo runs on the platforms you already use, such as Snowflake and Databricks. You see exactly how each accelerator would work with your own data.

3

Leave with a baseline

The maturity assessment gives you an objective, benchmarked view of where you stand and which priorities return value fastest.

Talk to a data expert

How MPH for Data Helps You Move Faster

The DASH framework describes how your team moves from first analysis to confident action. AI-powered accelerators support every stage of the data lifecycle.

D

Discover

Surface gaps, overspend, duplicated effort, and more with AI-powered analyses.

A

Authenticate

Confirm that your data is trustworthy in every context where it's used.

S

Synthesize

Bring your entire data ecosystem together in one intelligent platform.

H

Harness

Act on insight and make approved changes with the click of a button.

Compliance as Infrastructure, Not an Afterthought

Every accelerator includes the AI guardrail layer as a condition of deployment, not an add-on.

Agent policy

Each model has defined boundaries, enforced across every accelerator. No model acts outside its mandate.

Assurance

Audit trails, bias reports, and model-performance logs produce an examiner-ready record of every decision.

Human responsibility

Every autonomous domain has a named, accountable owner, and human override is available at every decision.

The suite is designed to support your compliance program across ECOA and Regulation B, HMDA, the Fair Housing Act, the Fair Credit Reporting Act, and many more.

‍Ask for the full regulatory coverage map in your demo.

What the Research Says Happens Next

Independent research on AI in lending points consistently in one direction.

20-60%

Credit-analyst productivity gains and roughly 30% faster decisions in underwriting from orchestrated multi-agent systems (McKinsey, December 2024)

75%

Increase in completed pre-applications when an AI virtual assistant guides the borrower (BCG, 2025)

30%

Increase in profits for a typical retail bank implementing AI at scale compared with non-AI scenarios (BCG, November 2025)

1.9x

Higher revenue growth at AI-leader banks than at laggards, already measured in 2024 (BCG, 2025)

Marlabs - A Partner, Not Just a Vendor

The technology is half the story. What makes AI deliver in lending is who builds it.

From Discovery to Live

Discovery & assessment

Weeks 1 to 6

Review of your loan origination system, core, and data landscape.

Solution design

Weeks 6 to 14

Accelerators configured to your workflows, data, and compliance posture.

Integration

Weeks 14 to 26

DataBridge connects to the core; accelerators go into your environment.

Testing & go-live

Weeks 26 to 32

Validation, user acceptance, and production release.

Managed operations

Month 7 onward

Monitoring, retraining, and expansion to the next accelerator.

Value arrives in phases, not at the end. Quick wins in the first phase fund and justify the next one.

FAQs

Do we have to replace our core system?

No. Your core remains the system of record throughout. The suite connects through secure APIs and writes results back so that nothing needs to be migrated.

Which core and origination systems do you support?

We support FIS, Fiserv, Jack Henry, Encompass, and Salesforce today and have plans to build additional connectors as engagements require them.

How long before we see something working?

Discovery runs 1 to 6 weeks depending on the complexity of your business needs. The first capability typically reaches production inside the first two quarters with value delivered throughout each phase rather than at the end.

How do you handle fair lending and adverse-action requirements?

We build explainability into the decision rather than add it afterward. Our Risk-Right accelerator produces reason codes designed for CFPB adverse-action notices under ECOA and Regulation B, and continuous bias testing runs across protected classes both before launch and periodically after.

Can we start with one accelerator?

Yes. Each accelerator owns a stage of the lifecycle and delivers value on its own. Most engagements start with the stage where the cost is most visible and then expand from there.

Ready to Move from Pilot to P&L?

Start with a focused discovery session. We map your current lending stack, pinpoint the highest-impact use cases across the accelerators, and design a phase 1 engagement scoped to your institution.

Not ready to book just yet? Check out our free whitepaper “Beyond Optimization: Transforming Business Workflows with Agentic AI” in the meantime.

Book a Demo

Read the Whitepaper