The constraint

It is a data problem,
not an AI problem

Most AI initiatives do not stall on model selection or platform choice. They stall underneath, on duplicate records, ungoverned master data, disconnected pipelines, and ownership gaps nobody has been accountable for. The organization has an AI mandate and a fragmented data estate, and the gap between them is where budgets are lost.

The fix is not a year-long cleanup before AI can begin.

It is a prioritized, engineered foundation built in parallel with activation, so value shows up in weeks.

Platforms we specialize in

Built on the platforms
your enterprise already runs

Our engineers hold depth across the systems where enterprise data actually lives, and we
architect for the estate you have rather than the one a single vendor would prefer you had.

Business applications

Data 360 · Agentforce 360 · MuleSoft · Marketing Cloud · Revenue Cloud · SuiteCloud

Data platforms and MDM

IDMC · MDM · lakehouse and warehouse architectures

Cloud and runtime

Public cloud, container runtime, and accelerated compute

Integration and pipeline

Native API · change data capture · streaming

Model layer

Multi-model orchestration, no lock-in

Where we go deepest

Two practices built on data depth

35 - 50%

Faster time to first AI deployment

40 - 60%

Reduction in failed AI project rates

2 - 3x

Higher ROI on AI investments

Salesforce

Practice depth across agents, data foundation and governance

Agentforce, Data 360 & MDM, Agent Fabric (MuleSoft) & Informatica CDGC. The three platforms at the centre of the most complex enterprise engagements, run by one team rather than three vendors.

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Oracle NetSuite

Connect the finance data, then put agents on it

Finance data is the highest-consequence data in the enterprise. We connect it, consolidate it across entities, and then run agents on top. Delivered by a team that understands the close, not just the software.

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Platform and data engineering

The engineering underneath both

The practices above sit on one engineering organization. Same team, same standards,
same architecture discipline, regardless of which platform the data happens to live in.

180+

Engineering and
solution delivery staff

7

Delivery cities across
four regions

15+

OEM partnerships

40+

Delivered successful
engagements

Infrastructure services

The practices above sit on one engineering organization. Same team, same standards

Data and AI services

The practices above sit on one engineering organization. Same team, same standards

Platform services

The practices above sit on one engineering organization. Same team, same standards

Our Method

Agentic engineering, layer by layer

We do not hand estates to a general-purpose model and hope. Every engagement runs a
defined ten-layer sequence, with AI applied where it compounds engineer judgment and
human review gating every phase that changes production behavior.

01
Codebase and estate discovery
Static analysis, dependency mapping, complexity and risk assessment, architecture discovery

02
Context engineering
AI
Domain modeling, repository knowledge graphs, business logic extraction, policy constraints

03
Strategy and sequencing
Static analysis, dependency mapping, complexity and risk assessment, architecture discovery

04
Engineer enablement
Runbooks, guardrails, pull request standards, test baselines, approval gates

05
Refactoring and transformation
Modularization, API extraction, framework upgrades, incremental and reversible

06
Assisted implementation loops
AI
Iterative refactor, review, fix, and pull request cycles with human in the loop

07
Testing, validation, risk control
AI
Test generation, regression validation, coverage uplift, compliance checks

08
Containerization and CI/CD
Container builds, pipelines, security scans, deployment automation

09
Deployment and cutover
Canary releases, blue-green deployments, rollback readiness

10
Operate, observe, iterate
Observability, performance tuning, continuous refactoring
Engineering Proof

Real outcomes we have delivered

Legacy modernization

3 months

25,000 lines re-engineered from monolith to service-based architecture

Canonical model replacing in-memory operations, state machine with pause and resume, migration turnaround cut from hours to minutes.

Global networking and cybersecurity leader

Agentic operations

80%

Reduction in average ticket resolution time

Containerized microservices AI operations engine with intent-based interpretation and closed-loop remediation across transport, security, and application layers.

Global networking and cybersecurity leader

Data and cost intelligence

70%

Reduction in manual cost-reporting effort

Analytical backend with bronze and silver pipeline, normalized multi-provider metrics, granular attribution by model, project, and API key.

Enterprise AI infrastructure operator

How we deliver

Speed to scale

Readiness assessment first. Proof before commitment.
Production in 8 to 12 weeks.

Map the data gaps. Identify the highest-ROI use case. Deliver a prioritized business case with a data estate map and readiness score. No cost, no commitment.

Need the full picture first? Data Audit as a Service is a paid engagement that profiles the estate in depth and delivers a remediation plan scoped to your target use cases.

A working solution against a live use case, on your real data in your environment. Not a sandbox demo.

Full deployment with monitoring and optimization. Measurable KPI delta against the baseline you set in week one.

Why Gruve

We fix the foundation first

Agents fail on data, not on models. We build and certify the trusted layer before anything is built on top of it.

We are platform-fluent, not platform-captive

Deep certified expertise in Salesforce and NetSuite, engineered on an architecture-first approach that spans Informatica, Snowflake, Databricks, and the major clouds. We design for your estate.

Remediation and activation
run in parallel

There is no reason to wait for a full cleanup. We sequence the fixes that unblock the first use case and deliver value in weeks rather than after a year-long program.

One team owns strategy, build,
run, and operate

No vendor handoff gaps. The team that designs the foundation is the team accountable for it in production.

FAQs

Frequently asked questions about
Data & AI

What is an AI data foundation?

The governed, quality-assured, and connected data layer that AI systems depend on to produce trustworthy output. It spans master data, pipelines, quality controls, lineage, and access governance. Without it, agents produce confident answers grounded in unreliable inputs.

What is a data readiness assessment?

A complimentary one to two week review of your data estate against the AI use cases you intend to deploy. It delivers a readiness scorecard and a prioritized activation roadmap. No cost and no commitment to a larger program.

How is Data Audit as a Service different?

DAaaS is a paid four-week engagement. Where the assessment scores readiness and sequences the work, DAaaS profiles the estate in depth and delivers a unified profile blueprint, a governance framework, and a remediation plan scoped to the target platform. Most enterprises start with the assessment and move to DAaaS when the scope is confirmed.

Do we need to fix all our data before starting AI?

No. Full remediation before activation is the most common reason AI programs never ship. We identify which defects actually block the target use case, fix those first, and run broader remediation in parallel with activation.

How is this different from a standard data cleanup project?

Cleanup projects optimize for data hygiene. We optimize for AI readiness, which is a different target. Automation handles inventory and profiling; our data engineers validate findings, resolve identity conflicts, and ground every recommendation in what the target platform requires to go live.

Which platforms do you work across?

Salesforce, Oracle NetSuite, Informatica, Snowflake, Databricks, Microsoft, AWS, Google, MuleSoft, Boomi, and the model providers. We architect for your existing estate rather than steering toward a single vendor stack.

Who benefits most from this?

Enterprises with an active AI mandate and a fragmented data estate, particularly in automotive, manufacturing, high tech, financial services, and regulated industries where data sits across legacy CRM, ERP, and unstructured sources.

How do we get started?

Book a data readiness assessment. We map your estate, score readiness against your intended use cases, and deliver a prioritized roadmap in one to two weeks, at no cost.

Get started

Find out what your
data can actually support

Start with a complimentary data readiness assessment. In one to two weeks you
will have an estate map, a readiness score against your target use cases, and a
prioritized roadmap. No commitment to a larger program. 

    Response within 24 hours · NDA available on request