Industries · Enterprise

Modernization, without the multi-year sprawl.

Enterprise reporting estates accrete over years and rarely get rebuilt in one. We bring a pragmatic, increment-by-increment approach that ships value every quarter.

Analytics modernization

Replace the legacy without freezing the business.

We migrate legacy reporting estates onto governed, self-service analytics layers, incrementally, with the business never going dark. Report turnaround moves from weeks to hours, and analysts get their time back.

Phased migration with parallel-running
Metric reconciliation and trust building
Decommissioning and cost recovery

AI in the workflow

Embed AI where the work already happens.

The highest-leverage AI deployments are inside the systems your team already lives in, your CRM, ticketing, ERP, content tools, rather than a new chatbot they have to remember to open.

Workflow-embedded LLM assists
Document and email understanding
Knowledge-base retrieval and summarization

Data product operating model

Stand up a data product team that lasts.

We help enterprise data teams adopt a data-product operating model: cross-functional pods, clear ownership, service-level expectations, and a roadmap the business actually consumes from.

Operating model and team design
Roadmapping and intake processes
SLAs, observability and incident response

Where we work

Engagements across the enterprise stack.

From the executive cockpit to the data platform underneath it, we work across the full enterprise analytics estate, usually starting where the pain is most acute.

Executive analytics

CEO and board dashboards
Operating-committee cockpits
Business-unit briefings
Mobile-first leadership views

Function-specific analytics

Finance and FP&A analytics
Supply chain and operations
Marketing and growth
People and workforce

Workflow AI

CRM-embedded assists
Inbox triage and drafting
Document and contract analysis
Knowledge retrieval

Reporting modernization

Legacy estate inventory
Phased migration plans
Semantic layer build-out
Decommissioning and cost recovery

Data platform

Snowflake, BigQuery, Databricks
dbt and Airflow / Dagster
Data contracts between teams
Lineage and observability

Operating model

Data-product team design
Intake and roadmap processes
SLAs and on-call rotations
Center-of-excellence stand-up

A typical modernization arc

The shape of an enterprise modernization.

No two engagements run identically, but enterprise analytics modernizations tend to follow a recognizable arc, not a Big Bang, but a sequence of confidence-building increments.

01
Inventory & decision mapping (weeks 1–3)
We catalog the active reporting estate and map it against the actual decisions leadership makes. The result is usually that 60–70% of the legacy reports have no decision attached.
02
Foundation stand-up (weeks 3–8)
Modern data platform layered alongside the legacy estate. Semantic layer for the small set of metrics that actually matter. Pipelines for the source systems that feed them.
03
First wave of migration (weeks 8–16)
The highest-priority reports rebuilt on the new platform, parallel-running with the legacy versions until trust transfers. Metric definitions reconciled and documented.
04
Decommissioning & cost recovery (weeks 16–24)
Legacy reports retired in waves. License and infrastructure costs recovered. The analyst team gets material time back.
05
Embedded enablement & expansion (ongoing)
Pattern libraries, training and an embedded enablement function so future analytics work follows the same standards, without us in the room.

Case snapshot

How it plays out, in practice.

A representative engagement, described in the structure of challenge, approach and outcome. Specifics changed to preserve client confidentiality.

Enterprise

Reporting Estate Modernization

Challenge

A multi-business-unit enterprise was running its reporting on a legacy on-prem BI estate. Report turnaround was measured in weeks, the analyst team was firefighting, and trust in the numbers was eroding.

Approach

Cataloged the active reports and mapped them to actual leadership decisions
Stood up Snowflake + dbt + Looker alongside the legacy estate
Migrated active reports in priority order, parallel-running for confidence
Decommissioned legacy reports only after each replacement was verified

Outcome

Report turnaround dropped from weeks to hours. Trust in the numbers was rebuilt through documented metric definitions. Analysts recovered enough time to start shipping the deeper analysis they\u2019d been deferring for two years.

Frequently asked

Questions we hear, answered honestly.

Do you do big-bang replatforming?

We strongly recommend against it. Big-bang migrations fail far more often than they succeed and they freeze the business for the duration. Every enterprise modernization we’ve done has been incremental, with old and new estates parallel-running until trust transfers.

Will you work alongside our existing SI partner?

Often, yes. We’ve been productive in environments with established SI partners, platform vendors and in-house teams. We’ll be explicit upfront about what we own and what we don’t, and we won’t silently expand scope.

How do you handle change management?

We treat enablement as part of the deliverable. Training, pattern libraries, documentation and a ninety-day adoption review are standard. We’d rather ship less and have it adopted than ship more and watch it sit unused.

Can you stand up a data-product team for us?

Yes, operating model, roles, intake process, SLAs, on-call. We pair the design with a few months of embedded operation so the model is tested under load before we step back.

Continue exploring