Resilience Solutions

Data, Analytics & AI

Turn fragmented data into trusted decisions and intelligent action.

We design modern data foundations and intuitive analytics experiences, then move intelligence into the workflows where decisions actually get made.

Discuss a data or AI initiativeNot sure this is the right fit?

The problems this solves

What usually brings people here

If one of these sounds like your situation, this is the right page.

  • Every team reports a different number for the same thing.

    Definitions live in individual reports rather than in a governed semantic layer, so reconciliation consumes more effort than analysis.

  • Our data is not ready for AI.

    Quality, lineage, access control and freshness were never designed for retrieval or model training, so AI initiatives stall at the pilot boundary.

  • We have dashboards nobody uses.

    Reporting was built around what was easy to extract rather than around a decision someone has to make on a specific cadence.

  • We do not know which AI use cases are worth doing.

    A long list of ideas, no consistent way to assess value, feasibility, data readiness and risk, and no criterion for stopping.

Capabilities

What we actually do

  • Data strategy and governance

    Ownership, definitions, quality standards, lineage and the access model that makes data usable and defensible.

  • Data engineering

    Batch and streaming pipelines, ELT, and the operational discipline to keep them running.

  • Lakehouse and warehouse architecture

    Storage, compute and modeling design chosen against real query patterns and cost.

  • Master data and data quality

    Reconciliation, matching, stewardship workflows and quality monitoring with alerting.

  • BI, semantic models and visualization

    A governed semantic layer so a metric means one thing, and reporting built around decisions.

  • Predictive ML and data science

    Forecasting, classification, optimization and recommendation models, with monitoring for drift and calibration.

  • Generative AI and RAG

    Retrieval over governed enterprise knowledge, with citations, permission-awareness and evaluation.

  • AI agents and workflow intelligence

    Agents scoped to a task, with tools, approvals, observability and human oversight designed in.

  • MLOps and LLMOps

    Versioning, evaluation, deployment, monitoring and cost control for models in production.

  • Responsible AI

    Risk classification, testing, human oversight and audit trails appropriate to the use case.

How it fits together

From scattered sources to something a business can act on

Each tier is only as trustworthy as the one beneath it. Most failed AI work is a data-platform problem discovered late, which is why this is the order we build in.

  1. 01

    Sources

    Where the facts already live

    • ERP
    • CRM
    • Operational systems
    • Documents
    • IoT and telemetry
    • Third-party feeds

  2. 02

    Data platform

    Made current, governed and joinable

    • Ingestion
    • Lakehouse or warehouse
    • Streaming
    • Modeling
    • Quality and lineage
    • Access policy

  3. 03

    Intelligence

    Only as good as the tier above it

    • Analytics and BI
    • Machine learning
    • Generative AI
    • Retrieval (RAG)
    • Agents
    • Evaluation

  4. 04

    The business

    Where it has to land to count

    • A decision
    • An automated step
    • A customer experience
    • An operational control

How we approach it

Principles we hold to

  • Start with the decision

    We work backwards from a decision someone has to make, on a cadence, with consequences. That determines the data, the latency and the interface — not the other way round.

  • Governed before clever

    Retrieval, agents and copilots inherit whatever access model and data quality sit underneath them. Foundations first is not conservatism; it is the only way the clever part stays safe.

  • Evaluate continuously

    Models are assessed for accuracy, groundedness, safety, latency and cost on an ongoing basis, not once at launch. Drift is expected and monitored for.

Outcomes

What you should end up with

  • Governed, decision-ready data products and role-based insights.
  • A single agreed definition for the metrics the business runs on.
  • AI use cases selected on value, feasibility and risk rather than on novelty.
  • Production-ready intelligence tied to users, controls and value metrics.

Where this usually starts

A short working session on your situation — what you are trying to change, what is in the way, and whether this is the capability that addresses it.

Discuss a data or AI initiative

Evidence

What changes, and how you would know

Two different things belong here. One is the structural change this capability makes, which we can describe today. The other is a client outcome, which publishes only when a client has approved it in writing with a timeframe and a baseline.

Conflicting reports to a governed foundation
Illustrative transformation model

Two teams presenting different numbers for the same question is rarely a tooling problem. It is a definitions problem wearing a dashboard.

Before · Where the numbers come from
  • Definitions living inside individual reports
  • Four answers to "how many customers do we have"
  • Access granted per extract, and rarely revoked
  • Lineage reconstructed by asking the person who built it
After · Where they come from afterwards
  • One governed semantic layer the reports read from
  • A definition with an owner, a version and a change history
  • Access evaluated against entitlement, not against a spreadsheet
  • Lineage recorded, so a number can be traced to its source

What moved: Arguments move from whose number is right to what to do about it. That is the precondition for anything analytical, and it is where most AI programs stall.

A structural model of the change, not a record of an engagement. No client, figure or timeframe is implied — those publish only against an approved claim.

Client outcomes for Data, Analytics & AI

Proof pending approval

A published outcome here will carry the client's written approval, the timeframe it was measured over and the baseline it was measured against. Until all three exist, this section says so rather than showing a figure you could not check.

Discuss a similar challenge

Where this capability leads

Every connection below is recorded in the content itself, and each one says why it is here.

Explore where this capability fits

Answer four questions about your goal, sector, environment and constraint, and see how this capability sits alongside the others in a path — with the reasoning shown.

Start from a goal

Open the solution finder

Questions

Frequently asked

Where should an organization start with generative AI?
With a use case where the cost of being wrong is low, the value of being right is measurable, and the underlying data is already governed. Internal knowledge retrieval and document-heavy workflows usually meet all three. Starting with a customer-facing use case before the data foundations and evaluation process exist is the most common reason pilots do not reach production.
What does "AI-ready data" actually mean?
Data that is discoverable, permission-aware, quality-monitored, lineage-tracked and fresh enough for the use case. For retrieval-augmented generation it also means content that is chunked and embedded with metadata preserved, so that answers can cite sources and respect the same access controls the source system enforces.
How do you stop an AI assistant from exposing information people should not see?
By making retrieval permission-aware at the point of query rather than filtering results afterwards. The retrieval layer evaluates the requesting user's entitlements against document-level metadata before any content reaches the model. The governing principle is that a user must never be able to retrieve through AI what they could not access directly.

Discuss a data or AI initiative

Tell us what you are trying to change. Someone who does this work reads every request and connects you with the right capability team.

Discuss a data or AI initiative
See all capabilities