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AURTIV SERVICES / 8 SPECIALIST CAPABILITIES

Trusted information. Better-informed decisions.

Connect reliable pipelines and shared measures to analytics and machine learning. Choose the decision first, then build the data and validation needed to support it.

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A connected engagement

Choose a starting point.
Connect what comes next.

Connect reliable pipelines and shared measures to analytics and machine learning. Choose the decision first, then build the data and validation needed to support it. The groups below show complementary capabilities. Choose a focused assignment or combine them into a scoped delivery plan.

01 / CONNECTED CAPABILITIES

Prepare the foundation

Data & Intelligence / 03

Data Engineering

ETL and ELT pipelines that connect source systems to usable, governed datasets.

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Create data flows people can rely on.

Data engineering prepares and moves information from operational sources into useful datasets. The work includes meaning, quality and ownership as well as connectivity. We design pipelines around the required freshness, historical coverage and recovery needs, so downstream users can understand what a dataset contains and when it is safe to use.

IN PRACTICE

Create a consistent operational dataset

Illustrative engagement

01 / THE STARTING POINT

The challenge

Reporting teams repeatedly extract data from several systems and reconcile incompatible identifiers by hand.

02 / THE WORK

Our approach

Define source contracts, map identifiers and implement validated transformations. Add freshness checks, lineage and a recoverable backfill process, with named owners for source-quality issues.

03 / THE DIFFERENCE

How it helps

Analysts can spend less time recreating data preparation. The benefit depends on stable definitions and a visible response when a pipeline fails.

Explore delivery guidance & success measures

Build it thoughtfully

  • Handle late records, schema changes and reruns explicitly.
  • Publish ownership and quality expectations with the dataset.

Know what good looks like

  • Data reliability: freshness, completeness and unresolved quality incidents.
  • Consumer effort: repeated preparation and reconciliation needed before analysis.

Should every source be updated in real time?

No. Refresh frequency should match the decision. Batch processing can be simpler and less expensive where immediate updates do not change the outcome.

Data & Intelligence / 07

Data Platform Modernisation

Upgrade analytics platforms and data architectures without losing sight of continuity and ownership.

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Improve the foundation without interrupting the decisions.

Data-platform modernisation addresses constraints in storage, transformation, governance or access. We map the reports and applications that depend on the existing platform before choosing a transition path. The objective is a platform the team can operate and evolve, with preserved business definitions and a deliberate retirement plan.

IN PRACTICE

Replace a fragile reporting platform

Illustrative engagement

01 / THE STARTING POINT

The challenge

An ageing environment has difficult refreshes and tightly coupled transformations used by many reports.

02 / THE WORK

Our approach

Inventory dependencies, select a bounded data domain and migrate its pipeline and model. Run reconciliation in parallel, validate consumer reports and retire the old path after agreed acceptance.

03 / THE DIFFERENCE

How it helps

The team can improve changeability while protecting reporting continuity. Transition effort and duplicated operating costs remain visible.

Explore delivery guidance & success measures

Build it thoughtfully

  • Migrate by meaningful data domains with identifiable consumers.
  • Agree reconciliation and retirement criteria before running old and new systems together.

Know what good looks like

  • Continuity: reconciled reports and consumer acceptance during transition.
  • Platform improvement: refresh reliability, change effort and retirement progress.

Does modernisation require a new vendor?

No. Simplifying models, ownership or pipelines on the current platform may solve the problem. Platform replacement should follow an evidenced assessment.

02 / CONNECTED CAPABILITIES

Understand & predict

Data & Intelligence / 04

Business Intelligence

Power BI, Tableau or custom dashboards built around agreed business metrics.

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Give teams a shared view of what the numbers mean.

Business intelligence connects business definitions to reports that people can use to make decisions. We establish metric ownership, data models and the questions each view should answer. The design should let users move from a headline number to its context without confusing different populations, time periods or calculation rules.

IN PRACTICE

Build an operational performance dashboard

Illustrative engagement

01 / THE STARTING POINT

The challenge

Different teams publish different versions of the same measure, making review meetings about reconciliation.

02 / THE WORK

Our approach

Agree definitions and grain, prepare a reusable reporting model and build focused views for each audience. Add filters, drill-down paths and visible refresh information, then validate totals with metric owners.

03 / THE DIFFERENCE

How it helps

Meetings can focus more on action when the underlying definitions are understood. Adoption is measured by useful decisions, not dashboard visits alone.

Explore delivery guidance & success measures

Build it thoughtfully

  • Document metric definitions, exclusions and ownership alongside the report.
  • Validate totals and filter behaviour before adding more visual detail.

Know what good looks like

  • Trust: reconciliation discrepancies and unresolved definition questions.
  • Usefulness: recurring decisions supported and manual reporting work replaced.

Can existing dashboards be improved instead of replaced?

Yes. An assessment can identify definition, model or usability problems and prioritise focused improvements within the existing reporting platform.

Data & Intelligence / 02

Predictive Analytics

Forecasting, demand prediction and risk analysis grounded in representative historical data.

Let’s discuss this capability ↗︎

Plan with evidence about what may happen next.

Predictive analytics turns historical patterns into forecasts or risk estimates that support planning. We agree the planning horizon, decision cadence and uncertainty that users need to see. A forecast is useful when it changes an action responsibly, not simply when it produces an attractive chart.

IN PRACTICE

Forecast demand for operational planning

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team needs to plan capacity but relies on a single estimate that hides seasonal variation and uncertainty.

02 / THE WORK

Our approach

Review data coverage, build a baseline and backtest forecasts using successive historical periods. Present ranges and explain how unusual events or missing inputs affect interpretation.

03 / THE DIFFERENCE

How it helps

Planners can compare scenarios and review forecast errors systematically. Assess the effect on decisions alongside statistical accuracy.

Explore delivery guidance & success measures

Build it thoughtfully

  • Evaluate forecasts on future-like periods rather than randomly mixing observations from time.
  • Show uncertainty and document when the model should not be relied on.

Know what good looks like

  • Forecast quality: error by horizon, segment and periods of unusual demand.
  • Planning value: changes in decisions and avoidable over- or under-allocation.

Can a forecast guarantee future demand?

No. Forecasts describe patterns and uncertainty in available evidence. They should be reviewed when conditions change and used alongside business judgement.

Data & Intelligence / 01

Machine Learning Development

Custom classification and prediction models for clearly defined business outcomes.

Let’s discuss this capability ↗︎

Build a prediction around a decision someone can act on.

Machine learning is useful when patterns in historical examples can support a recurring decision. We define the target, available evidence and cost of different errors before selecting a model. The work includes data preparation, a simple baseline and evaluation that reflects how the prediction will actually be used.

IN PRACTICE

Prioritise cases for specialist review

Illustrative engagement

01 / THE STARTING POINT

The challenge

An operations team has more cases than reviewers can inspect equally and needs a useful ordering.

02 / THE WORK

Our approach

Define the outcome using historical records, exclude information unavailable at decision time and compare a model with the existing prioritisation rule. Review performance across meaningful case groups before a supervised pilot.

03 / THE DIFFERENCE

How it helps

Review effort can be directed using measured evidence. The model should support a defined action, with humans retaining the decisions agreed in scope.

Explore delivery guidance & success measures

Build it thoughtfully

  • Split evaluation data to reflect the real deployment setting and prevent information leakage.
  • Compare with a simple baseline and examine costly errors separately.

Know what good looks like

  • Decision usefulness: relevant cases found within the available review capacity.
  • Error impact: missed cases and unnecessary reviews across important groups.

Do we need a complex model?

Not necessarily. A simple model or rule may meet the requirement with less maintenance. Complexity should be justified by validated improvement.

03 / CONNECTED CAPABILITIES

Interpret & monitor

Data & Intelligence / 05

Computer Vision

OCR, image classification and visual inspection solutions for defined operational tasks.

Let’s discuss this capability ↗︎

Turn images into reviewable operational signals.

Computer vision extracts information from images or video for a defined task such as detection, classification or inspection. Real-world conditions matter: lighting, camera position and rare defects can change results. We scope the acquisition setup and review process alongside the model so evaluation reflects the environment where it will operate.

IN PRACTICE

Assist a visual inspection workflow

Illustrative engagement

01 / THE STARTING POINT

The challenge

Inspectors need help identifying a defined class of visible defects in a repeatable imaging setup.

02 / THE WORK

Our approach

Agree labels, capture representative examples and assess image quality. Evaluate detections across operating conditions and route uncertain cases to a person, retaining examples for error analysis.

03 / THE DIFFERENCE

How it helps

The system can help focus inspection attention. Measure missed defects and unnecessary reviews separately before changing the existing inspection process.

Explore delivery guidance & success measures

Build it thoughtfully

  • Include varied lighting, angles and difficult negatives in evaluation.
  • Define the response to low-quality images instead of forcing a confident classification.

Know what good looks like

  • Inspection quality: missed targets and false alerts under real capture conditions.
  • Workflow impact: review effort and handling of uncertain or unusable images.

Can a model trained on sample images work with any camera?

That should not be assumed. Changes in capture conditions need validation and may require new data or adjustments.

Data & Intelligence / 06

NLP Solutions

Text extraction, classification and analysis for business documents and conversations.

Let’s discuss this capability ↗︎

Make unstructured language easier to work with.

Natural-language processing supports tasks such as classification, extraction and search across text. We define the categories or information needed, inspect language variation and choose an approach suited to the workload. The output should fit the receiving workflow, including how uncertain or unfamiliar text is handled.

IN PRACTICE

Organise incoming customer messages

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team manually sorts free-text messages into topics before routing them to the right owner.

02 / THE WORK

Our approach

Define a practical taxonomy, review labelled examples and evaluate classification across languages and message types. Provide an uncertain category and a feedback route for reviewers to correct mistakes.

03 / THE DIFFERENCE

How it helps

Teams can reduce repetitive sorting while retaining oversight of ambiguous cases. Review category quality as customer language and products evolve.

Explore delivery guidance & success measures

Build it thoughtfully

  • Keep categories meaningful to the people who act on them.
  • Evaluate rare categories, mixed intent and unfamiliar phrasing separately.

Know what good looks like

  • Classification usefulness: correct routing and errors by category.
  • Review burden: uncertain cases, corrections and taxonomy maintenance effort.

Is a large language model always required?

No. Rules, smaller classifiers or retrieval may fit some tasks better. Compare options using representative text, cost and maintenance needs.

Data & Intelligence / 08

ML Model Monitoring

Track data drift, model performance and the signals that should trigger investigation or retraining.

Let’s discuss this capability ↗︎

Notice when a model needs attention.

Model monitoring examines the inputs, predictions and operational behaviour of a deployed model. Some quality signals arrive immediately; others require delayed outcomes or human review. We define which changes deserve investigation, who responds and what evidence is needed before adjusting or replacing a model.

IN PRACTICE

Monitor a production classification model

Illustrative engagement

01 / THE STARTING POINT

The challenge

A model continues returning predictions, but the team cannot tell whether changing inputs are affecting useful accuracy.

02 / THE WORK

Our approach

Track input quality and prediction distributions, collect available outcomes and review performance by segment. Create alert thresholds with investigation instructions and distinguish service incidents from model-quality concerns.

03 / THE DIFFERENCE

How it helps

Owners can investigate degradation earlier and avoid blind retraining. Monitoring becomes a decision process with a response, not simply another dashboard.

Explore delivery guidance & success measures

Build it thoughtfully

  • Treat drift as a signal for investigation, not proof that accuracy has declined.
  • Document label delays and blind spots so dashboards do not imply unavailable certainty.

Know what good looks like

  • Detection usefulness: actionable alerts and time to investigate confirmed issues.
  • Model quality: observed performance where labels exist, with coverage clearly stated.

Should every alert trigger retraining?

No. The cause may be broken input data, a pipeline change or a harmless population shift. Diagnose the issue and validate a candidate change before release.

HOW THE WORK CONNECTS

A practical path through the practice.

  1. 01

    Define the decision and examine source quality, access and coverage.

  2. 02

    Build the dataset, report or model and compare with a useful baseline.

  3. 03

    Assign ownership for refreshes, quality issues and ongoing performance review.

SCOPE & COMMERCIALS

Combine capabilities.
Keep responsibilities clear.

The practice is a way to plan related work together. It does not require purchasing every service or changing your existing subscription.

Start with a bounded assessment, a defined project or an agreed allocation of specialist capacity. Pricing depends on the scope, dependencies and delivery model.

Confirm third-party costs, access, handover and ongoing support before work begins.

Discuss this practice ↗︎

RESEARCH & FURTHER READING

Explore the thinking behind the work.

scikit-learn · Common pitfalls ↗︎

Practical guidance on evaluation and preventing data leakage.

Microsoft · Retrieval-augmented generation ↗︎

An introduction to grounding answers in retrieved source material.

Further reading behind the practice. These resources do not imply vendor affiliation or certification. Engagement examples describe possible work, not customer results.