Services

Data, analytics and AI
for complex decisions

We help teams understand difficult problems, build the right analytical solution and turn it into something people can actually use.

Typical starting points: uncertain demand, complex operations, location questions, product behavior or repetitive analytical work.

What we doAnd more
How to startCompare the four ways
  1. 1Diagnostic1–2 weeks
  2. 2Proof of concept2–4 weeks
  3. 3End-to-end project4–12 weeks
  4. 4Ongoing support1–2 days per week

Where data can
change the decision

These are the areas we work in most often. The method depends on the problem: sometimes the answer is a forecast or an optimization model, sometimes a spatial analysis, a product insight or an automated workflow. We choose the approach that fits the decision.

Data sourcesUpdated Monday, 07:00 Sales12,480 rows Inventory3,210 rows Forecastmodel v3 Supplier emails14 new AI REASONING Why is North down 12%? 3 products at zero stock Supplier email: delayed to Friday East has 6 weeks of stock Suggest a transfer from East Weekly briefing AI draft Sent Sales vs last week+4%8 weeks ● North −12%: out of stock → Move stock from East SOURCES Sales Inventory Supplier email LLena, analystChecked the numbers, approved Approve Sent 3 h every Monday 20 min to reviewExample. AI prepares, a person decides.

01AI & intelligent automation

Helps decidewhere AI is worth building, and where something simpler does the job.

Apply AI where it can remove repetitive analytical work, connect information and support recurring decisions without adding unnecessary complexity.

Good fit when skilled people spend hours on repetitive analysis or the same judgment calls, week after week.

Common work

AI agentsAgentic workflowsLLM applicationsAutomationProof of concept
Station AvenueMarket StreetMill RoadNorth RoadMill RiverOLD TOWNRIVERSIDEEAST END 2,810 customers not covered Existing siteNew siteService areaCovered demandUncovered demandDarker = more customers 1Current coverage 2Gap found 3New site CUSTOMERS COVERED45% → 78%with one new site

02Geospatial, mobility & network analytics

Helps decidewhere to place capacity, coverage or service, and how to move it.

Use location, movement and network data to understand where demand happens, how systems connect and where coverage can improve.

Good fit when location matters: networks, fleets, delivery areas, physical sites or real estate.

Common work

Geospatial analyticsMobilityLocation intelligenceSite & market analysisNetwork analysis
OPTIONCOSTLower is betterSERVICE LEVELHigher is betterRISKLower is betterFEASIBLEWithin limits Current plan 120 78% Scenario A 100 82% Recommended Scenario B 92 90% Scenario C 110 85% RECOMMENDED OPTIONScenario BBest balance of costand service level +15%service levelvs. current plan −8%total costvs. Scenario A Feasiblewithinconstraints
BeforeOptimized North East West South North East West South −20% +30% −10% +15% Capacity (before)ReallocationCapacity (optimized)
FacilityCustomer demandCoverage areaNot covered Before Optimized Same total capacity4 facilities −11%average distance +29%coverage inhigh-demand areas

03Optimization & simulation

Helps decidewhich option to choose when resources are limited and the trade-offs are real.

Explore trade-offs, constraints and possible outcomes before making a costly operational or strategic decision.

Good fit when several options look reasonable and choosing the wrong one would be expensive or hard to undo.

Common work

Resource allocationScenario simulationLocation optimizationNetwork optimizationWhat-if analysis
Historical demandForecastForecast uncertaintyCapacity limit Units 100200300400500600700 Today Capacity Capacity shortfallexpected in W9Action: add capacitybefore W9 W1W3W5W7W9W11

04Forecasting & predictive analytics

Plan ahead when demand is uncertain

Forecast demand, capacity and workload with statistical models and machine learning, so teams can plan earlier. The work ranges from a one-off forecast to a model that updates every week as new data arrives.

Good fit when planning is still based on last year’s numbers or gut feeling, and the surprises keep costing money.

Common work

Demand forecastingCapacity planningTime seriesPredictive modelingMachine learning
VisitSee the product 100% Sign-upCreate an account 38% lost 62% ActivateComplete key action 34% lost +4 pts 28% 28% → 32% RetainActive after 30 days +3 pts 21% 21% → 24% 62% 45% 51% 75% Biggest drop-offOnly 45% of sign-upsactivate. +6 pts activationafter the onboardingchange was tested. 1Find the drop-off 2Test a fix 3Measure the lift

05Customer & product analytics

Helps decidewhich product changes to make, and whether they actually worked.

Understand how customers behave, where journeys break down and which changes are most likely to improve product performance.

Good fit when metrics are moving, opinions differ and nobody is sure what is driving the change.

Common work

ConversionRetentionSegmentationExperimentsMetric design

Supporting capability

Data foundations when needed

When a project needs it, we also build the pipelines, integrations and analytical infrastructure that make the solution work end to end. Data engineering supports the solution. It does not become the project by default.

Data pipelinesAPIsDatabasesData integrationAnalytical tooling

Your problem is not on this list? These areas overlap and are not a complete list. If data can inform the decision, we are happy to take a look. Discuss a problem

Ways to work together

Start with the scope
that makes sense

Not every problem needs a long consulting program. We can start small, validate the direction and expand only when there is a clear reason to.

  1. 01

    Diagnostic

    1–2 weeks · fixed scope

    A focused question, a defined scope and a concrete recommendation or analytical answer.

    Start with a diagnostic
  2. 02

    Proof of concept

    2–4 weeks

    Test whether a forecasting, optimization, geospatial or AI approach is useful before committing to a larger build.

    Test an idea first
  3. 03

    End-to-end project

    Typically 4–12 weeks

    From problem framing and modeling through implementation, integration and a working analytical solution.

    Plan a project
  4. 04

    Ongoing support

    1–2 days per week

    Senior analytical support for teams that need recurring help with decisions, models, priorities or implementation.

    Ask about ongoing support

Have a data problem that does not
fit neatly into a box?

Start with the problem. We can work out whether forecasting, optimization, analytics, AI or something simpler is actually useful.

Discuss a problem