Industry knowledge. Custom AI.
Space AI. Turn complex signals into useful decisions.
Space data becomes valuable when it improves a specific decision on Earth or in an operational workflow. HolisticRM combines Earth observation, navigation and domain data with bespoke model development. Selected by ESA for 8 AI projects, our experience spans company and consortium roles; the project references below explain the relevant role and delivery stage.
- Connect space data with real customer and operational needs
- Compare model combinations against the right mission constraints
- Build evidence for the next stage of development and deployment
Completed ESA demonstration · HolisticRM prime contractor
ESA NAVISP project · Modelling contribution in a consortium
Ongoing ESA demonstration · HolisticRM subcontractor to GeoX
Completed ESA feasibility study · HolisticRM subcontractor to GeoX
Top 10 AI use cases for space
Ten opportunities across space operations and downstream services. These are proposed application patterns, not a claim that HolisticRM has delivered every one. We prioritise with mission and business specialists, considering data availability, validation needs and the consequences of a wrong decision.
01Navigation performance predictionModel how navigation-system performance changes across operating scenarios to support analysis and planning by qualified specialists.
What we implementCombine geometry, historical performance and relevant environmental features; compare baseline and machine-learning predictors, then evaluate errors across time, geography and adverse conditions.
Business benefitProvide richer scenario evidence and reduce reliance on repeated manual analytical work.
How value is measuredMeasure prediction error and uncertainty coverage for agreed performance quantities and scenarios.
Risk: Rare conditions may be underrepresented, making average test scores insufficient for critical use. Control: Use scenario-specific acceptance tests, preserve expert review and keep operational assurance requirements separate from model evaluation.
02Earth-observation scene quality triagePrioritise imagery suitable for a downstream task before analysts spend time processing unusable or irrelevant scenes.
What we implementCombine scene metadata with cloud, shadow and quality classification; compare selection policies against expert-reviewed examples for the intended sensor and area.
Business benefitReduce avoidable processing and shorten the queue of images requiring manual inspection.
How value is measuredTrack usable-scene recall, rejected useful scenes and processing effort per accepted product.
Risk: A strict quality filter can discard rare events or scenes that remain useful locally. Control: Test task-specific thresholds, retain provenance and route uncertain or high-priority scenes to human review.
03Land-cover and change intelligenceTurn repeat imagery into reviewable indications of relevant land-use or infrastructure changes across an area of interest.
What we implementAlign multi-date observations, account for season and sensor differences, and combine segmentation or change models with domain reference layers.
Business benefitHelp analysts focus field checks and maintain more timely spatial intelligence.
How value is measuredMeasure confirmed change precision, missed events and time from observation to reviewed alert.
Risk: Seasonal appearance or misalignment can be mistaken for meaningful physical change. Control: Validate separate regions and seasons, expose supporting imagery and require review before consequential decisions.
04Space-enabled solar sitingUse satellite and local context to screen potential renewable-energy locations consistently across a large search area.
What we implementJoin Earth observation features with terrain, grid and land constraints; produce interpretable suitability rankings and compare candidates with known reference locations.
Business benefitAccelerate initial screening and give developers a traceable shortlist for specialist assessment.
How value is measuredTrack analyst time, candidate review quality and progression into verified feasibility work.
Risk: A model score can overlook changing grid capacity or constraints absent from the data. Control: Separate hard exclusions from scores, date input layers and require current engineering and permitting checks.
05Space-enabled EV infrastructure planningCombine location, movement and environmental context to assess where charging infrastructure could serve realistic demand.
What we implementTrain demand or suitability models using available charging outcomes and spatial features; evaluate candidate sites under network, competition and grid scenarios.
Business benefitSupport more transparent prioritisation of charging-network expansion and capital deployment.
How value is measuredMeasure site-level forecast error and realised utilisation after a defined ramp-up period.
Risk: Demand shifts and nearby deployments can invalidate assumptions made during early planning. Control: Use scenario ranges, validate unseen regions and refresh material inputs before an investment decision.
06Geospatial customer affinity and acquisitionTranslate Earth observation and geographic context into more relevant audience-location choices for consumer-facing organisations.
What we implementCombine permitted customer geography with contextual spatial layers; build client-specific affinity models and compare campaign locations through a controlled test.
Business benefitHelp marketing teams focus spend on locations with stronger evidence of customer fit.
How value is measuredTrack incremental qualified response and return on spend against comparable control locations.
Risk: Neighbourhood averages can be misread as facts about individual people or sensitive traits. Control: Use appropriate aggregation, review feature suitability and verify actual campaign outcomes rather than inferred personal characteristics.
07Telemetry anomaly reviewHelp operations specialists find unusual sequences in telemetry streams while preserving mission-specific interpretation and control authority.
What we implementCreate operating-mode-aware baselines, compare anomaly detectors and group related signals into evidence-rich events for a ground-based review queue.
Business benefitReduce repetitive monitoring work and improve the focus of engineering investigation.
How value is measuredMeasure useful event precision, missed known events and operator review time.
Risk: Normal mode changes may trigger alarms while unseen faults remain undetected. Control: Evaluate labelled events and mode transitions, limit alert volume and retain existing fault-management procedures.
08Observation and ground-resource schedulingSupport planners comparing feasible observation or ground-processing schedules when resource, timing and service priorities compete.
What we implementCombine demand forecasts and task estimates with a constrained optimiser; simulate schedules using availability windows, deadlines, capacity and mission rules.
Business benefitMake planning trade-offs visible and improve the use of constrained resources.
How value is measuredTrack completed priority tasks, missed deadlines and planner effort against current scheduling.
Risk: Missing constraints or uncertain execution times can produce an unusable plan. Control: Capture rules with operators, stress-test disruption scenarios and require approval before committing operational schedules.
09EO-assisted environmental inspectionCombine imagery and local evidence to flag environmental conditions that may warrant closer investigation by domain specialists.
What we implementDefine the target event, curate reviewed examples and compare spectral, temporal and contextual models; deliver maps with confidence and supporting observations.
Business benefitExtend screening coverage and help experts prioritise limited inspection resources.
How value is measuredMeasure confirmed-event precision, detection coverage and time to reviewed assessment.
Risk: Clouds, limited ground truth and changing seasons can obscure events or exaggerate certainty. Control: Show coverage gaps, validate against independent observations and avoid treating an automated flag as a confirmed event.
10Space engineering knowledge assistantsHelp engineering teams retrieve approved requirements, interface details and lessons learned with traceable answers and controlled access.
What we implementIndex versioned project documentation, preserve permissions and retrieve source passages; evaluate language-model answers against specialist-written questions and revision-sensitive cases.
Business benefitReduce document-search friction and improve continuity across complex engineering teams.
How value is measuredTrack verified answer quality, correct source retrieval and reviewer time per task.
Risk: Conflicting revisions or fabricated citations can mislead an engineer about an important requirement. Control: Display authoritative revisions, reject unsupported answers and require qualified review for design or mission decisions.
These are opportunities to evaluate for your organisation. Priorities, feasibility and targets are agreed against your data, constraints and baseline.
Tangible business benefits
Less time between data and action
Triage, retrieval and prioritisation can reduce repetitive processing and give specialists a more useful work queue.
Measure: Processing cost, analyst hours and time to a reviewed decision for the selected workflow.
Benefits beyond immediate financial returns
Confidence across disciplines
Mission, data, engineering and commercial teams share a traceable account of what the model does and where its limits matter.
-
Define the mission or customer decision
Agree the intended user, operating context, value and failure consequences with domain specialists. Our CRISP-DM-based approach draws on PMI CPMAI principles to keep research connected to a concrete decision.
Output: Use-case boundary, success criteria and maturity target.
-
Understand the observations
Review sensor characteristics, timestamps, spatial coverage, labels, licences and access constraints. Map how space-derived data will connect to the domain information needed for the chosen application.
Output: Data provenance, coverage assessment and preparation plan.
-
Improve inputs and modelling together
Our holistic method tests useful additional context while comparing suitable model families and combinations. Start with a practical baseline; include complexity only when its value survives the agreed evaluation.
Output: Reproducible data and model experiments.
-
Challenge the model outside familiar conditions
Validate across time, locations, operating modes or sensors as appropriate. Test uncertainty, uncommon scenarios and resource limits with domain experts before choosing the delivery configuration.
Output: Scenario-based validation and a go, refine or stop decision.
-
Demonstrate in the intended workflow
Integrate a bounded pilot into the analyst's map, engineering tool or operational review queue. Capture usability, processing constraints and real user actions alongside model performance.
Output: Pilot evidence, user feedback and next-stage requirements.
-
Operate with traceability and control
Version inputs, models and releases; define access, monitoring, incident handling and fallback. Retrain only through an agreed review cycle, preserving the distinction between a research prototype and an approved operational system.
Output: Handover package, monitoring and controlled improvement plan.
Better data and better models, developed together.
Our Holistic Agile ML approach builds on CRISP-DM and draws on the business-led, iterative principles of PMI’s CPMAI methodology. Domain experts shape the decision, usable data and acceptance criteria. We improve relevant inputs while comparing suitable model families, architectures and model combinations against a practical baseline.
Protected test sets, versioned experiments and agreed go/no-go gates help catch leakage, weak assumptions and unnecessary complexity before wider rollout. A controlled pilot, human escalation, monitoring and rollback reduce operational exposure. The result is a bespoke model set selected for your business case and operating constraints.
Make the risks visible. Build the controls in.
Risk controls are tailored to the decision, data and operating environment, with named owners and agreed acceptance evidence.
- Transfer across geography, sensors and modes
-
A strong result on familiar data may not transfer to another region, instrument or operating regime.
How we reduce the risk: Use representative holdouts, record coverage limits and require targeted revalidation before expanding the service.
- Rare events and misplaced confidence
-
Average performance can conceal failures under unusual conditions with substantial operational consequences.
How we reduce the risk: Define scenario-specific tests and uncertainty handling with domain experts; keep mission assurance and operational approval as explicit workstreams.
- Data continuity and provenance
-
Missing observations, licensing restrictions or changed upstream processing can interrupt a service or alter model behaviour.
How we reduce the risk: Track source and processing versions, monitor coverage and freshness, and define fallback behaviour for unavailable inputs.
- A useful model without a viable service
-
A technically successful experiment may not fit the user's workflow, response-time needs or cost constraints.
How we reduce the risk: Set business and operational acceptance criteria early, test with actual users and review the cost of the complete data-to-decision chain.
Does an ESA project reference mean a model is certified for mission-critical use?
A project reference establishes the specific programme, role and work described by its source. Operational assurance depends on the intended system and application. We define validation, review responsibilities and release criteria for each engagement rather than treating a reference as a general certification.
Can space data improve a non-space business?
Yes, when it contributes useful information to the decision. Earth observation and geographic context can support site selection, infrastructure planning and customer-location analysis. We test their incremental value against a practical baseline instead of assuming that adding satellite data always improves a model.
What does a bespoke model set mean for a space application?
It means choosing components for the actual job: perhaps image interpretation, a structured predictor and a constrained optimiser, connected through a validated workflow. Each component has its own inputs, limits and evaluation; the whole system is then assessed against the user's operational and business needs.
From space data to a decision that matters.
Bring your mission, Earth observation or downstream service challenge. We will help define the right data, model combination and evidence for the next development stage.
Bring one business challenge, the decision you want to improve and an outline of your available data. Please leave out confidential customer or patient information.