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AsterMind AI

Enterprise AI transformation partner

Intelligence engineered into action.

We design, build and scale enterprise AI systems that understand your business, make trusted decisions and turn intelligence into measurable outcomes.

AI solution finder

Select your industry, the challenge you are carrying and the outcome you are measured on.

Partner and technology ecosystem

AI & foundation-model partners/Cloud partners/Data & analytics platforms/Industrial technology platforms/Enterprise application ecosystems/AI & foundation-model partners/Cloud partners/Data & analytics platforms/Industrial technology platforms/Enterprise application ecosystems/

Partner and client marks are published only when verified and approved by the organisation concerned.

Customers

Trusted across space, industry, care and the city.

A selection of enterprise programmes we run across regulated and asset-heavy industries.

Orbitera Space Systems

Space & satellite

Satellite telemetry anomaly intelligence

Helion Aerospace

Aerospace & defence

Predictive engine health analytics

Vertex Aeronautics

Aerospace & defence

Generative design and compliance copilots

Northforge Industries

Manufacturing

Vision-based quality inspection

Kestrel Manufacturing Group

Manufacturing

Autonomous production scheduling

Meridian Health Network

Healthcare

Clinical documentation automation

Aurelia Biosciences

Life sciences

Regulatory dossier generation

Continuum Logistics

Supply chain

Supply-chain control tower

PortNine Freight

Supply chain

Dynamic route and yard optimisation

Civitas Metro Authority

Smart cities

Traffic and utility orchestration

Lumen Grid Utilities

Energy & smart cities

Grid load forecasting agents

Sentinel Assurance

Financial services

Real-time financial crime detection

Customer names shown are programme references published with consent; commercial figures are disclosed only where a client has approved them.

The AI value story

AI is valuable only when it changes what the enterprise can do.

Five capabilities decide whether intelligence reaches the decision. We engineer all five, in order.

Stage 01

Sense

Connect enterprise data, signals, systems and documents into one governed substrate.

AI services

Everything required to move AI into production.

Seven connected capabilities. Engaged individually or as a full transformation programme.

Capabilities

  • AI opportunity discovery
  • Enterprise AI roadmap
  • AI operating model
  • AI maturity assessment
  • Business-case and ROI development

Business outcomes

  • Portfolio prioritised by measurable value
  • Funding model agreed with finance
  • Clear production readiness gates

We map where intelligence changes economics, then sequence the portfolio so the first delivery funds the next.

Explore AI Strategy & Transformation

Capabilities

  • Enterprise AI agents
  • Multi-agent orchestration
  • Human-in-the-loop workflows
  • Agent observability
  • Autonomous process execution

Business outcomes

  • Cycle-time reduction on multi-system processes
  • Exception handling routed to the right human
  • Every action logged and reversible

Agents that plan, call real systems and complete work end to end — with approvals, memory and full observability.

Explore Agentic AI

Capabilities

  • Enterprise copilots
  • Retrieval-augmented generation
  • Document intelligence
  • Multimodal AI
  • Enterprise search
  • Content and knowledge automation

Business outcomes

  • Faster document turnaround
  • Higher first-response accuracy
  • Reduced knowledge search time

Copilots and retrieval systems grounded in your documents, entitlements and domain language — not the open web.

Explore Generative AI

Capabilities

  • Predictive maintenance
  • Digital twins
  • Computer vision
  • Production optimisation
  • Quality intelligence
  • Energy optimisation

Business outcomes

  • Fewer unplanned stoppages
  • Lower scrap and rework
  • Measured energy intensity reduction

OT and IT signals fused into models that predict failure, hold quality and optimise throughput and energy.

Explore Industrial AI

Capabilities

  • Custom AI product development
  • Model integration and fine-tuning
  • LLMOps, MLOps and AgentOps
  • AI application modernisation
  • AI-enabled software engineering

Business outcomes

  • Shorter release cycles
  • Predictable inference cost per transaction
  • Fewer production incidents

Product teams that ship AI software: evaluation harnesses, deployment pipelines and operations from day one.

Explore AI Engineering

Capabilities

  • AI-ready data foundations
  • Knowledge graphs
  • Real-time data engineering
  • Vector and semantic search
  • Cloud AI platforms
  • Enterprise system integration

Business outcomes

  • Reusable data products across use cases
  • Faster onboarding of new AI use cases
  • Consistent metrics across functions

AI-ready data products, semantic context and real-time pipelines connecting models to the systems of record.

Explore Data & AI Platforms

Capabilities

  • AI risk assessment
  • Model governance
  • Explainable AI
  • Security and privacy
  • Regulatory compliance
  • Sovereign AI
  • Continuous monitoring

Business outcomes

  • Approval time reduced for new use cases
  • Complete inventory of AI in production
  • Documented human oversight

Controls that let autonomy scale: risk classification, explainability, monitoring and regulatory evidence.

Explore Responsible AI & Governance

Industries

AI grounded in the realities of your industry.

Select an industry to see the operating challenge, the priority opportunity and the architecture we deploy.

Manufacturing

Asset-heavy operations run on reactive maintenance and manual quality checks while OT data stays trapped on the line.

Priority AI opportunity

Predictive operations and closed-loop quality across plants.

Featured use cases

Predictive asset maintenanceAI-powered quality inspectionAutonomous production schedulingDigital twins and scenario simulation

AI architecture snapshot

Sensors & PLCsHistorianMES / ERPEdge inferenceOperator copilot

Outcome indicators

Unplanned downtime-18% Illustrative
Scrap rate-12% Illustrative
Inspection throughput3x Illustrative

Use-case explorer

Twenty production use cases, filtered to your context.

Filter by industry, function, technology, outcome and implementation horizon.

20 use cases

Predictive MLOperations3-6 months

Predictive asset maintenance

Critical equipment fails without warning, forcing unplanned production stops.

AI approach
Multivariate anomaly and remaining-useful-life models per asset class, deployed at the edge.
Required data
Historian tags, vibration and thermal sensors, maintenance logs.
Systems involved
SCADA, CMMS, ERP
Expected impact
Cost — Fewer unplanned stoppages; maintenance shifted into planned windows.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Computer visionOperations3-6 months

AI-powered quality inspection

Manual inspection is inconsistent and cannot keep line speed.

AI approach
Line-side vision models with active learning on operator corrections.
Required data
Line cameras, defect libraries, inspection outcomes.
Systems involved
MES, QMS
Expected impact
Quality — Lower escape rate and reduced rework.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Predictive MLSupply chain3-6 months

Demand and inventory forecasting

Forecasts miss under promotion and disruption volatility.

AI approach
Hierarchical forecasting with causal features and scenario overlays.
Required data
POS, orders, promotions, external signals.
Systems involved
ERP, planning, WMS
Expected impact
Cost — Improved forecast accuracy and reduced stockouts.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
OptimisationOperations6-12 months

Autonomous production scheduling

Schedules are rebuilt manually whenever constraints shift.

AI approach
Constraint optimisation with agentic re-planning and planner approval.
Required data
Work orders, capacity, changeover matrices.
Systems involved
MES, APS, ERP
Expected impact
Speed — Higher throughput and faster response to disruption.
Time to value
6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIEngineering & R&D0-3 months

Engineering knowledge copilots

Engineers rediscover solutions already documented elsewhere.

AI approach
Grounded retrieval over specifications, CAD metadata and issue history.
Required data
PLM records, drawings, test reports.
Systems involved
PLM, ALM, document stores
Expected impact
Speed — Shorter root-cause analysis and higher design reuse.
Time to value
0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIOperations3-6 months

Clinical document generation

Specialists spend hours drafting regulated documents.

AI approach
Template-constrained generation with citation enforcement and clinician review.
Required data
Trial data, protocols, prior submissions.
Systems involved
EHR, CTMS, eTMF
Expected impact
Speed — Faster drafting with reviewer edits tracked.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Knowledge graphEngineering & R&D6-12 months

Drug-development intelligence

Evidence is scattered across literature, trials and internal research.

AI approach
Biomedical knowledge graph with multi-hop retrieval and provenance.
Required data
Publications, trial registries, internal studies.
Systems involved
ELN, research data platforms
Expected impact
Speed — Broader evidence coverage per research question.
Time to value
6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Predictive MLRisk & compliance3-6 months

Fraud and anomaly detection

Rules engines lag behind evolving criminal behaviour.

AI approach
Graph features plus streaming models with analyst feedback loops.
Required data
Transactions, device signals, counterparty graph.
Systems involved
Core banking, case management
Expected impact
Risk — Fewer false positives at equal or better detection.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIRisk & compliance3-6 months

Intelligent underwriting

Submission review is slow and inconsistent across underwriters.

AI approach
Evidence extraction plus risk scoring with explainable factors.
Required data
Submissions, loss history, exposure data.
Systems involved
Policy admin, rating engine
Expected impact
Revenue — Faster quote turnaround and more consistent decisions.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Predictive MLSales & marketing3-6 months

Personalised banking

Offers ignore real customer context and timing.

AI approach
Next-best-action models with suitability and consent constraints.
Required data
Transactions, product holdings, interactions.
Systems involved
CRM, core banking, marketing platform
Expected impact
Revenue — Higher offer relevance with compliance controls intact.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Agentic AIOperations6-12 months

Claims automation

Simple claims consume the same handling effort as complex ones.

AI approach
Agents triage, validate evidence and settle within authority limits.
Required data
FNOL, images, policy terms.
Systems involved
Claims platform, payments
Expected impact
Cost — More straight-through processing, humans on exceptions.
Time to value
6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AICustomer experience3-6 months

Citizen-service copilots

Caseworkers navigate multiple legacy systems per enquiry.

AI approach
Sovereign copilot over policy, entitlement and case data with audit logging.
Required data
Policy documents, case records, entitlement rules.
Systems involved
Case management, identity
Expected impact
Speed — Shorter handling time and improved first-contact resolution.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Knowledge graphRisk & compliance3-6 months

Regulatory intelligence

Regulatory change tracking is manual and error-prone.

AI approach
Change detection mapped to internal controls and owners.
Required data
Regulatory feeds, internal policies, control library.
Systems involved
GRC platform
Expected impact
Risk — Faster impact assessment on regulatory change.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIOperations0-3 months

Intelligent document processing

High-volume documents are keyed in by hand.

AI approach
Layout-aware extraction with confidence thresholds and human review queues.
Required data
Scanned forms, PDFs, emails.
Systems involved
ECM, workflow, ERP
Expected impact
Cost — Manual keying hours removed with accuracy monitoring.
Time to value
0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIWorkforce productivity0-3 months

Enterprise knowledge search

Employees cannot find governed answers across systems.

AI approach
Entitlement-aware hybrid retrieval with citations.
Required data
Intranet, wikis, file shares, ticket history.
Systems involved
SharePoint, Confluence, ITSM
Expected impact
Speed — Reduced search time and fewer repeat tickets.
Time to value
0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Agentic AICustomer experience3-6 months

Customer-service agents

Contact centres handle repetitive, system-bound requests.

AI approach
Agents that read entitlements and execute transactions with approval limits.
Required data
Interaction history, order and account data.
Systems involved
CRM, billing, order management
Expected impact
Cost — Higher containment with clear escalation paths.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Generative AIIT & software engineering0-3 months

AI-assisted software delivery

Delivery slows under legacy code and documentation debt.

AI approach
Code understanding, test generation and migration assistants under review gates.
Required data
Repositories, tickets, test suites.
Systems involved
SCM, CI/CD, ITSM
Expected impact
Speed — Faster delivery cycles with quality gates enforced.
Time to value
0-3 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
Agentic AISupply chain6-12 months

Supply-chain control towers

Disruption is detected after the cost is committed.

AI approach
Event correlation with scenario simulation and agentic re-planning.
Required data
Carrier events, inventory, supplier signals.
Systems involved
TMS, WMS, ERP
Expected impact
Risk — Earlier disruption warning and lower expedite spend.
Time to value
6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
OptimisationSustainability3-6 months

Energy-consumption optimisation

Energy is managed by static setpoints, not live conditions.

AI approach
Forecast-driven optimisation with operator-approved setpoint changes.
Required data
Meters, weather, production plans, tariffs.
Systems involved
BMS, SCADA, ERP
Expected impact
Sustainability — Reduced energy intensity at constant output.
Time to value
3-6 months

Impact ranges are illustrative until validated with your data.

Discuss this use case
OptimisationEngineering & R&D6-12 months

Digital twins and scenario simulation

Changes are tested in production because there is no safe sandbox.

AI approach
Twin runtime fed by live telemetry, used for what-if simulation.
Required data
Asset models, telemetry, process parameters.
Systems involved
MES, historian, PLM
Expected impact
Quality — Fewer disruptive trials and better change decisions.
Time to value
6-12 months

Impact ranges are illustrative until validated with your data.

Discuss this use case

Impact

Outcomes that move the business.

Company figures are reported from delivery records. Anything modelled is labelled illustrative.

0+

AI and data specialists

Company figure

0+

Production AI solutions delivered

Company figure

0

Countries served

Company figure

0%

Reduction in unplanned downtime

Illustrative

0%

Faster application delivery

Illustrative

0%

Improvement in forecast accuracy

Illustrative

Client impact

AI measured by what changes.

Client names are published only with permission. Figures are client-reported or withheld.

Manufacturing

From reactive maintenance to predictive operations: reducing critical equipment downtime.

Organisation
Global discrete manufacturer (anonymised)
Challenge
Unplanned stoppages on constrained lines were absorbing overtime and lost output.
AI solution
Asset-class failure models with edge inference and planner-approved work orders.
Systems integrated
Historian, CMMS, ERP
Delivery approach
10-week pilot on two lines, then multi-site rollout.
Measurable outcome
Downtime on covered assets reduced (client-reported, figure withheld pending approval).
The models earned trust because maintenance teams saw the lead time they needed to act.
Head of Operations
Full case study

Banking & Financial Services

Detecting financial crime in real time without drowning analysts in false positives.

Organisation
Tier-1 bank (anonymised)
Challenge
Rules-based monitoring generated alert volumes the team could not investigate.
AI solution
Graph features and streaming models with analyst feedback captured into retraining.
Systems integrated
Core banking, streaming platform, case management
Delivery approach
Shadow mode for one quarter before decisioning authority.
Measurable outcome
Material reduction in false positives at equal detection (client-reported).
Shadow mode was non-negotiable. It is why the model went live at all.
Director, Financial Crime
Full case study

Healthcare & Life Sciences

Compressing regulated document drafting while keeping clinicians in control.

Organisation
Life-sciences organisation (anonymised)
Challenge
Specialist time was consumed by first-draft documentation work.
AI solution
Template-constrained generation with citation enforcement and structured review.
Systems integrated
CTMS, eTMF, document management
Delivery approach
Two document families, reviewer-in-the-loop from day one.
Measurable outcome
Draft turnaround shortened; every claim traceable to a source.
Citations changed the conversation with our quality group.
VP, Regulatory Affairs
Full case study

Government & Public Sector

A sovereign caseworker copilot that shortens handling time and logs every step.

Organisation
National public-sector agency (anonymised)
Challenge
Caseworkers navigated several legacy systems per enquiry under strict data rules.
AI solution
In-country deployment with entitlement-aware retrieval and immutable audit trails.
Systems integrated
Case management, identity, policy repository
Delivery approach
Controlled pilot with union and privacy review before scale.
Measurable outcome
Reduced handling time with complete auditability (agency-reported).
Auditability was the requirement that made adoption possible.
Programme Director
Full case study

Logistics & Supply Chain

Sensing disruption days earlier with an agentic supply-chain control tower.

Organisation
Global logistics provider (anonymised)
Challenge
Disruption surfaced only after cost had been committed.
AI solution
Event correlation, scenario simulation and agent-proposed re-plans with planner approval.
Systems integrated
TMS, WMS, carrier feeds
Delivery approach
Single trade lane first, expanded by network segment.
Measurable outcome
Earlier disruption warning and lower expedite spend (client-reported).
Planners kept the decision. The agent kept the options ready.
VP Supply Chain
Full case study

AI architecture

From fragmented systems to an intelligent enterprise.

A reference architecture we adapt per client — with trust and governance applied across every layer.

Trust & GovernanceAll layers
IdentitySecurityPrivacyExplainabilityComplianceMonitoringAuditability

Layer purpose

Experiences

Where people meet the intelligence: copilots, applications and decision surfaces.

Related service

AI accelerators

Move from idea to production faster.

Reusable engineering assets that remove the first eight weeks of every AI programme.

Accelerator

Enterprise Agent Studio

Design, evaluate and operate governed agents against real enterprise tools.

Solves
Agent prototypes stall because permissions, testing and observability are missing.
Architecture
Tool registry, policy engine, evaluation harness, trace store.
Integrations
ERP, CRM, ITSM, Identity provider
Implementation
6–10 weeks to first supervised agent in production.
Governance controls
Scoped permissions · Approval thresholds · Full traces
Request a demonstration

Accelerator

AI Knowledge Hub

Entitlement-aware retrieval across every enterprise knowledge source.

Solves
Copilots answer without provenance or respect for access rights.
Architecture
Connectors, hybrid index, re-ranking, citation service.
Integrations
SharePoint, Confluence, Object storage, Ticketing
Implementation
4–8 weeks to a governed knowledge surface.
Governance controls
Entitlement propagation · Citation enforcement · PII redaction
Request a demonstration

Accelerator

Document Intelligence Engine

Turn high-volume documents into validated structured data.

Solves
Manual keying and review dominate document-heavy processes.
Architecture
Layout parsing, extraction models, confidence routing, review UI.
Integrations
ECM, Workflow, ERP
Implementation
4–6 weeks per document family.
Governance controls
Confidence thresholds · Human review queues · Accuracy monitoring
Request a demonstration

Accelerator

Industrial AI Control Tower

Asset health, quality and throughput intelligence across plants.

Solves
Plant data exists but never reaches the decision in time.
Architecture
Edge collectors, model runtime, alerting, operator console.
Integrations
Historian, SCADA, MES, CMMS
Implementation
8–12 weeks for a first site, then replicate.
Governance controls
OT segmentation · Operator override · Change logging
Request a demonstration

Engagement model

A practical path from ambition to production.

Four stages, each with an exit criterion. Timelines are configurable to your governance cycle.

Responsible AI

Designed to act. Governed to earn trust.

A control surface, not a slogan. Every autonomous path ships with oversight, evidence and a way back.

01

Secure AI

Threat modelling, secrets isolation and tenant separation by design.

02

Explainable decisions

Feature attribution and decision traces attached to every output.

03

Data privacy

Minimisation, redaction and purpose binding across the pipeline.

04

Human oversight

Approval thresholds and reversible actions on every autonomous path.

05

Model monitoring

Drift, quality, cost and latency tracked as production SLOs.

06

Bias testing

Subgroup evaluation with documented thresholds before release.

07

Regulatory alignment

EU AI Act, NIST AI RMF and ISO/IEC 42001 mapped to controls.

08

Sovereign deployment

In-country, in-tenant or air-gapped runtimes where required.

09

Audit trails

Immutable records of prompts, tools, approvals and outcomes.

10

AI red teaming

Adversarial testing of prompts, tools and agent permissions.

ROI scenario calculator

Model the business case before you commit.

Assumptions are yours. Projections are calculated, clearly separated and never presented as guarantees.

Your assumptions

Calculated projection

Hours in scope annually
9,600
Hours automated
3,360
Gross annual saving
151,200
Net year-one position
-448,800
Indicative payback
47.6 months

These are projections computed from the assumptions you entered — not guaranteed results, not a forecast, and not based on your data. We validate assumptions during Discover.

Pressure-test these assumptions

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