The capabilities GET applies to real technical problems in fast-moving domains.
We do not market a sector — we work on a class of technical question. What follows describes the shape of those questions and the domains we've carried them through. Neither list is closed: capabilities grow with the team, and new domains open with each engagement.
Detection, search, inspection, monitoring, and visual analysis — applied to operational environments where reliability and interpretability matter more than novelty.
Example use cases
Object detection and tracking on real-world footage
Visual search across large image archives
Quality inspection and anomaly identification
Activity and event monitoring
Document and structured-image processing
Possible deliverables
Annotated benchmarks against client data
Trained models with evaluation harness
Inference services for client infrastructure
Failure-mode and edge-case documentation
Questions clients ask
Can a vision model reach acceptable precision on our data?
What annotation volume is realistically required?
How does the system behave on out-of-distribution inputs?
CAP / 02
Audio & Multimodal Systems
Research involving audio, video, text, sensor streams, and combinations of them — where the signal of interest crosses modalities.
Example use cases
Speech recognition and speaker analysis for niche domains
Acoustic event detection and classification
Cross-modal search (text → video, audio → text)
Sensor fusion for operational monitoring
Possible deliverables
Domain-adapted models with measured accuracy
Streaming inference architectures
Joint embedding spaces with retrieval evaluation
Data collection and labeling protocols
Questions clients ask
Will an off-the-shelf model work on our acoustic environment?
How do we evaluate quality when ground truth is partial?
What does a robust multimodal pipeline look like in production?
CAP / 03
Automation & Decision Support
Intelligent workflow systems that reduce manual review, surface what matters, and assist operators in high-volume or high-stakes decisions.
Example use cases
Triage and prioritization tools for analyst queues
Document and case-file summarization for review teams
Anomaly surfacing and exception routing
Process orchestration with human-in-the-loop checkpoints
Possible deliverables
Reviewer interfaces with audit trails
Backend pipelines and queues
Calibration reports for confidence thresholds
Operational playbooks for handoff
Questions clients ask
Where can review effort be cut without losing oversight?
How do we measure if the automation is actually helping?
What does the failure-recovery path look like?
CAP / 04
Infrastructure & Deployment
Practical deployment paths for advanced systems — including secure, on-premise, and client-controlled environments where standard public cloud patterns are not enough.
Example use cases
On-premise model inference and orchestration
Air-gapped or restricted-network deployments
Data-residency-constrained pipelines
Encrypted-at-rest data and embedding stores
Possible deliverables
Reference architecture documents
Infrastructure-as-code for client environments
Threat model and access-control design
Operational runbooks
Questions clients ask
What does a deployable architecture actually look like here?
How do we run this without sending data to a third party?
Can we operate offline with periodic synchronization?
CAP / 05
Data & Systems Research
Processing pipelines, search systems, indexing strategies, and structured analysis at scales where engineering and modeling overlap.
Example use cases
Large-scale ingestion and normalization pipelines
Hybrid (keyword + vector) search systems
Entity resolution and record linkage
Domain-specific indexing strategies
Possible deliverables
Pipeline architecture and code
Index design and benchmark results
Quality measurement frameworks
Cost and scaling models
Questions clients ask
What does the right ingestion-to-index architecture look like?
Where is precision lost, and what would it take to recover it?
What scales linearly and what does not?
CAP / 06
Evaluation & Validation
Technical testing, benchmarking, risk analysis, and deployment-readiness assessment — often as a standalone engagement before larger investment or post-build verification.
Example use cases
Independent technical due diligence
Benchmarking against internal or external baselines
Failure-mode analysis and adversarial testing
Deployment-readiness review for advanced systems
Possible deliverables
Evaluation framework and harness
Quantitative benchmark report
Risk register with severity and likelihood
Recommendations with cost framing
Questions clients ask
Does this system perform as claimed on our data?
What is the structural risk if we ship as-is?
Where should we invest before scaling up?
The catalogue is open — capabilities are added as the team grows
Domains
Domains we've carried high-stakes work through.
Where failure has cost, oversight is mandatory, and the consequences of getting it wrong extend beyond a balance sheet. Across these domains we've led research, delivered prototypes, validated systems, and partnered on production deployment.
GOV / 01Active
Government & Public Sector
Federal, state, and municipal programs where decisions affect citizens, budgets, and accountability — and the work has to stand up to audit, FOI, and regulatory review.
DEF / 02Active
Defense & National Security
Mission systems, intelligence analytics, and operational AI for environments where reliability, controlled disclosure, and security clearance posture are baseline requirements.
AER / 03Active
Aerospace & Space Systems
Satellite analytics, mission-software adjacent research, autonomy, and ground-segment AI for organizations operating where retries are rare and integration tolerances are tight.
NUC / 04Active
Nuclear Energy & Safety
Applied AI for monitoring, anomaly detection, predictive maintenance, and operational decision support inside regulated nuclear environments where safety cases must be defensible.
LAW / 05Active
Law & Legal Tech
Document understanding, case-file analysis, contract review, e-discovery, and legal-research systems where explainability, provenance, and human-in-the-loop oversight are non-negotiable.
MED / 06Active
Healthcare & Clinical Systems
Clinical decision support, health systems research, and patient-facing AI evaluated against the standards of implementation science — usability, safety, and real-world effectiveness.
FIN / 07Active
Financial Services & Risk
Fraud and AML, market and credit analytics, compliance automation, and risk-modelling systems where model governance, fairness, and regulator-facing documentation are part of the deliverable.
INF / 08Active
Critical Infrastructure
Power, water, transport, and telecom backbones — analytics, anomaly detection, and operational AI for systems where downtime cascades into public consequence.
CYB / 09Active
Cybersecurity & Threat Intelligence
Threat-detection models, SOC analytics, identity and access intelligence, and defensive AI — built for adversarial environments where evaluation has to include failure under attack.
BIO / 10Active
Pharmaceutical & Life Sciences
Drug-discovery analytics, clinical-trial intelligence, biomedical NLP, and laboratory-data systems for organizations where the standard of evidence is the standard of approval.
ENR / 11Active
Energy & Utilities
Grid analytics, generation forecasting, predictive maintenance, and operational AI across conventional, renewable, and transmission environments — engineered for uptime and audit.
IND / 12Active
Manufacturing & Industrial
Vision-based inspection, predictive maintenance, process optimization, and industrial AI — built to operate on factory floors with realistic edge compute, lighting, and human workflow constraints.
AUT / 13Active
Automotive & Mobility
ADAS, perception and sensor-fusion stacks, autonomous-driving research, simulation pipelines, and fleet intelligence — where milliseconds of model latency and millimeters of localization error translate directly into safety outcomes.
GEO / 14Active
Geospatial & Earth Observation
Satellite imagery analysis, change detection, geospatial segmentation, remote sensing, and location intelligence — for organizations whose decisions depend on knowing what is changing on the ground, and how fast.
ROB / 15Active
Robotics & Autonomous Systems
Industrial and field robotics, perception–planning–control stacks, autonomous manipulation, drones and aerial systems, and human-robot interaction — where the physical world's dynamics, not just the model's accuracy, decide whether the system actually works.
Not seeing a domain? Most engagements begin as unmapped problems · talk to us
Working outside this list?
If your problem doesn't map cleanly to a capability, that's often the right reason to talk.
The most informative engagements usually start as ambiguous requests. We do not require a clean problem statement to start a conversation — and we are explicit when an idea is not yet ready for development.
Frequently asked
How capabilities and domains map to engagements.
Direct answers about what we apply, where we work, and how regulated environments are handled.
Applied AI and computer vision, audio and multimodal systems, automation and decision support, infrastructure and deployment, data and systems research, and evaluation and validation. Each capability combines domain study, applied AI methods, and prototype engineering — not generic consulting.
Government and public sector, defense and national security, aerospace and space systems, nuclear energy and safety, law and legal tech, healthcare and clinical systems, financial services and risk, critical infrastructure, cybersecurity and threat intelligence, pharmaceutical and life sciences, energy and utilities, manufacturing and industrial AI, automotive and mobility, geospatial and earth observation, and robotics and autonomous systems.
Yes. Engagements involving regulated environments are scoped against the relevant control set (FedRAMP High, DoD IL4/IL5, HIPAA, PCI, ITAR) from the start. Architectural decisions, data residency, key custody, audit logging, and accreditation evidence are first-class design inputs rather than afterthoughts.
Yes. The Infrastructure & Deployment capability covers on-premise model inference, air-gapped enclaves, data-residency-constrained pipelines, and encrypted-at-rest data and embedding stores. Cloud-only patterns do not always fit; we design for the environment the system has to live in.
Capabilities describe the shape of technical questions we work on. Scoping happens against the client's domain, data, constraints, and operational environment — so a Computer Vision engagement for an inspection workflow looks different from one for document understanding. We are explicit when an idea is not yet ready for development.
Next step
Have a technical challenge worth investigating?
Bring us the problem. We will help determine what is possible, what is practical, and what should be built next.
