G-ATAI / Solutions

Research & industries

Explore our research, knowledge systems and industry applications. Connect technical ideas to practical use cases in finance, healthcare, manufacturing and other sectors.

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Industries We Support

Sectors

Sector tech shifts: real-time finance, interoperable health APIs, headless commerce, and factory digital twins with predictive analytics.

Financial Services: Real-Time, Compliant & Connected

In finance, legacy batch processes are giving way to streaming risk platforms, instant payments and the global adoption of ISO 20022 messaging standards. We help banks, fintechs and payment networks build scalable, compliant infrastructure that supports frictionless transactions and real-time analytics.

ISO 20022 readiness: migration strategy, architecture and validation for new messaging formats.
Streaming risk engines: ingest and process market & operational data in real-time to detect exposures.
Instant payments platforms: modern APIs, reconciliations and settlement workflows for 24/7 operations.

Healthcare & Life Sciences: API-Driven, Privacy-First

Healthcare interoperability is evolving fast standards like FHIR/HL7, consent-driven data flows and de-identification are vital. We support providers, payers and research institutions to build secure, compliant systems that unlock data value while maintaining trust.

FHIR API platforms: architecture and integration for record exchange, event-driven care workflows and consumer apps.
Consent & identity management: patient-centric access controls with audit-ready trails.
De-identification & analytics: pipelines that extract insights from sensitive health data while preserving privacy.

Manufacturing & Industry 4.0: From Telemetry to Predictive Maintenance

Manufacturers are moving beyond sensor collection to real-time intelligence using IIoT, OPC UA connectivity and digital twin modeling. We help enterprises integrate factory floor, edge and cloud systems to drive operational efficiency and predictive asset maintenance.

OPC UA architecture: secure data modelling, device integration and edge-to-cloud pipelines.
IIoT telemetry platforms: deploy scalable ingestion, normalization and dashboarding of sensor data.
Predictive maintenance: apply ML/analytics to detect anomalous behaviour and schedule maintenance before failure.

AI Capability (When Relevant)

Optional

AI shifts: task-specific small models, tool-use with structured outputs, and retrieval-first patterns for grounded results.

Function-Calling & Typed Outputs for Reliable Results

Instead of open-ended responses, use AI with function calling and JSON Schema definitions to ensure predictable, typed outputs you can embed directly into workflows.

JSON Schema enforced APIs: define input/output interfaces so tools and agents produce expected formats.
Typed responses: validate model output at runtime, convert into objects, raise errors when mismatched.
Audit logs: capture both prompt and structured output for traceability and debugging.

Latency-Aware Routing, Semantic Caching & Resiliency

AI production systems demand low latency and predictable cost. Use semantic caching, route requests between local models vs cloud, and handle falls-backs and retries gracefully.

Local vs remote routing: decide based on latency, cost, model size, or compliance.
Semantic cache layers: reuse retrieved knowledge or previous output to reduce API calls and speed up responses.
Retry & fallback logic: monitor cost/latency budget, retry smaller models or cached output when needed.

Guardrails for Safety, Cost & Privacy

Deploying AI at scale requires more than accuracy. Embed mechanisms for tool‐use monitoring, cost control and data privacy ensuring the system is safe, compliant and economical.

Tool-use sandboxing: limit which tools/agents can call what; monitor calls for anomalies.
Cost ceilings: enforce max tokens per request, track spend across tenants/features.
Data privacy compliance: scrub PII, enforce access policies, avoid leaking internal knowledge to external models.

Knowledge & Search

Knowledge

Search trends: hybrid lexical + vector retrieval, graph-augmented RAG, and semantic caching for lower latency.

Hybrid Retrieval: Lexical, Vector & Re-Ranking

Pure keyword search is no longer sufficient. Combine lexical search with vector embeddings and then re-rank based on relevance and telemetry data to get the right result fast.

Chunking & segmentation: split documents into semantic chunks for embedding and retrieval.
Re-ranking strategies: use embedding similarity + metadata signals (clicks, dwell time) to boost relevance.
Telemetry-tuned ranking: feed usage data back into the model to continuously improve retrieval quality.

Graph-Based RAG (Retrieval-Augmented Generation)

Enhance your RAG stack with a knowledge graph: map entities, relationships and citations so generated responses are grounded, auditable and fact-based.

Entity/relationship modeling: capture links between people, places, products, events in a graph structure.
Graph-query layer: preprocess retrieval results with graph algorithms to ensure consistency and coverage.
Citation trails: link generated text back to graph sources and original documents for traceability.

Access Control & Embedding Governance

Embeddings and indices often contain sensitive information. Implement attribute-based access control (ABAC) on embedding vectors and retrieval logic to enforce privacy, tenant isolation and data sovereignty.

Role-based vector access: only allow embeddings or retrieval of data based on user/tenant roles.
Index segmentation: maintain separate indices or namespaces for sensitive vs non-sensitive data.
Audit logging on queries: capture which vectors were accessed, by whom and why.

Research & Innovation

At the heart of our company is a commitment to continuous research and innovation. Our dedicated R&D team explores emerging technologies and methodologies to stay ahead of the curve and deliver state-of-the-art AI solutions.

Industries Served

Our AI models serve finance, healthcare, retail, manufacturing, technology and more. From customer experience to supply-chain optimization and decision intelligence, we adapt our solutions to each sector’s reality.
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Integration & Deployment

We plug AI into your existing tools and workflows with a focus on scalability, observability and reliability, so models perform in production—not just in demos.
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Data Engineering

We handle collection, cleaning, enrichment and governance so models sit on top of clean, trustworthy data—structured and ready for training or analytics.
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Custom AI Solutions

When off-the-shelf is not enough, we design bespoke models and agents for your exact constraints: latency, regulation, on-prem, multi-language, or edge devices.
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