Application workspaces
Organise prompts, instructions, tools, knowledge sources, test cases and releases inside controlled workspaces.
Build AI applications, connect private knowledge, coordinate agents, operate models and enforce governance across cloud, on-premise, edge and air-gapped environments.
Ingenious AI is designed as a governed operating environment rather than a single chatbot. Applications, knowledge, agents, models, integrations, analytics and security operate through shared controls and traceable workflows.
Assistants, copilots, applications, dashboards and APIs.
Agents, workflows, tools, routing, evaluations and human review.
Ingenious cognitive models, BYOM, specialised models and inference control.
RAG, document intelligence, knowledge graphs, citations and policy.
Ingestion, indexing, metadata, permissions, lineage and residency.
Identity, RBAC, SSO, MFA, policy enforcement, audit and verification.
Private cloud, on-premise, edge, sovereign and air-gapped runtime.
Create assistants, copilots and domain applications using governed prompts, reusable workflows, private knowledge, tools, model policies and evaluation suites.
Organise prompts, instructions, tools, knowledge sources, test cases and releases inside controlled workspaces.
Assess relevance, groundedness, logic, policy alignment, confidence and review requirements before deployment.
Publish approved versions, track usage, review traces and manage changes through governed release cycles.
Ingest PDFs, policies, reports, websites, databases and API feeds. Preserve source context, access controls, citations and relationships so every answer can be traced.
Extract structured content, classify documents, detect metadata, segment by meaning and preserve page-level source references.
Represent concepts, entities, rules and dependencies as explicit relationships that support reasoning beyond document retrieval.
Specialised agents collaborate through a governed orchestration layer. Every tool call, decision, result and exception remains observable and reviewable.
Search private and approved external knowledge, compare sources and produce cited evidence packages.
Build decision trees, simulate scenarios and send structured problems through DFAM appraisal.
Review logic, evidence, consistency, confidence and escalation requirements.
Invoke APIs, databases, workflows, enterprise software and communication systems.
Store approved user, task and organisational context across sessions and long-running workflows.
Evaluate rules, ethical dimensions, permissions, review gates and audit obligations.
Support model development from corpus preparation and schema alignment to evaluation, quantisation, deployment and version control.
Prepare structured, domain-targeted training and evaluation data with lineage, filtering and governance records.
Align outputs with CFAM, DFAM, agent, knowledge graph and verifier interfaces.
Track model versions, benchmarks, deployment formats, quantisation and rollback compatibility.
Use dedicated private cloud, on-premise infrastructure, edge devices or air-gapped environments while maintaining the same platform controls.
Dedicated, isolated environments with enterprise identity and observability.
Managed deploymentDeploy inside customer-owned infrastructure and network controls.
Local controlCompact model interfaces and local cognitive operations close to the task.
Low latencyNo internet or external API dependency for sensitive environments.
Full isolationPrivate AI chat, RAG document intelligence, knowledge access, dashboards and account management.
Agents, workflows, memory, analytics, APIs, integrations and dedicated model access.
Dedicated or on-premise infrastructure, air-gapped capability, BYOM, custom training and enterprise governance.