Products
Four Products for Security, AI,
Observability & Compliance.
Each product works on its own and runs in your cloud account, data center, or our managed cloud. Together they share sign-on, roles, and data, so a finding in one shows up where the others need it.
Application Security & Non-Human Identity (NHI)
TrustIvan
Code, Container & Machine-Identity Security in One Place
Most breaches now start with a vulnerable dependency or a leaked machine credential. TrustIvan covers both: Trivy-based scanning of images, packages, and SBOMs, plus discovery and rotation of non-human identities such as service accounts, API tokens, CI/CD bots, and MCP tool servers. Triage decisions are stored against the finding rather than the image, so an accepted risk stays accepted after the next rebuild and the next scan.
Capabilities
- Container & SBOM Scanning. Trivy-based inspection of base images, layers, OS packages, and language dependencies, with persistent triage so accepted risks do not reappear on every scan.
- Non-Human Identity (NHI) Governance. Discovers service accounts, tokens, cloud credentials, and CI/CD runners, flags unused or over-privileged ones, and automates rotation.
- AI Agent Runtime Authorization. Policy checks on every tool call an LLM agent or MCP server makes, so an agent can reach only the systems its task needs.
- Fix Pull Requests. Opens pull requests with dependency and base-image upgrades, so findings arrive as reviewable changes instead of tickets.
- Supply Chain Attestation. Signs container images and SBOMs with Cosign and in-toto attestations so provenance can be verified at deploy time.
- Secret Detection in CI/CD. Scans pull requests and commits for exposed API keys, private keys, and cloud service tokens before they merge.
Integrations
- Docker
- Kubernetes
- GitHub Actions
- GitLab CI
- AWS ECR
- Google Artifact Registry
- Trivy
- Cosign
Targets
- Container Scan Time
- < 12s
- Vulnerability Backlog Reduction
- 78%
- Machine Identities per Tenant
- 100K+
- Critical CVE Triage
- < 15min
Design targets and benchmarks, validated with each customer during a pilot.
Why Teams Choose It
- Reduces token and secret sprawl across repositories and pipelines
- Maps findings to NIST SSDF, CIS Benchmarks, and Executive Order 14028 SBOM requirements
- Lets AI teams ship agents without handing them broad credentials
Enterprise AI & Agent Orchestration
MITHUNAI
Private Enterprise AI That Shows Its Sources
MITHUNAI pairs a visual studio for building agent workflows with a retrieval engine that answers from approved sources only. Answers link back to the passages they used, so a reviewer can check any claim in one click. Deploy it in your VPC or data center, route sensitive prompts to local models, and embed assistants in internal tools or customer-facing apps.
Capabilities
- MITHUNAI Studio. Visual workflow canvas for chatflows, model routing, and dataset pipelines across private and open-source LLMs.
- Grounded Knowledge Engine (Arukz). Retrieval-augmented Q&A with source attribution on every answer and a link back to the original passage.
- Multi-Agent Collaboration. Specialized agents draft, review, and critique each other’s output before a result is returned.
- Embeddable Web Widget. A drop-in widget that brings cited answers into your product, help center, or intranet.
- Private Model Routing. Routes prompts between on-premise open models (Llama, Mistral) and hosted frontier models based on data sensitivity.
- PII & Secret Guardrails. Strips personal data, card numbers, and secret tokens from prompts before they reach a model.
Integrations
- vLLM
- Ollama
- Hugging Face
- LangChain
- LlamaIndex
- PostgreSQL pgvector
- ECharts
- REST / gRPC
Targets
- Answers With Citations
- 99.98%
- Token Throughput / s
- 450k+
- Document Ingestion Latency
- < 2.4s
- Deployment Options
- VPC / On-Prem
Design targets and benchmarks, validated with each customer during a pilot.
Why Teams Choose It
- Reduces unsupported AI answers by requiring a cited source for each claim
- Keeps proprietary data in your VPC or data center, never used to train public models
- Shortens the time employees spend searching internal documentation
Observability & AI-Ops
aiAxonIQ
OpenTelemetry Observability on ClickHouse, Built for High Cardinality
Per-host and per-custom-metric pricing makes high-cardinality data such as user IDs, tenant IDs, and request paths expensive to keep, so teams drop the labels they need most during an incident. aiAxonIQ stores telemetry in ClickHouse, where cardinality costs disk and compute rather than a line item, and compresses it column by column. Go collectors stream OTLP data through a Kafka buffer that absorbs ingestion spikes, Prophet models forecast latency and capacity trends, and NL→SQL turns a question into ClickHouse SQL you can read and edit.
Capabilities
- OpenTelemetry-Native Ingestion. Go workers receive OTLP logs, metrics, and traces and write them to ClickHouse, with OpenSearch available for full-text log search.
- Natural Language to SQL (NL→SQL). Ask “show error spikes in checkout during the last deploy” and get the generated ClickHouse SQL alongside the result.
- Forecasting & Anomaly Detection. Seasonality-aware Prophet forecasts flag latency regressions and capacity trends before they breach an SLO.
- Multi-Tenant Isolation. Role-based access control and tenant-scoped ClickHouse partitions for shared and dedicated deployments.
- Distributed Trace Waterfalls. Span-level views that show which microservice hop added the latency, across Kubernetes and cloud services.
- High-Cardinality Columnar Storage. Keep user, tenant, and request-level attributes on every span without per-series billing.
Integrations
- OpenTelemetry (OTLP)
- ClickHouse
- Apache Kafka
- OpenSearch
- Prometheus
- Grafana
- Kubernetes
- AWS CloudWatch
Targets
- Event Ingestion Rate
- 1.2M / sec
- Observability Cost Savings
- 65%
- Query Response Time (p95)
- < 80ms
- Mean Time to Detect
- -70%
Design targets and benchmarks, validated with each customer during a pilot.
Why Teams Choose It
- Targets 60%+ lower observability spend than per-host SaaS monitoring pricing
- Flags regressions from forecast trends, not only static thresholds
- Lets any engineer query telemetry in plain English or SQL
Open-Source Continuous Compliance (GRC)
trustlynce
Continuous Compliance Your Engineers Can Run
Auditors increasingly expect evidence that controls work all year, not screenshots taken the week before fieldwork. trustlynce covers the GRC lifecycle (risk registers, controls, vendor risk, privacy assessments such as DPIA and TIA, access reviews, and audit programs) and connects to AWS, GCP, Azure, GitHub, and Okta to collect evidence on a schedule. Its 270+ Model Context Protocol (MCP) tools let LLM agents and CI/CD jobs query posture, gather evidence, and draft policies for a person to approve.
Capabilities
- Full GRC Lifecycle. Risk registers, control matrices, vendor assessments, privacy assessments, and access review campaigns in one system.
- 270+ Model Context Protocol (MCP) Tools. Any MCP-compatible agent can query policies, collect evidence, draft risk reports, and route approvals.
- Web, CLI, GraphQL & n8n. Manage compliance from the web console, the `tly` CLI, the GraphQL API, or n8n workflow nodes.
- Verifiable Audit Trail. Cryptographically verifiable evidence records, policy-based RBAC, and electronic signatures, exportable as auditor-ready packages.
- Vendor & Third-Party Risk. Automated website security checks, DPA and BAA contract tracking, and subprocessor risk profiles.
- Public Trust Portal. Share your compliance status and security documents with prospective customers, with NDA-gated access.
Integrations
- Model Context Protocol (MCP)
- AWS
- Google Cloud
- Microsoft Azure
- GitHub
- GitLab
- Okta
- Jira
- Slack
- n8n
Targets
- MCP Tools
- 270+
- Faster Audit Preparation
- 4x
- Framework Packs
- SOC 2, ISO, HIPAA
- Open Source License
- MIT
Design targets and benchmarks, validated with each customer during a pilot.
Why Teams Choose It
- Keeps audit evidence current all year instead of once per audit
- Lets AI agents prepare evidence and drafts while people approve
- Self-host with Docker to keep compliance data in your environment
Interoperability
How the Products Work Together
Each product is independently deployable. Run two or more and these integrations switch on.
TrustIvan → aiAxonIQ
Vulnerability and policy events are emitted as OpenTelemetry logs, so they appear next to the traces they affect.
trustlynce → MITHUNAI
MITHUNAI agents call trustlynce MCP tools to check compliance posture and draft policy updates for review.
MITHUNAI → TrustIvan
Fix pull requests drafted by AI agents must pass TrustIvan scans and policy before they can merge.
Shared Sign-On
One identity provider (Okta, Microsoft Entra ID, Google) with role-based access across all four products.
The Lionhouse Platform
One Contract. One Sign-On.
Every Product.
With the Enterprise Suite you do not manage four vendor accounts or write glue code. The products share authentication, policy decisions, telemetry, and compliance evidence out of the box.
Shared Sign-On & Roles
SAML 2.0 or OIDC (Okta, Microsoft Entra ID, Google Workspace), with roles applied across all four products.
Private Data Plane
No cross-tenant data pooling. Telemetry, findings, and embeddings stay in your dedicated tenant or your own infrastructure.
MCP Between Products
Model Context Protocol endpoints let MITHUNAI agents read AppSec findings and compliance status, within the scopes you grant.
Talk to an Architect
See the Four Products Running in Your Environment.
A technical session with a Lionhouse architect covering your stack, your deployment model, and a pilot plan.