01
Intake
Import findings from AI hunters, scanners, red teams, PSIRT queues, bug bounty reports, or external assessments while preserving source, scope, owner, assets, and raw evidence.
365UI — Private AI Systems for High-Stakes Workflows
Turn enterprise knowledge and security findings into governed AI action.
Superagent and OrgMem power private enterprise knowledge workflows, while BMC Red-Team Lab and Ironclad apply the same traceability, approvals, evidence, and audit discipline to AI-infrastructure security.
Powered by reusable platforms. Delivered by forward-deployed engineers inside the customer’s real environment.
1M+ / 778K+
Delivered vector and document scale
137s → 6.6s
Delivered retrieval benchmark
268
Public-safe BMC security skills
7 / 10
Assessment lanes and vendor / family packs
Finding-to-Fix Workflow
Not a report generator. A state machine that turns unstable AI output into engineering-consumable work.
365UI receives findings from AI tools, scanners, red teams, PSIRT, and external assessments, then drives evidence validation, approval gates, engineering handoff, fix regression, and defensive pairing.
02
Normalize
Deduplicate, classify, and grade trigger path, impact, disclosure route, priority, and next experiment so noise does not become a metric.
03
Validate
Run RE queries, reachability checks, reproduction conditions, false-positive checks, and lab attestation. Risky actions stay behind approval gates.
04
Handoff
Create engineering-readable evidence packs, fix briefs, risk notes, test commands, and code-owner handoff materials.
05
Verify Fix
Run reattack gates, regressions, merge-ready checklists, and post-fix proof that the original path is blocked.
06
Defend
Pair Sigma / SIEM notes, negative corpus, cleanup verification, and audit trail so remediation and defense close together.
invariant: no raw AI claim ships without evidence
invariant: no risky validation runs without lab attestation
Customer Deliverables
Customers buy operational evidence packs, not a demo of smart AI.
Every deliverable maps to an engineering action and acceptance criterion: who owns it, how it reproduces, what proves the fix, how defenders detect it, who approved it, and where evidence lives.
Evidence Pack
Reproduction conditions, trigger path, blast radius, evidence files, confidence, gaps, and next experiment.
Engineering Brief
Minimal fix suggestion, test command, risk note, and regression condition the code owner can act on.
Approval Matrix
Allowed / restricted / prohibited action matrix that keeps AI tools inside approved customer boundaries.
Fix Verification
Reattack gate plus regression proving the original path is blocked without weakening safety gates.
Detection Notes
Sigma / YARA / IOC / query rule drafts with benign false-positive validation.
Audit Trail
Who approved what, what ran, where evidence lives, and how status changed for PSIRT and compliance.
Platform
Turn private AI capability into operable, auditable high-stakes workflows.
Superagent, OrgMem, BMC Red-Team Lab, and Ironclad provide the reusable product core. Forward-deployed engineers connect that core to each customer’s data, infrastructure, controls, and acceptance criteria.
Reusable module
Private AI runtime
Superagent combines private retrieval, agent orchestration, document processing, and replaceable infrastructure into a reusable runtime.
Reusable module
Organizational memory and knowledge governance
OrgMem preserves traceable organizational experience through versioned knowledge, reviewed writes, and tiered agent memory.
Reusable module
AI-infrastructure security validation
BMC Red-Team Lab productizes controlled validation, reproducible evidence, engineering handoff, and remediation paths.
Reusable module
Guardrailed infrastructure control
Ironclad constrains infrastructure actions with read-first workflows, risk gates, confirmations, and audit evidence; it is currently a PoC.
Reusable module
Multi-user agent operations
Deployment and hardening modules around third-party agent runtimes cover onboarding, observability, backup, and sandbox controls.
Reusable module
Evaluation, governance, and human approval
Deterministic fixtures, replay, approval gates, audit records, and engineering handoff keep systems within measurable acceptance boundaries.
Proof of Work
Customers do not buy “smart AI”; they buy governed workflows that operate.
Starting with the product core, delivery maps to real business and engineering actions: grounded retrieval, governed tool use, approvals, evidence, handoff, and audit.
Read full case studiesCurated capability proof
Real-time, operator-controlled AI workflows
A bilingual live-interpretation workflow uses operator controls, failure handling, deterministic fixtures, and evidence-backed acceptance gates; it does not claim event deployment, accuracy, or latency.
Book 365UI Live InterpretationCurated capability proof
Secure multi-user agent deployment and hardening
Multi-user deployment and hardening around third-party agent runtimes cover onboarding, observability, backup, and sandbox controls; no base-runtime ownership, production deployment, or completed-roadmap claim is made.
Curated capability proof
GPU-efficient retrieval, embedding, and reranking operations
Service integration, tests, and operational optimization around upstream models support reusable retrieval, embedding, and reranking workflows; no proprietary-model or unpublished performance claim is made.
Superagent Deep Dive
An enterprise-grade private AI answer engine — beyond generic chat.
Superagent turns fragmented enterprise knowledge into reliable private RAG and agent workflows. It is purpose-built for scenarios where answers require multiple evidence sources: documents, web pages, product catalogs, tickets, structured databases, and operational data.
The core advantage is full control. A FastAPI / LangGraph runtime connects hybrid vector + keyword retrieval, Qwen3 embedding / reranking, document parsing, evaluation, and observability; models, vector stores, search engines, and connectors remain replaceable and tunable.
Request path
1Source capture
2Document conversion
3Semantic chunking
4Embedding + indexing
5Hybrid search
6Rerank + expand
7Tool/data injection
8Answer with trace
Knowledge ingestion
Crawl websites, parse PDFs and Office files, normalize HTML tables, enrich chunks with context, and keep indexes fresh as source data changes.
Hybrid retrieval
Combine semantic vector search, keyword search, RRF fusion, reranking, metadata filters, and parent/sibling expansion so answers come from the right evidence.
Agent runtime
Expose RAG and tool-using agents through an OpenAI-compatible API, with task-specific models, structured tool calls, and a single chat/completions entry point.
Structured context
Inject live product, inventory, ticket, CRM, SQL, or operational data into the answer path instead of relying only on static documents.
Evaluation loop
Trace every retrieval and generation step, compare answer quality, inspect failed recalls, and improve chunking, ranking, prompts, and tools over time.
Private deployment
Run inside a customer-controlled environment with replaceable models, vector stores, search engines, and data connectors.
OrgMem Deep Dive
Organizational memory for the AI era — far beyond a traditional wiki.
OrgMem puts organizational knowledge, decision history, and agent execution memory into one auditable system. It is both a knowledge base and a long-term memory layer: humans keep governance, while agents gain structured read/write access and continuous learning.
Memory evolution path
Episode → Pattern → Strategy → Playbook
Failures are remembered, successes are reinforced, and repeatedly verified experience becomes durable organizational know-how.
Versioned Knowledge Base
Knowledge lives in Markdown + Git — not locked into a proprietary database or SaaS. Every change is tracked, auditable, and supports branching and review.
AI Captures, Humans Approve
AI continuously captures and distills execution insights, but formal organizational knowledge requires human review before entering the knowledge base — ensuring accuracy and trust.
Multi-Signal Precision Search
Semantic retrieval and exact keyword matching work together with multi-stage reranking and entity boosting — balancing conceptual understanding with terminology precision for real enterprise knowledge.
Agent Execution Memory
More than document search. OrgMem records execution traces, failure analyses, effective strategies, and team preferences so AI assistants continuously learn from experience.
Tiered Memory Architecture
Current-task knowledge is instant, project-level strategies preload at session start, and cross-project knowledge is retrieved on demand — minimizing noise and maximizing relevance.
Knowledge Lifecycle Management
Confidence scores, usage frequency, and recency govern knowledge validity. Repeatedly verified high-value patterns are automatically promoted into formal playbooks.
BMC Red-Team Lab · Latest
An AI-native autonomous vulnerability discovery and validation platform for server BMCs.
268 specialized skills route across 7 assessment lanes and 10 vendor / family packs — Unauth-DAST, Web UI, Redfish privilege mapping, IPMI / OEM, iKVM, firmware RE, supply-chain SBOM, OpenBMC / libpldm collaboration, and QEMU emulated validation. A multi-model Council pits Hunter (Claude Opus 4.8) against Skeptic (GPT-5.5) on every candidate, with a deterministic Governor making the Fail-Closed final call.
Every finding climbs a Proof Ladder and evidence-priority model — static candidate → deployed daemon reachability → QEMU full-system / lab_validation_emulated → approval-lease-gated real-hardware validation — and every offensive primitive ships with a paired Sigma detection rule. The latest pass adds BMC-TLO multi-step emulation benchmarks, automatic chain construction, fleet-scale dedupe and throughput SLOs, field journals, tool-index preflight, and redaction / poisoning scans. The OpenBMC libpldm decode_get_types_resp() report is in upstream fix collaboration.
268 specialized skills7 assessment lanes10 vendor / family packsMulti-model Council debateQEMU emulated validationBMC-TLO benchmarkProof LadderPaired Sigma defensesOpenBMC upstream confirmedGlobal PANIC halt
Product-powered forward deployment
Connect reusable platforms to the customer’s real environment.
01
Diagnose
Define one workflow, environment, owner, data boundary, and success metric.
02
Pilot
Use a fixed scope, fixed acceptance criteria, and reusable platform components to validate the system.
03
Deploy
Connect customer-controlled infrastructure, integrations, approvals, migration, and engineering handoff.
04
Operate
Continue evaluation, governance, observability, upgrades, and workflow expansion.
Customer-specific integrations stay isolated; reusable engineering patterns, tests, and controls improve the platform without sharing customer data.
FAQ
What enterprise teams usually ask before starting.
How is this different from scanners or pentests?
Scanners and pentests produce findings. 365UI turns findings into work engineering can consume, leadership can track, and defenders can detect.
Can it connect to our existing finding sources?
Yes. AI hunters, scanners, red-team reports, PSIRT cases, bug bounty reports, and external assessments can all enter the same evidence validation and engineering fix workflow.
Why start with BMC?
BMC / firmware is high-risk, difficult, and technical enough to prove 365UI can handle real evidence, approvals, engineering handoff, and paired defense. Once hardened there, the workflow can move into OT, kernel drivers, cloud control planes, and AI agent operations.
Why buy now?
AI security discovery tools will create more suspected issues. The missing layer is a private, controlled, auditable finding-to-fix pipeline.
What are 365UI’s two product pillars?
Superagent + OrgMem serve enterprise private AI, retrieval, agents, and organizational memory. BMC Red-Team Lab + Ironclad serve AI-infrastructure security and finding-to-fix evidence workflows. They are equal pillars that share the same discipline for traceability, approvals, evidence, and audit.
How is OrgMem different from a traditional wiki?
OrgMem stores more than documents. It captures decision history, execution lessons, failure analysis, effective strategies, and agent memory, then promotes verified experience into official organizational knowledge through human review and versioning.
Can agents be pushed into high-stakes specialized domains beyond chat — and how do you defend against AI-driven 0-day tooling?
Yes — BMC Red-Team Lab is the proof. 268 specialized skills route across 7 assessment lanes and 10 vendor / family packs, with a multi-model Council debating every candidate (Hunter = Claude Opus 4.8, Skeptic = GPT-5.5, Governor = deterministic Fail-Closed code). Findings move through a Proof Ladder and evidence-priority model from static candidate to daemon reachability, QEMU full-system / lab_validation_emulated, then approval-lease-gated real-hardware validation. The system now includes BMC-TLO multi-step benchmarks, fleet-scale dedupe/SLOs, field journals, tool-index preflight, and paired Sigma defenses. The OpenBMC libpldm report is acknowledged upstream by the OpenBMC security team.
Turn knowledge and security findings into governed AI action.
Bring one enterprise knowledge workflow or security finding source, a clear scope, and a business owner. We start with a verifiable private AI pilot or evidence pack, then scale into a long-running control plane.