365UI — Private AI Systems for the Enterprise
Superagent is the flagship 365UI platform: a self-hostable enterprise knowledge Q&A and intelligent workflow system, already running in an S&P 500 production environment — for teams that demand answer accuracy, workflow auditability, and strict data privacy.
Platform
365UI connects private knowledge, business systems, and daily workflows to a controllable AI runtime so teams can search, reason, act, and retain know-how safely.
Enterprise Private Knowledge Platform
A private AI layer for teams that need precise answers from messy documents, product data, tickets, databases, and internal workflows — with full control over data and infrastructure.
Organizational Memory & Knowledge Retention
Make team experience and decision history trackable, auditable, and reusable. AI continuously distills execution insights; human review gates what enters the official knowledge base.
Intelligent Email Assistant
A private assistant for high-volume inboxes: daily highlights, smart triage, attachment understanding, semantic email search, and draft replies.
Realtime Voice Interaction
An always-on voice assistant for home and edge devices with reminders, ambient awareness, realtime conversation, and cross-system actions.
Workflow Automation Agents
Turn repetitive expert work into auditable automated workflows: code quality remediation, candidate screening, report generation, and cross-system operations.
Model Selection & Inference Deployment
Comparative evaluation of 40+ leading LLMs, embedding, and reranking models, plus high-performance inference deployment on GPU clusters.
AI Team Governance
A governance framework for human-AI teams that standardizes assistant rules, decision records, and lessons learned — preventing context drift when multiple people and AI agents collaborate.
AI-native BMC autonomous vulnerability discovery & validation
A closed-loop AI red-team platform for server BMCs: 232 specialized skills routed across 7 deep assessment lanes, driven by a multi-model Council (Claude Opus 4.7 Hunter + GPT-5.5 Skeptic + a deterministic-code Governor). Every finding climbs a Proof Ladder — static candidate → daemon reachability → lab reproduction → exploitability — and every offensive primitive ships with a paired Sigma detection rule. The OpenBMC `libpldm decode_get_types_resp()` report has been acknowledged by the OpenBMC security team upstream.
Proof of Work
Every 365UI product comes from real delivery experience — S&P 500 enterprise AI platforms, million-scale document retrieval, workflow automation, LLM inference services, messaging assistants, and edge voice products.
Read full case studiesLatest · OpenBMC upstream acknowledged
An AI-native autonomous vulnerability discovery and validation platform for server BMCs: 232 specialized skills routed across 7 deep assessment lanes, with a multi-model Council debating every candidate — Hunter (Claude Opus 4.7, Thinking) vs Skeptic (GPT-5.5, Extra Reasoning), with a deterministic-code Governor (Fail-Closed) making the final call. Every finding climbs a Proof Ladder (static candidate → deployed daemon reachability → lab reproduction → exploitability) and every offensive primitive ships with a paired Sigma detection rule. The OpenBMC `libpldm decode_get_types_resp()` report has progressed from a source-level OOB read to deployed pldmd reachability, controlled fake MCTP peer-path evidence, and a candidate fix — acknowledged upstream by the OpenBMC security team.
Delivered to S&P 500 enterprise
A complete private AI assistant platform covering multi-source data ingestion (web, SharePoint, PDF/Office), multi-tenant configuration, and zero-code deployment across industries — running in production today.
Million-scale document search
High-precision hybrid search over 1M+ vectors and 778K+ documents, combining semantic retrieval, keyword matching, multi-stage reranking, and multi-hop reasoning with agent-coordinated tools including database queries and web search.
Measurable business impact
End-to-end retrieval and generation pipeline optimized for production readiness: response latency reduced from 137 seconds to 6.6 seconds, data import throughput improved by 260x.
Code quality + recruiting efficiency
Large-scale operational automation: a Code-Fix Agent that resolved 12,000+ code quality issues automatically, and an AI Recruiter that extracts screening criteria from job descriptions and generates candidate shortlists on demand.
Model selection and inference deployment
Comparative evaluation of 40+ leading LLMs, embedding, and reranking models, with optimized inference deployment on H100/H200/B300-class GPU clusters.
Multi-person, multi-agent team standards
An open-source governance framework for human-AI teams, standardizing assistant rules, architecture decision records, and lessons learned to prevent rule sprawl and context drift in multi-agent collaboration.
AI product in a closed ecosystem
A production AI assistant deployed inside a closed messaging ecosystem with multimodal chat, intent routing, web search, finance data, deep research, group analytics, content moderation, and an API gateway.
Ambient intelligence prototype
A 24/7 voice assistant deployed on edge devices with full-duplex realtime conversation, persistent memory, scheduling, do-not-disturb management, and camera-based ambient awareness.
Superagent Deep Dive
Superagent transforms fragmented enterprise knowledge into reliable AI workflows, with proven deployment inside an S&P 500 production environment. It is purpose-built for scenarios where answers require multiple evidence sources: documents, web pages, product catalogs, tickets, structured databases, and live operational data.
The core advantage is full control. Every layer — data ingestion, document parsing, index building, retrieval ranking, LLM, and toolchain — is independently replaceable and tunable, so enterprises can deploy privately and continuously optimize answer quality on their own terms.
Request path
Crawl websites, parse PDFs and Office files, normalize HTML tables, enrich chunks with context, and keep indexes fresh as source data changes.
Combine semantic vector search, keyword search, RRF fusion, reranking, metadata filters, and parent/sibling expansion so answers come from the right evidence.
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.
Inject live product, inventory, ticket, CRM, SQL, or operational data into the answer path instead of relying only on static documents.
Trace every retrieval and generation step, compare answer quality, inspect failed recalls, and improve chunking, ranking, prompts, and tools over time.
Run inside a customer-controlled environment with replaceable models, vector stores, search engines, and data connectors.
OrgMem Deep Dive
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.
Knowledge lives in Markdown + Git — not locked into a proprietary database or SaaS. Every change is tracked, auditable, and supports branching and review.
AI continuously captures and distills execution insights, but formal organizational knowledge requires human review before entering the knowledge base — ensuring accuracy and trust.
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.
More than document search. OrgMem records execution traces, failure analyses, effective strategies, and team preferences so AI assistants continuously learn from experience.
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.
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
232 specialized skills routed across 7 deep assessment lanes — Unauth-DAST, Web UI, Redfish privilege mapping, IPMI / OEM, iKVM, firmware RE, supply-chain SBOM, and OpenBMC / libpldm collaboration. A multi-model Council architecture pits Hunter (Claude Opus 4.7 Thinking) against Skeptic (GPT-5.5 Extra Reasoning) on every candidate, with a deterministic-code Governor making the Fail-Closed final call.
Every finding climbs a Proof Ladder — static candidate → deployed daemon reachability → lab reproduction → exploitability — and every offensive primitive ships with a paired Sigma detection rule. The OpenBMC libpldm decode_get_types_resp() report has progressed from a source-level OOB read to deployed pldmd reachability, controlled fake MCTP peer-path evidence, and a candidate fix shape — acknowledged upstream by the OpenBMC security team. That is the real closed loop that counters Mythos-class AI 0-day tooling.
Capability Map
Ingest private knowledge from web pages, PDFs, Office files, email, databases, and APIs into a unified, searchable data layer
Multi-signal retrieval with precision reranking and structured data injection — answers are grounded in evidence, not guesswork
Agents handle multi-step tool calls, long-running workflows, memory, and access control for real business scenarios beyond single-turn chat
Fully self-hostable — data stays within your perimeter, suited for teams with strict data governance requirements
Built-in evaluation, trace logging, rollback, and version management for continuous improvement in production
Delivered across enterprise knowledge Q&A, process automation, organizational memory, team governance, recruiting, messaging platforms, and edge voice
Agent-native architecture pushed into high-stakes vertical domains — BMC Red-Team Lab: 232 routable skills across 7 deep assessment lanes, a multi-model Council (Hunter / Skeptic / deterministic Governor) debating every candidate, a Proof Ladder for evidence promotion, and paired Sigma defenses; the OpenBMC `libpldm` report is acknowledged upstream by the OpenBMC security team
Engagement
Pick one high-value knowledge domain and deliver a private AI assistant with measurable answer quality.
Connect multiple data sources with role-based workflows, review queues, and production-grade monitoring.
Integrate the Superagent runtime into your product, portal, support flow, or internal operations.
FAQ
365UI is not a generic chat account. It is an implementation system for private enterprise data and real workflows, with private deployment, hybrid retrieval, tool orchestration, audit traces, evaluation loops, and internal system integration.
Yes. Superagent is designed as a customer-controlled private AI runtime. Models, vector stores, search engines, and data connectors can be replaced to match the customer's infrastructure.
A clear workflow, real data, a business owner, and one success metric. 365UI starts with a working, measurable pilot, then expands into multi-source production workflows.
The answer path combines semantic search, keyword search, reranking, metadata filters, structured data injection, and evidence trace so responses are grounded in retrievable facts instead of free-form generation alone.
Teams with private documents, databases, tickets, email, product data, SOPs, or compliance content are a strong fit, especially support knowledge search, sales enablement, internal policy assistants, recruiting, reporting, and workflow automation.
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.
Yes — BMC Red-Team Lab is the proof. 232 specialized skills routed across 7 deep assessment lanes, with a multi-model Council debating every candidate (Hunter = Claude Opus 4.7 Thinking, Skeptic = GPT-5.5 Extra Reasoning, Governor = deterministic Node code, Fail-Closed). Every finding climbs a Proof Ladder from static candidate to deployed daemon reachability and exploitability evidence, and every offensive primitive ships with a paired Sigma detection rule. The OpenBMC libpldm report is acknowledged upstream by the OpenBMC security team. Countering Mythos-class AI 0-day tools relies on a self-hosted, 24/7 red-team loop — not external 0-day brokers.
All we need is one workflow, one dataset, and one success metric. We deliver a working pilot first, then scale to a full platform.
