Enterprise AI Infrastructure for Secure Knowledge Retrieval & AI Agents
The retrieval and orchestration layer that connects AI to your company's internal knowledge. Deploy on-prem, bring your own models, keep data inside your network.
For engineering, ops, and IT leaders who need AI that works with internal data - without compromising security or compliance.
Why enterprise AI adoption fails
Most companies can't deploy AI internally - not because the models aren't good enough, but because the infrastructure to connect AI to internal knowledge doesn't exist.
of AI projects fail to reach production
Gartner, 2023 ↗
average cost of a failed enterprise AI initiative
IBM Global AI Adoption Index, 2023 ↗
to build a custom internal RAG pipeline from scratch
a]16z Enterprise AI Report, 2024 ↗
of AI projects never make it past the pilot stage
MIT Sloan Management Review, 2022 ↗
Fragmented Company Data
Knowledge lives in Confluence, Drive, Slack, Notion, Jira, wikis, and dozens of internal tools. No single system connects it all.
Disconnected Systems
Teams use different tools with no shared context. AI can't help if it can't see across silos - and most tools only index one source.
Security & Compliance Blockers
Most AI tools require sending data to external APIs. Regulated industries and security-conscious orgs can't accept that risk.
Tribal Knowledge Loss
Critical know-how lives in people's heads. When they leave, institutional knowledge leaves with them - undocumented and unrecoverable.
Build it yourself vs. deploy Agora
| Internal Build | Agora | |
|---|---|---|
| Time to production | 8-15 months | < 1 week |
| Engineering headcount | 3-5 FTEs dedicated | 0 - managed infra |
| Retrieval accuracy | Low - constant tuning | Production-grade out of box |
| Ongoing maintenance | Embeddings drift, pipelines break | Automated re-sync & updates |
| Security posture | DIY audit & compliance | RBAC, audit logs, SOC2-aligned |
Agora is the missing retrieval & orchestration layer
Skip the 12-month build. Connect your data sources, deploy AI agents on your infrastructure, and go live in days - not quarters.
Measurable outcomes from day one
Backed by research from Microsoft, Stanford, and GitHub. AI-powered retrieval delivers ROI across every team.
Reduction in information search time
Employees find answers in seconds instead of digging through multiple tools and Slack threads.
Faster new employee onboarding
New hires ramp up in weeks instead of months with instant access to institutional knowledge.
Reduction in internal support load
AI agents handle repetitive HR, IT, and ops questions that currently overwhelm support teams.
Faster incident resolution
Engineers find runbooks, past incidents, and architecture docs instantly during outages.
AI enablement across teams
Every team gets AI agents scoped to their data - engineering, sales, HR, legal, ops - without building custom pipelines.
Annual knowledge loss in the US alone
Employee turnover and institutional knowledge drain cost enterprises trillions. AI retrieval captures and preserves it.
Deploy AI agents for every team
Each agent has scoped access to relevant data sources and follows your organization's rules and permissions.
Engineering Knowledge Assistant
Instantly find architecture decisions, API documentation, runbooks, and deployment guides scattered across repos and wikis.
Internal Support Automation
AI agents that answer HR, IT, and operations questions using internal policies and procedures. Reduces ticket volume significantly.
AI Onboarding Assistant
New hires get instant answers from onboarding docs, team wikis, and process guides. Accelerates ramp-up from months to weeks.
Enterprise Knowledge Search
Semantic search across all internal data sources. Find answers, not just links, with full source citations.
Sales & Customer Success
Agents with access to product docs, case studies, and pricing. Enables faster response to prospect and customer questions.
Compliance-Aware AI
Deploy AI with full audit trails, RBAC, and data residency controls. Enable AI adoption without compliance risk.
Enterprise retrieval and agent infrastructure
Deploy, ingest, retrieve, and orchestrate. Everything needed to run AI agents on your company's internal knowledge.
On-Prem Deployment
Deploy on your own hardware with a single Helm command. Air-gapped environments supported. Data never leaves your network.
Retrieval Pipeline
Hybrid retrieval combining dense vector search with BM25 full-text scoring. Configurable chunking strategies (sliding window, semantic boundary, parent-child). Cross-encoder reranking with reciprocal rank fusion. Incremental re-indexing on source change.
AI Agent Framework
Agents defined as config: system prompt, tool bindings, retrieval scope, and guardrails. Multi-step execution with tool-use chains (API calls, DB queries, webhook triggers). Scoped RBAC per agent. Parallel execution with shared context passing between steps.
Multi-Source Ingestion
Confluence, Google Drive, S3, Notion, Jira, and more. Automatic sync keeps everything current. Respects source permissions.
Model Flexibility
Use GPT, Claude, Gemini, Llama, Mistral, or any model. Switch providers without changing workflows. Run local models for full air-gap.
Enterprise Security
Per-request audit trail: user, agent, sources accessed, LLM call, latency, token count. RBAC at document, source, and agent level. SSO via OIDC/SAML. Encryption at rest (AES-256) and in transit (mTLS). Exportable compliance reports.
How Agora fits your infrastructure
End-to-end pipeline from data sources to AI agents, deployed entirely within your network perimeter.
Deployment flexibility for any security posture
On-prem, VPC, or managed cloud. Choose the deployment model that fits your compliance requirements.
On-Premises: Your Data Never Leaves Your Infrastructure
Full control. Deploy on your own hardware with a single Helm command. Air-gapped environments supported. Nothing leaves your network.
Multi-source enterprise ingestion
Connect the systems your organization already uses. Automatic sync respects permissions and keeps indexes current.
Confluence 
Notion 
Google Drive 
AWS S3 
GCP Storage 
Azure Storage 
WikiJS 
Upload Ingests all your content types
Bring your own LLM. No vendor lock-in.
Use cloud models, self-hosted open-source, or run fully local for air-gapped environments. Switch providers without changing your workflows.
Why Agora over alternatives
Unlike cloud-only solutions, Agora gives you full infrastructure ownership with no external data dependencies.
| Agora | Glean | MS Copilot | ChatGPT Ent. | |
|---|---|---|---|---|
| On-prem / air-gapped deployment | ||||
| Bring your own LLM | ||||
| Bring your own vector DB | ||||
| Custom agent workflows | ||||
| No vendor lock-in | ||||
| Customer-controlled encryption | ||||
| Multi-source enterprise ingestion | ||||
| Enterprise search |
On-prem / air-gapped deployment
Bring your own LLM
Bring your own vector DB
Custom agent workflows
No vendor lock-in
Customer-controlled encryption
Multi-source enterprise ingestion
Enterprise search
Agora is the only enterprise AI platform that gives you full infrastructure ownership. Deploy on-prem, bring your own models, and keep complete control over your data pipeline.
Deploy enterprise AI on your infrastructure
Run AI agents on your company's internal knowledge. Fully on-prem, no external dependencies, production-ready in minutes.
From the Blog
Insights on enterprise AI adoption, knowledge infrastructure, and operational efficiency
The Hidden Cost of Disorganization: How Fragmented Internal Documentation Drains Productivity
Employees spend an average of over 1.8 hours daily searching for and gathering information. This isn't just a minor inconvenience; it's a significant drain of productivity and resources.
The Hidden Cost of Onboarding New Employees
Welcoming new team members should energize an organization, yet onboarding frequently drains productivity rather than boosting it. Here's why it takes so long and what you can do about it.
Large Language Models (LLM) -- The Brains Behind Smarter and Faster Work
Artificial intelligence has evolved from science fiction to become an essential daily tool. Large Language Models represent a major driver of this transformation, enabling systems that can write code, summarize documents, and engage in human-like conversation.