Knowledge Spaces

Typical pilot to production timeline is 4 weeks, including platform configuration, data ingestion, bot setup, and user acceptance testing. Most organizations are running their first production bot within the first week.

We start with a discovery session to understand your data sources, use cases, and governance requirements. From there we configure your Space, ingest your documents, set up bots with appropriate guardrails, and run a pilot with your team. After validation, we transition to production with full RBAC, SSO, and audit logging enabled. Ongoing managed service includes monitoring, model updates, and support.

We support 16+ foundation models across all major providers – OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Groq, and xAI. The platform is model-agnostic by design: you choose which models to enable, and our routing engine selects the best model per query based on your policies, cost constraints, or performance requirements. As new models become available, the platform incorporates them without disrupting existing workflows.

Each bot can be configured with one or more allowed models. The platform can route queries based on rules you define – for example, using Groq for fast, simple lookups and Claude for complex reasoning tasks. You can also set model preferences per Space, per bot, or let the system auto-select based on query complexity. This ensures you’re never locked into a single vendor and can optimize for speed, cost, or accuracy.

When you upload documents (PDFs, DOCX, TXT up to 100MB), they’re chunked, embedded via Pinecone vector embeddings, and indexed for semantic search. When a user asks a question, the system retrieves the most relevant document passages, passes them as context to the selected LLM, and generates an answer grounded in your actual data. Answers include citations back to source documents, so users can verify and trust the output.

Consumer brands use Knowledge Spaces to power customer support bots, product recommendation engines, and self-service knowledge bases. Because every bot is backed by your actual product documentation and policies, customers get accurate, on-brand answers – not generic AI responses. You can deploy via an embeddable chat widget on your website, integrate through our API, or use the standalone interface. One health technology client serves thousands of end users through Knowledge Spaces-powered wellness bots.

B2B organizations use Knowledge Spaces to centralize institutional knowledge, accelerate onboarding, and scale expertise across teams. Common use cases include internal knowledge assistants for consulting teams, client-facing portals with governed access to deliverables, proposal research tools that search across past work, and compliance bots that provide instant guidance on regulations. The multi-tenant architecture means you can isolate client data while sharing platform infrastructure.

Yes. Knowledge Spaces exposes a full API that other AI systems can call programmatically. This enables agentic workflows where one AI agent queries a Knowledge Spaces bot as a tool – for example, a planning agent retrieving policy guidance from a compliance bot, or a reporting agent pulling data summaries from an analytics bot. Each API call is governed by the same RBAC, guardrails, and audit logging as human interactions, ensuring safe AI-to-AI communication.

Our Compliance Lab deployment serves compliance consulting firms with dedicated FAR, CAS, and DFARS knowledge modules containing 15+ MB of structured regulatory data. FARbot, our public-facing compliance assistant, is built on Knowledge Spaces with configurable model selection, guardrail policies, and outputs traceable to source evidence. Every answer includes citations, and multi-tenant isolation ensures firm-to-firm data separation with full audit trails.

FedRAMP authorization is in progress. The platform is currently deployed on GovCloud-ready infrastructure with FISMA-aligned security controls including SAML 2.0 SSO, CAC/PKI authentication, 4-tier RBAC, and 64+ audit event types. We are actively pursuing FedRAMP authorization and welcome agency sponsors.

Knowledge Spaces includes a configurable guardrail engine with PII detection, prompt injection prevention, and content moderation. Administrators can define allowed topics, disallowed outputs, mandatory disclaimers, and evaluation gates – all through plain-language configuration, no code required. Every guardrail action is logged. Administrators retain full control over what the AI can and cannot discuss, with complete transparency into every enforcement action.

Yes. We support on-premises and air-gapped deployment for classified or restricted environments. The platform can run entirely within your infrastructure on AWS, Azure, GCP, or bare metal. For defense and intelligence clients, we offer deployment architectures that operate without external internet access while maintaining full functionality.

Knowledge Spaces captures 64+ distinct audit event types covering every meaningful platform action – user logins, document uploads, bot queries, model selections, guardrail triggers, permission changes, and more. All events are timestamped and attributed to specific users. Audit logs can be exported for compliance reporting, DCAA reviews, or integration with your existing SIEM/logging infrastructure.

Knowledge Spaces includes 15+ data connectors out of the box, including Salesforce, PostgreSQL, MySQL, REST APIs, and OAuth-based integrations. You can also upload documents directly (PDF, DOCX, TXT up to 100MB). For enterprise deployments, we build custom connectors to your internal systems – ERPs, CRMs, data warehouses, SharePoint, and legacy databases.

A Space is a governed container for knowledge – it holds documents, connector configurations, model settings, and governance rules. Within each Space, you configure one or more Bots, each with its own system instructions, allowed models, and access controls. Sharing Controls determine who can access each bot – internal teams, external clients, or the public. This layered architecture lets you run multiple distinct AI use cases from a single platform while maintaining strict data isolation.

Yes. Knowledge Spaces provides a RESTful API for programmatic access to all platform capabilities – querying bots, managing documents, configuring Spaces, and retrieving analytics. The API supports streaming responses, tool calling, and JSON mode. This enables integration into existing applications, automated workflows, and AI-to-AI communication patterns where other systems call Knowledge Spaces as an intelligent backend.

Yes. Knowledge Spaces supports bring-your-own-key (BYOK) so you can use your existing OpenAI, Anthropic, or Google API accounts for direct billing and cost control. Alternatively, Sprinklenet can provide model access with transparent usage pass-through pricing. This flexibility lets you manage AI costs exactly how your finance team prefers.

Yes – this is one of Knowledge Spaces’ most powerful features. You can share a Space with external organizations in read-only mode, allowing their bots to query your knowledge without accessing the underlying documents. For example, a private equity firm can share portfolio intelligence across companies, or an enterprise brand can share marketing insights with agency partners – all with clear data boundaries and governance controls.

Yes. The platform includes interaction analytics (usage metrics, engagement tracking, time-on-task), quality monitoring (hallucination risk detection, output quality scoring, model drift alerts), and performance dashboards (latency, availability, concurrent user metrics). For advanced use cases, our AI HUB intelligence overlay adds natural language-driven visualization, trend analysis, and multi-source data exploration – all queryable by non-technical users.

Every RAG-powered response includes paragraph-level citations back to the source documents that informed the answer. Users can click through to see the exact passage the AI referenced, building trust and enabling verification. For compliance-sensitive environments, this traceability is critical – auditors and reviewers can confirm that every AI output is grounded in approved source material, not hallucinated.

Knowledge Spaces is built for enterprise scale. Our production deployments manage tens of thousands of concurrent users across dozens of independently configured AI endpoints. The platform scales horizontally with containerized infrastructure, so capacity grows with your needs. There is no hard limit on the number of Spaces, bots, or users per deployment.

Yes. The embedded chat widget is fully customizable – colors, logos, button styles, and behavior. Partners and clients can wrap their own branded UI around the core Knowledge Spaces intelligence. For government clients, we support USWDS (U.S. Web Design System) component alignment. The result is a seamless branded experience for end users, powered by Knowledge Spaces behind the scenes.

Yes. Beyond conversational Q&A, Knowledge Spaces supports structured input/output workflows, rules-based approval routing, multi-step processes with human review gates, batch report generation, and event-driven triggers. For example, an invoice approval workflow can route documents through AI analysis, flag anomalies, and escalate to the appropriate reviewer – all governed by the same RBAC and audit controls.

Knowledge Spaces is a governed enterprise platform – not a consumer chatbot. Consumer tools like ChatGPT and Copilot don’t offer multi-tenant data isolation, role-based access control, comprehensive audit logging, document-grounded RAG with citations, configurable guardrails, or multi-model orchestration. Knowledge Spaces gives organizations control over what the AI knows, what it’s allowed to say, and who can access it – critical requirements for enterprise and government use that consumer products aren’t designed to address.

Building a production RAG system from scratch typically takes 3 – 6 months and requires expertise in vector databases, embedding models, LLM APIs, authentication, logging, and deployment. Knowledge Spaces provides all of this out of the box as a managed service, with enterprise-grade security and governance already built in. You get to production in weeks instead of months, with ongoing platform maintenance, model updates, and support included. For teams that have already built custom pipelines, Knowledge Spaces can serve as the governance and orchestration layer on top of your existing infrastructure.