Your team’s critical knowledge is trapped in Confluence, SharePoint, and custom wikis. Finding answers wastes hours and frustrates employees. We build secure AI copilots that connect to all your data silos, creating a single point of access.
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Implementation scope and rollout planning
Clear next-step recommendation
Transform your static documentation into an intelligent, conversational AI assistant that delivers instant answers.
Your team’s critical knowledge is trapped in Confluence, SharePoint, and custom wikis. Finding answers wastes hours and frustrates employees. We build secure AI copilots that connect to all your data silos, creating a single point of access.
Deploy a secure, conversational interface in weeks, not months, with 99.9% uptime SLA and full data sovereignty.
Active Directory or Okta.This is not a generic chatbot. We use advanced Retrieval-Augmented Generation (RAG) infrastructure and fine-tuned models to ensure minimal hallucination and maximum accuracy on your proprietary data. Explore our approach to Retrieval-Augmented Generation (RAG) Infrastructure for scalable, accurate systems.
Enable your team to focus on execution, not search. For a unified view across all enterprise data, see our Enterprise Search and Retrieval AI service.
Our AI integration transforms static documentation into a dynamic intelligence layer, delivering quantifiable improvements in operational efficiency and decision-making speed.
Deploy a conversational AI interface that allows employees to query tribal knowledge and procedural documents in natural language, cutting information retrieval time from minutes to seconds. We integrate with your existing Confluence, SharePoint, or proprietary wiki using secure, API-first connectors.
Surface critical, underutilized documentation locked in legacy systems. Our Retrieval-Augmented Generation (RAG) infrastructure ensures answers are grounded in your authoritative sources, dramatically reducing reliance on tribal knowledge and inconsistent answers.
Accelerate new hire ramp-up and ensure consistent policy adherence. AI copilots provide instant, context-aware answers to procedural questions, reducing training overhead and mitigating compliance risks from outdated or misinterpreted guidelines.
Connect AI intelligence to bespoke ERPs, custom databases, and niche software without costly migrations. Our overlay engineering creates a unified conversational layer, preserving your core investment while unlocking modern AI capabilities. Learn more about our approach to Legacy ERP AI Copilot Integration.
Activate dark data from scanned PDFs, meeting notes, and old file shares. Our multimodal pipelines extract structured insights, creating a searchable corporate memory that informs strategy and identifies process gaps previously hidden in unstructured formats.
A clear breakdown of the phased approach and key outputs for integrating an AI copilot with your internal knowledge bases (Confluence, SharePoint, wikis).
| Phase & Key Deliverables | Timeline | Starter | Enterprise |
|---|---|---|---|
Phase 1: Discovery & Architecture | 1-2 weeks | ||
Technical Requirements Document | |||
Data Source Integration Plan | |||
Security & Compliance Review | Basic | Comprehensive (ISO 42001) | |
Phase 2: Core RAG Pipeline Development | 2-3 weeks | ||
Semantic Chunking & Embedding Strategy | |||
Vector Database Setup & Indexing | Single Source | Multi-Source Federated | |
Basic Conversational UI (Web Interface) | |||
Phase 3: Advanced Features & Integration | 1-2 weeks | Limited | Full Suite |
Multi-Knowledge Base Cross-Referencing | |||
Live Meeting Integration (Teams/Slack) | |||
Audit Trail & Usage Analytics Dashboard | |||
Phase 4: Testing, Deployment & Handoff | 1 week | ||
Hallucination & Accuracy Validation Testing | Standard | Rigorous (Adversarial) | |
On-Prem / VPC Deployment | |||
Admin Training & Technical Documentation | |||
Ongoing Support & Model Tuning | Post-Launch | SLA with Dedicated Engineer | |
Total Estimated Project Timeline | 4-6 weeks | 6-8 weeks |
We deploy a structured, four-phase methodology designed for enterprise security and rapid time-to-value. This ensures your AI copilot integrates seamlessly with existing knowledge bases while meeting strict compliance and performance SLAs.
We conduct a comprehensive audit of your existing knowledge repositories (Confluence, SharePoint, wikis) to identify data silos, access patterns, and security requirements. This phase establishes the semantic foundation for accurate, context-aware retrieval.
Our engineers build encrypted, air-gapped data pipelines to ingest and pre-process your proprietary documentation. We implement semantic chunking, vectorization, and metadata enrichment to optimize for Retrieval-Augmented Generation (RAG) accuracy without data leaving your environment.
We architect and deploy a high-precision RAG system, integrating with leading vector databases (Pinecone, Weaviate) and fine-tuning domain-specific language models (DSLMs) on your corpus. This drastically reduces hallucination rates and ensures answers are grounded in your trusted sources.
We deploy the integrated AI copilot into your production environment with full monitoring, analytics, and feedback loops. Our team provides ongoing tuning based on user interactions and new data, ensuring performance improves over time. Explore our broader approach to Enterprise AI Copilot Customization.
We build AI systems for teams that need search across company data, workflow automation across tools, or AI features inside products and internal software.
Common questions about integrating AI copilots with Confluence, SharePoint, and proprietary wikis to unlock tribal knowledge.
Standard deployments take 2-4 weeks from kickoff to production-ready pilot. Timeline depends on data source complexity (e.g., Confluence vs. custom wikis), volume of documents, and required security controls. We follow a phased approach: 1-week discovery, 1-2 weeks for core RAG pipeline development, and 1 week for security hardening and user acceptance testing.
5+ years building production-grade systems
We look at the workflow, the data, and the tools involved. Then we tell you what is worth building first.
The first call is a practical review of your use case and the right next step.