Build an Enterprise AI Search Platform Like Glean

We can build the same architecture: unified multi-source search, permission-aware retrieval, dynamic knowledge graph, AI assistant, AI agents, verified answers, and knowledge governance. What we don't replicate is Glean's pre-built connector catalogue breadth or their operational scale across thousands of enterprise deployments. The build advantage is custom connectors for your specific systems, a knowledge graph modelled to your organisation, deeper agentic capabilities, and data that never leaves your infrastructure.

When your application landscape is standard SaaS (M365, Google Workspace, Salesforce, Slack, Jira), your connectors are in Glean's catalogue, your data residency allows cloud processing, and enterprise pricing fits your budget. Glean is a strong product with a proven track record. We'll tell you if it's the right answer during discovery - because building what you could buy is a waste of your engineering budget and ours. The build case starts where Glean's coverage ends.

The search system inherits permissions from each source application via your identity provider (Okta, Entra ID, Google Workspace). When a user searches, the system only returns documents that user has access to in the original source system. This enforcement happens at query time, not as a post-retrieval filter - so the system never retrieves, processes, or displays content the user shouldn't see. Permission synchronisation must stay current as people change roles, join teams, or leave. One permission error in enterprise search is a data exposure - which is why we test permission inheritance exhaustively before launch.

Yes - and this is the primary build case for most clients. Glean's connector ecosystem covers major SaaS applications. Legacy databases, proprietary platforms, industry-specific systems (EHR, case management, engineering PLM), and internal tools require custom connectors. We build connectors using standard protocols where available and custom integration where not, with the same permission inheritance and real-time synchronisation as standard connectors. If a system has an API, a database, or even a file export, we can connect it.

A knowledge graph maps the relationships between people, teams, documents, projects, and permissions in your organisation - maintained in real time as the organisation changes. It's what enables personalised relevance: a search ranking that understands a finance exec and an engineer need different documents for the same query. Glean's core innovation is this graph. Building one specific to your organisation means modelling YOUR relationships, org structure, and knowledge flows - rather than adapting to a generic schema. The graph is more valuable than the search interface on top of it.

Yes - and for many organisations this is the decisive factor. Glean offers zero-retention processing, meaning data isn't stored after indexing. But the data still transits through Glean's infrastructure during processing. For defence, government, legal, healthcare, and financial organisations where data cannot leave the perimeter under any circumstance, on-premise or air-gapped deployment is the only option. We also offer federated search - querying source systems directly at search time without building a centralised index - for environments with the strictest residency requirements.

Microsoft Copilot is strong for M365-heavy organisations - it searches across Word, Excel, PowerPoint, Teams, Outlook, and SharePoint natively. It's weaker across non-Microsoft applications. A Glean-style platform connects ALL your applications - Google Workspace, Salesforce, Jira, Slack, and your custom systems - with a unified knowledge graph spanning the full landscape. If your stack is 80%+ Microsoft, Copilot may be sufficient. If you run a mixed or non-Microsoft environment, a cross-ecosystem search platform delivers more.

Cost tracks the number of source systems, connector complexity (standard SaaS vs legacy/custom), knowledge graph depth, agentic capabilities, permission architecture complexity, deployment model (cloud vs on-premise), and scale (users and document volume). A single-department knowledge assistant is a different build from a full-enterprise search platform across 50 applications. We scope fixed pricing after the systems audit, because the connector development effort - which varies enormously by system - is the largest cost variable.

A focused deployment covering 5-10 core applications can reach production in 3-5 months. A full enterprise deployment across 30+ applications with custom connectors, a deep knowledge graph, and agentic capabilities takes 6-12 months. Connector development for legacy systems is typically the longest phase. We phase the rollout: core applications first, with additional systems and capabilities added progressively. Users get value from the first connected sources while the platform expands.

Forrester noted that Glean's strengths are concentrated in knowledge access rather than full agentic workflows. We build the agentic layer: AI agents that don't just find information but summarise backlogs, draft updates, create meeting briefs, generate reports, and trigger workflows - using the knowledge graph as their grounding and the permission model as their boundary. This is where the AI-driven knowledge management category is heading, and it's the capability that most strongly justifies a custom build over a platform purchase.