Enterprise AI Platforms: How Glean, Writer, Dify, and Relevance AI Compare to Nexus

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What enterprise AI platforms are, and why they are not all solving the same problem

Enterprise AI platforms promise to put AI to work across your organization. But the term covers a wide range of approaches, and most of them are solving a narrower problem than they advertise. At their core, these platforms are knowledge-layer tools and assistant or app builders. Some index your company's information and make it searchable. Others generate content and enforce brand voice. Others provide open-source toolkits for engineering teams to prototype AI applications. Others offer visual builders for simple agent workflows. All of them call themselves enterprise AI platforms.

What they share is a foundation in information retrieval and content generation. They excel at finding answers, surfacing knowledge, and producing text. That is genuinely valuable. But there is a ceiling to what a knowledge-layer tool can automate. When the work requires executing across multiple systems, making autonomous decisions at branch points, handling exceptions that were not pre-mapped, and orchestrating multi-step processes end to end, these platforms reach their limits. They find the answer. They do not complete the work.

The differences become clear when you ask a few specific questions: Does the platform complete work autonomously, or does it surface information for a human to act on? When the agent encounters an exception it was not designed for, does it adapt and escalate, or does it stop? Who builds the agents, and who maintains them in production? How deeply does the platform integrate across your enterprise systems, not just for reading data but for executing actions? And critically: what support do you get when deploying AI at scale, beyond the software itself?

Nexus sits in this category but approaches the problem from the other direction. Rather than starting with information retrieval and adding agent capabilities on top, Nexus was built for deep process execution from day one. Agents on Nexus combine information retrieval with autonomous decision-making, multi-system orchestration, and end-to-end workflow completion. They do not just find the answer; they complete the work. And the platform is paired with Forward Deployed Engineers (FDEs) who embed with your team to handle integration, change management, and ongoing optimization. Every engagement starts with a 3-month proof of concept tied to measurable business outcomes. The platform handles autonomous workflow execution across 4,000+ enterprise systems. The service layer handles everything else. That combination of deep process execution plus hands-on engineering support is the central differentiator, and it is why Nexus converts 100% of POCs to annual contracts.

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Category comparison

Dimension	Glean	Writer	Dify	Relevance AI	Hebbia	Nexus
Completes work autonomously?	Knowledge-layer tool Finds and surfaces information Agent capabilities emerging (100+ native actions) Human still acts on what is surfaced	Content-layer tool Generates and governs brand content Agent workflows added through AI HQ and AI Studio Automation is growing but rooted in content ops	App-builder toolkit Workflow-based agents using Function Calling or ReAct Requires engineering to build and maintain Execution depth depends on what developers wire up	Agent builder Agents automate tasks within the platform's tooling Execution scope bounded by native integrations Human oversight needed for non-standard paths	Analytical AI engine Analyzes documents and extracts structured insights Multi-agent "swarm" processes millions of pages Tells you what is in the documents; does not execute workflows	Process execution engine Agents execute, validate, route, decide, and escalate independently Built for multi-system orchestration from day one Does not just find the answer; completes the work
Handles exceptions?	Employee acts on surfaced information Agent exception handling still evolving Platform built to retrieve, not to recover from process failures	Guardrails built into agent lifecycle Exception handling maturing as agent platform develops Governance is strong; autonomous recovery is early	Depends on workflow design Edge cases require developer intervention to redesign flows No built-in escalation patterns	Depends on agent configuration No native enterprise escalation patterns Exceptions outside configured paths require human intervention	Analytical exceptions handled within document scope ISD architecture reasons across edge cases in documents Does not handle process-level exceptions outside analysis	Agents adapt or escalate with full context No silent failures Enterprise escalation logic is native FDEs tune exception handling based on production data
Who builds agents?	IT deploys the platform Employees use it to search and ask questions Not a builder tool; it is an employee-facing assistant	Marketing, comms, and content teams primarily Expanding to broader business users Builder experience is content-first	Developers and technical users Engineering manages infrastructure and production Business teams cannot self-serve without technical support	Business teams via visual interface, self-serve Technical skills needed for non-native integrations No deployment partner included	Analysts, associates, and research teams use the platform IT/data teams handle deployment No self-serve; demo and onboarding required	Business teams across any department FDEs handle integration complexity and production readiness No internal engineering dependency FDEs identify highest-impact use cases with your team
Integration scope	100+ enterprise connectors for indexing 100+ native agent actions Integrations optimized for reading data, not executing across systems	Google Workspace, Microsoft 365, Snowflake, Slack, content/marketing stack Growing connector set Depth is strongest in content and collaboration tools	API-based, 50+ built-in tools Growing plugin ecosystem Custom tooling required for most enterprise systems You own the integration work	HubSpot, Salesforce, Zapier, Google Docs, and other business tools Native connectors cover standard mid-market stack Custom or legacy systems require workarounds	Document repositories and financial data platforms FactSet, Preqin/BlackRock Aladdin partnerships Designed for document ingestion, not cross-system workflow execution	4,000+ integrations across CRMs, ERPs, communication tools, legacy systems, and custom APIs FDEs handle integration to your specific environment Deploy across Slack, Teams, WhatsApp, email, phone, web
Pricing model	Per-seat (~$50/user/month + add-ons) Mandatory 10% support fee Minimum ~$50K-60K/year	Per-seat ($29-39/user/month Starter) Custom enterprise pricing	Free self-hosted Cloud: $59-159/month Custom enterprise pricing	Credit-based tiers: Free to $599/month Custom enterprise pricing	Per-seat licensing $3,000-3,500/seat/year (Lite) $10,000-15,000/seat/year (Professional) No self-serve; demo required	Per-agent, tied to value delivered 3-month POC before annual commitment Pricing reflects outcomes, not seat count
Service model	Standard enterprise SaaS support Requires 1-2 internal FTEs to manage	Enterprise onboarding Partner ecosystem for implementation (e.g. Perficient)	Community support (GitHub, Discord) Enterprise plan adds priority support You own production operations	Documentation, community forums Premier support on Enterprise tier No embedded deployment support	Enterprise onboarding and support ~120-137 employees across 5 continents Dedicated account management for large clients	Forward Deployed Engineers embedded in your organization Change management and adoption support Ongoing optimization based on production performance
Governance	SOC 2 Type II Data governance controls, granular permissions	SOC 2 Type II, HIPAA, PCI, GDPR ISO 27001, ISO 27701, ISO 42001	SOC 2 Type I and II, ISO 27001 GDPR on enterprise plan Self-hosted: you own the compliance burden	SOC 2 Type II, GDPR Enterprise plan includes SSO, RBAC, data residency	SOC 2 Type I and II AES-256 encryption, TLS 1.3 GDPR ready Never trains on customer data	SOC 2 Type II, ISO 27001, ISO 42001, GDPR Full audit trails and decision traceability Role-based access at every step
Best for	Companies where the primary problem is finding information scattered across tools Knowledge retrieval, not process execution	Organizations where content operations and brand consistency are the primary AI challenge Content generation, not workflow completion	Developer teams prototyping LLM applications or wanting full stack control App building, not turnkey enterprise deployment	Mid-market teams getting started with sales and marketing agent automation, self-serve Simple agent workflows, not deep multi-system orchestration	Financial analysts, lawyers, and consultants analyzing massive document sets Analytical depth on document-heavy work, not workflow execution $25T in AUM across client firms	Enterprises that need AI to complete high-volume workflows autonomously across systems Deep process execution with measurable financial outcomes FDEs embedded to handle integration, adoption, and optimization

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Quick decision guide

Choose Glean if your biggest problem is finding information. If employees waste hours searching through Slack, Confluence, Google Drive, and SharePoint for answers that already exist, Glean indexes your knowledge and makes it searchable. It is a strong knowledge-layer platform, now at $200M+ ARR, and it does that job well. If the bottleneck is access to information rather than acting on it, Glean solves that problem. Just recognize that it is a retrieval tool, not a process execution engine. If you eventually need AI that completes the work rather than surfaces it, you will need a different architecture.

Choose Writer if your primary challenge is content operations at scale. Writer has deep expertise in brand voice enforcement, content generation, and knowledge management for marketing and communications teams. Its proprietary Palmyra LLMs are cost-efficient and enterprise-tuned. If you need on-brand content across distributed teams and want to explore agent capabilities gradually from a content foundation, Writer is purpose-built for that. The question to ask: is your AI challenge about generating better content, or about executing end-to-end processes? Writer excels at the first. If the second is where your ROI lives, you will outgrow a content-layer tool.

Choose Dify if your team has strong engineering resources and wants full code-level control over the agent stack. Dify's open-source foundation (130k+ GitHub stars, 1,000+ contributors) gives you flexibility to self-host, inspect, customize, and extend everything. If budget is tight, engineering time is available, and you want to experiment before committing to a vendor, Dify is an excellent starting point for prototyping and learning. The tradeoff: Dify gives you a toolkit, not a deployment partner. Your engineers build it, maintain it, integrate it, and own production. That is a strength if you want control. It becomes a bottleneck when the goal shifts from building an AI app to deploying autonomous agents at enterprise scale.

Choose Relevance AI if you want to get started with AI agents quickly and self-serve, without a formal engagement. Their platform is accessible, well-designed, and priced for teams that want to experiment. If your use cases stay within standard business tools (HubSpot, Salesforce, Slack) and you have the internal capability to build and manage agents on your own, Relevance AI is a practical entry point. Where it reaches its limits: deep multi-system orchestration, complex exception handling, and the kind of integration and change management work that requires hands-on engineering support.

Choose Hebbia if your bottleneck is analytical throughput on document-heavy work. Hebbia's Matrix product is one of the strongest analytical AI engines available, purpose-built for financial analysts, lawyers, and consultants who need to reason across thousands of documents simultaneously. Its proprietary ISD architecture and multi-agent swarm go well beyond basic RAG. BlackRock, KKR, and Carlyle are named clients. If the challenge is deeper, faster analysis of investment memos, credit agreements, or legal contracts, and your team will then act on those insights through existing workflows, Hebbia was built for that. The question to ask: is your bottleneck understanding what is in the documents, or completing the work those documents point to? Hebbia excels at the first. If the second is where your ROI lives, you need process execution, not analytical depth.

Choose Nexus if your bottleneck is not finding information or generating content, but completing work at scale. Enterprise AI platforms are strong at the knowledge layer: surfacing answers, generating content, building simple AI workflows. Nexus goes beyond that layer. It combines information retrieval with deep process execution, autonomous decision-making, and multi-system orchestration across 4,000+ enterprise systems. Agents on Nexus do not just find the answer; they complete the work. And Forward Deployed Engineers embed alongside your team to handle integration complexity, identify the highest-impact use cases, and manage the organizational change that makes adoption stick. Nexus is a solution (platform plus service), not just software, and every engagement starts with a 3-month POC tied to measurable outcomes before you commit.

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Individual comparisons

Comparison	One-line summary
Nexus vs Glean	Glean is a knowledge-layer tool that finds information across your company. Nexus agents go beyond retrieval to complete entire workflows end-to-end. Different problems, different architectures.
Nexus vs Writer	Writer is a content-layer platform expanding into agents. Nexus was purpose-built for deep process execution from day one, with FDEs embedded in your team.
Nexus vs Dify	Dify gives engineering teams an open-source toolkit to build AI apps. Nexus gives enterprises a deployment partner accountable for getting autonomous agents into production at scale.
Nexus vs Relevance AI	Relevance AI is a self-serve agent builder for simple workflows. Nexus handles deep multi-system orchestration with FDEs, 4,000+ integrations, and enterprise-grade exception handling.
Nexus vs Hebbia	Hebbia is an analytical AI engine for finance and legal document analysis. Nexus agents go beyond analysis to complete entire workflows end-to-end across departments.

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What enterprises experienced

Lambda is a $4B+ AI infrastructure company. AI is their core business. They have world-class engineers who could build anything internally. Their CTO concluded the opportunity cost was too high.

Their Head of Sales Intelligence, Joaquin Paz (no engineering background), built an autonomous research agent on Nexus that monitors 12,000+ enterprise accounts. The agent performs 2 hours of deep analysis per account across dozens of data sources, delivering structured intelligence to account executives. This is the difference between the knowledge layer and process execution: the agent does not just find information about an account and hand it to a human. It completes the entire research workflow autonomously, synthesizes findings across sources, and delivers actionable output.

The results: $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring. 24,000+ research hours added annually (equivalent to 12 full-time analysts). $7M+ projected annual value as they expand to a full agent fleet.

Lambda tried other approaches first. Open-ended AI tools were intelligent but inconsistent: same question, different answer every time. Traditional automation was reliable but rigid: heavy hard-coding, breaks when systems change. Knowledge-layer tools could surface information but could not execute the full research process end to end. Nexus delivered both intelligence and consistency, with deep process execution that completed the work.

As Joaquin put it: "We looked at open-ended AI agents; they were smart but inconsistent. We looked at traditional automation; it was reliable but felt heavy. With Nexus, we got both: intelligent and consistent."

Lambda is now expanding from a single agent to an agent fleet across their entire go-to-market organization.

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Worth exploring?

If your team has been evaluating enterprise AI platforms and the core question has shifted from "can we find the information?" to "can we complete the work at scale?", you have likely hit the ceiling of knowledge-layer tools. The gap between surfacing an answer and executing an end-to-end process is where most enterprise AI evaluations stall. That gap is what Nexus was built to close.

Every Nexus engagement starts with a 3-month proof of concept tied to specific, measurable business outcomes. Forward Deployed Engineers embed with your team from day one. You see the results before committing, and you can exit anytime.

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Related categories

• AI Agents vs AI Assistants - Do you need AI that surfaces information for individuals, or AI that completes entire workflows autonomously?
• AI Agents vs Workflow Automation - Rule-based automation vs. intelligent agents that handle exceptions and adapt
• AI Agents vs Developer Frameworks - Should engineers build from scratch, or should business teams deploy with FDE support in weeks?
• Build vs Buy AI Agents - The real opportunity cost of building AI agents in-house
• Back to all comparisons