Modern enterprises struggle with fragmented data spread across countless SaaS tools, from CRMs and support ticketing systems to product analytics and engineering backlogs. Today, most AI solutions use the emerging Model Context Protocol (MCP) – “an open standard that enables AI agents to call external tools and live data sources” – to fetch information on the fly.
While MCP-based retrieval provides up-to-the-minute data, relying solely on just-in-time API calls can introduce latency, complexity, and missing context. Each user query may trigger multiple external calls (e.g. to CRM, ticketing, code repository APIs). This could lead to performance overhead and potential failures if those systems are slow or unreachable.
As one analysis put it, “MCP is ideal for speed and breadth; [custom] agents win when depth, translation, or customization is required.” In other words, fetching bits of data quickly is not enough. Deeper understanding and integration are needed for truly comprehensive answers.
DevRev offers a fundamentally different and more powerful approach: a unified knowledge graph that physically consolidates enterprise data ahead of time. Instead of piecemeal calls at query time, DevRev’s platform leverages robust APIs and batch connectors to bi-directionally sync data from all your common SaaS systems into one integrated data store, continuously and in near real-time. The result is a “single source of truth” data layer that can be tapped instantly by AI models.
In this blog, we outline DevRev’s technical architecture – a unified, relationship-rich knowledge graph with an SQL interface. We describe DevRev’s unique capabilities in unifying structured and unstructured data, enriching it with context, and enabling conversational analytics via SQL.
Unified knowledge graph vs. just-in-time data federation
Many AI solutions address data silos by federating queries, i.e. calling each system on demand when a question is asked. MCP makes this more standardized, but it still essentially treats data access as a “just-in-time” operation. For example, an AI assistant asked about a project status might at that moment query Jira’s API (via an MCP server) for issue tickets, then query Salesforce for related customer information, and so on. This on-the-fly approach ensures fresh data, but comes at a cost:
- Performance overhead: The MCP JSON-RPC layer adds latency beyond direct calls
- Operational complexity: Running additional MCP server processes and handling many external dependencies.
In high-volume or real-time scenarios, repeatedly orchestrating multiple API calls can slow responses and introduce points of failure. Moreover, each data source is accessed in isolation, which makes it hard to capture relationships (e.g. which support ticket corresponds to which engineering bug) during a single query, the AI has to infer or ask for those links separately.
As one expert summarized, “MCP is fast but uncontrolled; agents provide curated, traceable, trustworthy research,” meaning that grabbing raw data quickly is not the same as having a cohesive, contextualized view of information.
DevRev takes a more robust approach by building and maintaining an enterprise knowledge graph internally. Through its Airdrop data orchestration technology, DevRev pre-integrates data from diverse sources into a unified, accessible layer . This knowledge graph is continuously updated with enterprise data, enhanced with global context.
It organizes your different data sources into a new, unified, and customizable data layer, all while understanding and maintaining the relationships between them.
Instead of treating each SaaS tool as a separate silo to query at runtime, DevRev links them in one graph, connecting customers to tickets, tickets to product features, features to engineering tasks, conversations, documents, and so on. The graph weaves together data from multiple sources into an interconnected network, giving AI a 360-degree contextual view rather than disconnected snippets.
Crucially, this unified graph is ready to answer questions instantly. When any LLM needs information, it can retrieve it with a single query against DevRev’s store, without fanning out to dozens of external APIs at that moment. This yields far faster and more reliable responses. All the heavy lifting of integration happens upfront and continuously, not during the user’s session. And because DevRev’s platform preserves rich relationships and metadata, the AI can draw deeper insights. For example, understanding that “ACME Corp” in the CRM is the same entity as “Account #123” in the finance system, linked to 5 open support issues – all of which the graph can enumerate in one go.
In short, DevRev’s knowledge graph provides the AI with built-in context and connections that pure MCP-based approaches would struggle to assemble on the fly.
As a press release noted, “AI agents will only be as good as the context we provide them, and with the knowledge graph as the brainpower behind [DevRev’s] platform, it can do the hard work behind the scenes… eliminating the status updates, messy integrations and disconnected data” of the past.
Key Technical Advantage:
DevRev combines the freshness of live data integration with the robustness of a pre-built knowledge repository.
Intelligent ingestion for resilience and scalable AI adoption
DevRev’s knowledge graph is powered by a range of sophisticated data ingestion strategies, spanning batch API calls, rate-aware scheduling, and adaptive syncing techniques, designed to build and maintain a rich, queryable system of record. This architecture ensures that DevRev can reliably ingest and unify data from source systems, even in environments with API rate limits, throttling, or intermittent availability.
Unlike just-in-time approaches that require real-time access to external APIs for every query—often at the cost of latency, fragility, or service disruptions—DevRev decouples data access from usage. Data is pulled proactively, normalized, and contextually linked in advance. As a result, AI agents querying the graph aren’t burdening the underlying systems repeatedly. They’re accessing pre-composed, semantically enriched knowledge.
This approach becomes increasingly important as AI adoption scales. In high-usage environments, repeated MCP-based access by many agents or users can overwhelm source systems. DevRev mitigates this risk by enabling low-impact, high-fidelity data acquisition up front, creating a persistent knowledge layer that serves as a buffer between AI workloads and transactional systems.
Rich context through relationships and metadata
A core strength of DevRev’s knowledge graph is that it doesn’t just dump data into one place. It enriches and interlinks that data into a semantic network. In traditional systems, even if you index data from multiple sources, you often lose the relationships. For instance, a support ticket and a bug report might be indexed as separate documents with no obvious connection. DevRev, however, was architected from day one to maintain data integrity and relationships across systems to understand connections between product, people, and work.
If a customer complaint in Zendesk is related to a JIRA issue, which is related to a code commit in GitHub, all those links are first-class entities in the graph. This means an AI query can traverse those connections effortlessly.
For example, an employee could ask, “Has any high-severity support ticket been filed by Acme Corp in the last month, and did it result in a bug fix?”. Answering that requires joining data across customer records, support tickets, and engineering tasks. DevRev’s graph can answer such multi-hop queries directly because it has a unified representation of all those entities and their links.
A conventional approach without a knowledge graph might require the agent to call the CRM, then the support system filtering by that customer, then cross-reference IDs with the engineering system – a brittle and complex sequence. In contrast, DevRev’s unified data layer enables one coherent query.
As industry experts note, knowledge graphs “consist of entities and relationships that capture the interconnected nature of knowledge,” enabling precise multi-step reasoning and integration of diverse data sources.
Our platform translates this power into enterprise AI: DevRev’s agents can leverage the graph to draw informed conclusions (not just retrieve isolated facts) because the context is inherently connected.
Moreover, DevRev continuously annotates and enriches the data with metadata and AI-generated insights. For instance, our system might tag customer feedback with sentiment or link a support conversation to a knowledge base article if it appears related. In effect, DevRev’s knowledge graph becomes a living model of the enterprise, which an LLM-based assistant can reason over.
From passive insight to proactive action: DevRev’s write-enabled intelligence
Most AI systems stop at insight, surfacing the right data at the right time. But true enterprise intelligence demands more than just knowing; it demands doing. This is where DevRev’s platform distinguishes itself from solutions that merely read from APIs on request. Because DevRev maintains a system of record, it empowers agents to take action directly within the enterprise’s workflows.
Unlike traditional “MCP-only” solutions, which fetch data on the fly but require external tooling to act on it, DevRev provides a bi-directional sync layer that supports full CRUD (Create, Read, Update, Delete) operations across its unified knowledge graph.
In practice, this means DevRev is not just a passive observer of the enterprise state, but an active participant in enterprise execution. Whether it’s updating a support ticket, logging a new Jira issue, assigning ownership to a task, or triggering an automation sequence, DevRev offers first-class write APIs to enact decisions programmatically.
Conversational analytics via SQL: Marrying AI with a real database
Recent advancements in large language models have made natural language to SQL translation (“Text-to-SQL”) not only feasible but surprisingly accurate . Users can ask questions in plain English, “What was the average customer satisfaction for premium clients in Europe last quarter?,” and an LLM-empowered agent can translate that into an SQL query against DevRev’s knowledge graph database, then execute it to get a definitive answer.
Salesforce, for example, has demonstrated internal tools where employees query data via Slack in natural language and the system returns answers by generating SQL under the hood. DevRev supercharges this paradigm by providing a unified SQL endpoint for all enterprise data (not just one data warehouse or one SaaS app).
This marriage of LLM intelligence with proven database technology is exactly why we believe SQL will continue to underpin conversational analytics and search in the era of LLMs and AI. Natural language interfaces become vastly more powerful when grounded in SQL because the answers are coming from exact, up-to-date data rather than fuzzy heuristic matches. It’s worth noting that SQL also adds trust and governance. Results can be audited or refined using familiar SQL queries, which is comforting for enterprise data officers concerned about AI “making up” numbers.
The ambiguity of Model Context Protocol (MCP)
While Model Context Protocol (MCP) is a promising standard for enabling LLMs to fetch real-time data from external tools, it introduces inherent ambiguity in how data is accessed and composed. MCP typically relies on a planner-agent pattern, where the LLM must decide which tools to invoke, in what order, and how to stitch responses together. This procedural approach makes sense in open-ended tasks, but for enterprise data retrieval, it is often brittle, hard to debug, and difficult to constrain for security and correctness.
By contrast, SQL is declarative: it allows you to express what you want, not how to get it. When combined with a unified schema like DevRev’s knowledge graph, SQL provides a deterministic, inspectable, and auditable interface, critical for enterprise-grade AI systems.
This tension is not new. The challenges of federated data access—latency, partial failure, lack of schema-level understanding—have persisted for decades in distributed query planning. MCP inherits many of these issues, and the community is still working through them. That’s why DevRev’s hybrid approach of supporting MCP where needed, but favoring persistent, queryable representations via SQL is both pragmatic and future-proof.
This is a truly unique value proposition. No competitor relying purely on MCP calls or vector search can offer a full SQL query over integrated SaaS datasets.
DevRev’s horizontal platform proven across domains
It’s important to note that DevRev’s architecture isn’t theoretical. It’s battle-tested across multiple vertical solutions, demonstrating its flexibility and power in real-world scenarios.
DevRev’s platform comes with pre-built AI agents for several enterprise functions:
- Customer support: DevRev’s conversational support agent can deflect routine tickets and assist support reps by drawing on knowledge from past tickets, documentation, engineering updates, and even customer success data. By having all support interactions, customer profiles, and product issues in one graph, the AI can resolve queries faster. As Bolt’s Principal Support Engineer attested, DevRev “eliminate[s] duplicate efforts, and easily access[es] information” in support. This showcases how unifying data reduces friction in support workflows.
- Product development (Build/Engineering): Our product development agent assists engineering teams by connecting the dots between feature requests, bugs, code commits, and deployments. For instance, if a product manager asks which customer issues were addressed in the last release, the agent can answer instantly thanks to the linked graph of support tickets to Jira issues to Git commits.
- Customer experience/sales (Customer 360): DevRev provides a Customer 360 agent that gives a comprehensive view of a customer’s health by combining CRM records, support history, usage telemetry, and even community forum posts. The knowledge graph approach shines here by capturing all customer touchpoints “into a single platform”, unlike competitors who leave data scattered. The AI can proactively flag churn risks or upsell opportunities because it sees everything about the customer in context.
- Enterprise search: DevRev’s search agent allows employees to find anything across all apps. It doesn’t just do keyword matching; it can answer, “Where can I find the design spec for the feature that was requested by our top paying client?”, which involves knowing who the top client is, what feature they requested (support or sales data), and then finding the design spec in, say, Confluence – all connected via the graph. This kind of semantic, context-aware search agent is already part of DevRev’s offering.
- Workflow automation: With all data unified, DevRev also provides an automation layer (workflows) to trigger actions across systems. For example, if a support ticket is about a VIP customer and has a high sentiment negativity, it automatically alerts the account manager. These automations, backed by AI, show that having a centralized data hub isn’t just for Q&A, but for acting on insights.
The above examples illustrate a horizontal platform in action. DevRev did not build five separate products; it built one unified data foundation and various agent “personalities” on top. This speaks to the robustness and scalability of our architecture: from support to engineering to sales, the same knowledge graph backbone delivers value.
Finally, DevRev’s platform is cloud-native and enterprise-ready. It is built with security (SOC2 compliant), scalability (handling millions of records in sub-second queries ), and extensibility in mind.
With pre-wired data, intuitive workflows, and real-time UX, DevRev enables teams to stand up intelligent agents faster and with more context.
Because DevRev stores unified data in a high-performance database (powered by our feather-light modern data stack), we can do what most AI solutions cannot: expose an SQL interface across the entire knowledge graph. This is a critical differentiator. SQL is the most enduring and widely used query language in enterprise settings, proven over decades for its power and flexibility. By organizing enterprise knowledge in SQL-queryable form, DevRev makes it instantly compatible with the vast ecosystem of tools and skills enterprises already rely on. Combined with LLMs, SQL becomes a powerful bridge between natural language and data.
