Contextual AI, a forward-looking startup built on the principles of retrieval-augmented generation, has introduced its grounded language model (GLM) with the firm claim that it delivers the highest factual accuracy in enterprise AI. The company reports that its GLM achieved an 88% factuality score on the FACTS benchmark, outperforming several prominent competitors in the field. Google’s Gemini 2.0 Flash reportedly scored 84.6%, Anthropic’s Claude 3.5 Sonnet registered 79.4%, and OpenAI’s GPT-4o posted 78.8%. Contextual AI’s leadership, comprised of veterans who helped pioneer retrieval-augmented generation, describes this milestone as a deliberate calibration toward enterprise-grade reliability, where the cost of inaccuracies can translate into regulatory risk, financial losses, or strategic missteps. The overarching message is that the GLM is not merely a more capable generalist; it is a model specifically engineered to excel in environments where factual integrity is non-negotiable. This emphasis on truthfulness positions Contextual AI as a distinct alternative in a market dominated by general-purpose models that often prioritize breadth of capability over the narrow, high-stakes accuracy demanded by regulated industries and mission-critical workflows.
Groundedness: Building the New Gold Standard for Enterprise Language Models
Groundedness—ensuring that AI responses stay firmly anchored to information explicitly provided in a given context—has emerged as a key requirement for enterprise AI systems. In regulated sectors such as finance, healthcare, and telecommunications, organizations demand AI that can either deliver precise, verifiable information or transparently acknowledge its gaps. The new GLM from Contextual AI centers this principle as a core design pillar. In an exclusive discussion, CEO and cofounder Douwe Kiela explained how this approach translates into practical benefits for organizations facing strict compliance, auditability, and risk management constraints. He highlighted that groundedness is not simply about accuracy in a vacuum; it is about ensuring that the model’s outputs can be traced back to definitive sources or explicit contextual boundaries. In other words, a grounded model should be able to quantify the provenance of each assertion, rather than offering a generic inference that might look plausible but cannot be verified against the source material at hand.
An illustrative example provided by Kiela illuminates how groundedness operates in real-world use: when a standard language model is given a recipe or a formula with a caveat appended—“this is true for most cases”—the model will often present the recipe as if it is universally true, inadvertently masking uncertainty. Contextual AI’s GLM, by contrast, is designed to articulate the nuance and explicitly label statements that hold only under certain conditions. It can convey that a rule applies to most instances but not all exceptions, and crucially, it can indicate where it does not have sufficient information to make a definitive claim. This capacity to signal uncertainty and to tether responses to verifiable context is central to reducing hallucinations in high-stakes settings. Kiela underscored that the ability to say “I don’t know” is not a weakness but a powerful feature in enterprise contexts, where stakeholders require honesty about limitations and the integrity of the reasoning process behind a given answer.
The enterprise imperative for groundedness extends beyond individual outputs to the overall reliability and trustworthiness of AI systems. In regulated domains, the expectation is not merely for models to generate coherent responses, but to demonstrate a transparent chain of reasoning, provide verifiable references, and maintain alignment with established policies and regulatory requirements. Contextual AI frames groundedness as the practical means by which organizations can cultivate confidence in AI-driven decisions, from risk assessment to decision support in complex operational environments. The GLM’s grounding mechanism is thus presented as a strategic feature that reduces the likelihood of erroneous recommendations, minimizes the risk of misinterpretation, and enhances the auditability of AI-driven processes. By delivering responses that are anchored in the user-provided context and in curated data sources, the GLM aims to enable enterprise teams to deploy powerful AI capabilities without compromising on accountability or regulatory compliance.
RAG 2.0: An Integrated, End-to-End Approach to Enterprise Data
Contextual AI positions its GLM within a broader system architecture that goes beyond a single model to deliver a cohesive, end-to-end RAG solution tailored for enterprise needs. The company describes its approach as “RAG 2.0,” a next-generation framework that moves away from simple pipelines that combine off-the-shelf components in a brittle, patchwork fashion. In this model, every component of the retrieval-augmented generation stack is jointly optimized to maximize overall performance, accuracy, and reliability. The traditional RAG system often relies on a frozen embedding model to convert queries into dense vector representations, a vector database to fetch relevant information, and a black-box language model to generate the final answer. This render—where modules are stitched together through generic prompting or orchestration frameworks—can result in a mosaic that is technically functional but suboptimal, a kind of “Frankenstein’s monster” of AI assembly.
Contextual AI’s architecture replaces this piecemeal assembly with a tightly coupled, co-optimized stack. The company describes a mixture-of-retrievers component that intelligently determines retrieval strategies by analyzing the query’s intent and the most relevant sources. Rather than treating retrieval as a single static step, the system plans a retrieval strategy that adapts to the nuances of each question, selecting pathways that maximize precision while minimizing latency and noise. At the heart of this coordinated process sits what Kiela characterizes as the best re-ranker in the world—a mechanism that prioritizes the most relevant, authoritative, and contextually appropriate sources before the information is handed to the grounded language model for synthesis. By ensuring that only top-tier, contextually aligned material informs the final response, Contextual AI asserts it can deliver higher truthfulness, less speculation, and a more trustworthy user experience.
The concept of RAG 2.0 represents a conscious pivot from generic AI pipelines toward a domain-specific, enterprise-ready architecture. It reflects a recognition that enterprise AI requires not only sophisticated generation capabilities but also robust information governance, provenance tracking, and controlled integration with corporate data ecosystems. In practice, this means aligning embeddings strategies, retrieval algorithms, and generation policies to produce responses that are not only fluent and coherent but also anchored in verified data sources and regulatory-compliant reasoning processes. The RAG 2.0 framework, as described by Contextual AI, embodies a holistic rethinking of how enterprise AI systems can balance four critical dimensions: accuracy, reliability, scalability, and explainability. The emphasis on jointly optimizing all components ensures that improvements in one area—such as retrieval relevance—translate into corresponding gains in generation quality and groundedness, thereby reducing the likelihood of hallucinations across the entire system.
Multimodal Capabilities: Reading, Analyzing, and Integrating Structured Data
While the newly announced GLM focuses on text generation, Contextual AI’s platform has rapidly expanded to accommodate multimodal content, enabling it to read charts, diagrams, and structured data from widely used data platforms. The company notes that the most challenging enterprise problems often lie at the intersection of unstructured and structured data. This intersection is where business teams frequently encounter a mix of narrative documents, policy guidelines, transaction histories, and tabular data that must be reconciled to produce accurate insights. Contextual AI positions its multimodal capabilities as a core differentiator, allowing the platform to ingest and interpret a variety of data representations without sacrificing fidelity or interpretability.
The platform’s connectivity to popular databases and data warehouses—BigQuery, Snowflake, Redshift, and Postgres—enables seamless incorporation of structured information into the reasoning process. This means that charts, tables, and relational data can be grounded in the same contextual frame as unstructured content, allowing for consistent reasoning across disparate data sources. Kiela emphasized that enterprise success often depends on how well AI can operate at the confluence of different data modalities, where the most consequential business decisions are made. By supporting complex visualizations and diagrams, the platform can render a more complete picture of a given scenario, whether it involves a circuit diagram in the semiconductor industry, a finance dashboard, or a regulatory policy document. The capacity to interpret charts and connect to databases widens the practical reach of GLM-based systems, enabling more accurate summaries, data-driven recommendations, and precise compliance reporting.
The implications of multimodal support extend into governance and risk management, where visual data often carries critical cues that are not fully captured in textual narratives. For example, in engineering or manufacturing contexts, a circuit diagram or a process flow chart might contain constraints, tolerances, or failure mode indicators that are essential for a safe and compliant operational plan. The ability to parse these diagrams and align them with textual descriptions and database records can reduce ambiguity, improve traceability, and strengthen decision confidence across the enterprise. Contextual AI’s multimodal capabilities also promise to streamline collaboration between domain experts who routinely consult charts and dashboards to validate AI-generated outputs. In practice, this translates into faster review cycles, fewer back-and-forth clarifications, and a more efficient alignment between AI insights and human expertise.
Beyond static data, the platform’s ability to ingest live or periodically updated data sources represents another key advantage for enterprise users. As companies continually refresh external data feeds and internal data warehouses, an AI system that can dynamically reconcile new information with existing knowledge graphs and policy constraints stands to deliver more timely and accurate guidance. The integration with major data platforms ensures that organizations can leverage their existing investments in data infrastructure while expanding AI capabilities in a controlled, auditable manner. The end result is a more robust, scalable, and trustworthy AI layer that can operate across departments—from finance and compliance to product development and operations—without requiring bespoke, one-off integrations for each new data source.
Enterprise Roadmap: Re-ranker, Document Understanding, and Agentic Features
Contextual AI’s product roadmap centers on expanding reliability, interpretability, and capability while maintaining a disciplined focus on enterprise applications. The company plans to release its specialized re-ranker component shortly after the GLM launch, signaling a staged approach to delivering a complete RAG 2.0 stack that can be deployed across a variety of corporate contexts. The re-ranker is designed to optimize the ordering and selection of information before it feeds into the grounded language model, ensuring that the most credible, relevant, and policy-aligned sources shape the final answer. This step is critical for maintaining high levels of factuality and for providing users with a clear rationale for the model’s conclusions. The anticipated expansion into more sophisticated document-understanding capabilities is intended to equip the platform to interpret and reason over long, complex documents, contracts, regulatory filings, and policy manuals—areas where accuracy and interpretability are essential for risk management and compliance.
In addition to document understanding, Contextual AI is developing experimental features aimed at increasing agentic capabilities. While the company positions these features as experimental, they signal an ambition to move the AI system beyond passive response generation toward more proactive, goal-directed behavior. Agentic capabilities could involve tasks such as autonomous data gathering within predefined boundaries, structured planning to accomplish a user-specified objective, or orchestrating a sequence of actions across connected systems to fulfill a business process. The emphasis, however, remains firmly on safety, governance, and auditable behavior. The company’s strategy thus focuses on delivering practical, verifiable enhancements that align with enterprise risk tolerances and regulatory requirements, rather than pursuing speculative or impractical forms of artificial agency.
Contextual AI’s commercial strategy centers on onboarding organizations that are under pressure to demonstrate concrete ROI from AI investments. The company’s leadership emphasizes that the value proposition of a grounded, database-connected RAG system lies in its capacity to reduce costly misstatements, improve decision confidence, and accelerate the deployment of AI-enabled workflows in regulated settings. The enterprise mindset is reflected in the company’s client roster and in the narrative around ROI, risk management, and governance. By delivering a grounded language model that is purpose-built for enterprise contexts, Contextual AI seeks to differentiate itself from broad-market AI providers by offering a solution whose design, data handling, and evaluation criteria align with the stringent demands of large organizations, financial institutions, and policy-driven industries.
Founders, Team, and Early Traction: A Focus on Real-World Enterprise Impact
Contextual AI was founded in 2023 by Douwe Kiela and Amanpreet Singh, two figures with strong pedigrees in the AI research and development landscape. Kiela has a background in the fundamental methods that underpin retrieval-augmented generation, while Singh previously contributed to advances at Meta’s Fundamental AI Research (FAIR) team and at Hugging Face. This combination of scholarly depth and practical product-building experience informs the company’s emphasis on creating a grounded, enterprise-ready AI stack rather than chasing headline-grabbing capabilities in general-purpose models. The leadership team’s track record—particularly in RAG research and scalable AI architectures—provides a credible basis for the company’s claim that it is delivering a more reliable solution for enterprise customers.
Contextual AI has reported initial traction with notable clients, including HSBC, Qualcomm, and The Economist. These partnerships are presented as early validation that enterprises are receptive to specialized AI solutions that prioritize accuracy, accountability, and governance. The messaging around these early successes centers on the return on investment that grounded AI can deliver in real-world environments, where the risks of hallucinations—false or misleading information presented as fact—can have tangible consequences for compliance, risk management, and operational effectiveness. By collaborating with established brands across banking, technology, and media sectors, Contextual AI aims to demonstrate the practical benefits of its RAG 2.0 approach, including improved decision support, faster issue resolution, and more reliable policy adherence across complex workflows.
The company’s strategic positioning emphasizes the idea that advanced AI can deliver measurable business value when properly aligned with the realities of enterprise operations. Rather than chasing generic capabilities that may look impressive but fail to translate into ROI, Contextual AI argues that a targeted approach—rooted in grounding, provenance, and tightly integrated data systems—offers a clearer path to realizing the promised benefits of AI in the workplace. Kiela has framed this as a shift from “boring” but dependable grounding to the more flashy but less trustworthy capabilities of some generalized models. In other words, the company argues that enterprise teams do not need a model that can draft poetry or produce broad, generic content; they require a model that can consistently ground its outputs in the right data, explain its reasoning, and support high-stakes decisions with verifiable evidence.
Market Implications: How Grounded, RAG-2.0 AI Elevates Enterprise Capabilities
The implications of Contextual AI’s GLM and RAG 2.0 architecture extend far beyond a single product announcement. In enterprise settings, the demand for AI that is both powerful and trustworthy is accelerating as businesses seek to digitize processes, automate knowledge work, and derive actionable insights from increasingly complex data ecosystems. The so-called “hallucination problem” that has long plagued language models is particularly acute in regulated industries, where incorrect answers can lead to regulatory penalties, reputational harm, and operational risk. The GLM’s high factuality score on a recognized benchmark signals the potential for a shift in how enterprises evaluate and adopt AI technologies. By foregrounding groundedness and provenance, Contextual AI is aligning its product narrative with the rigorous evaluation criteria that CIOs, CISOs, and compliance officers typically apply when selecting technology solutions.
From a buyer’s perspective, the GLM’s demonstrated accuracy and the RAG 2.0 architecture’s emphasis on end-to-end optimization offer several practical advantages. First, improved factual reliability translates into lower rework costs and faster decision cycles. When AI-generated insights are demonstrably grounded in source data, analysts and decision-makers spend less time cross-checking results and more time acting on trusted recommendations. Second, the transparency embedded in a provenance-aware system enhances regulatory readiness. Audits often require traceability of how outputs were produced, including the sources consulted and the reasoning steps involved. A system that can expose the chain of evidence behind a conclusion helps organizations satisfy audit requirements, demonstrate governance, and maintain accountability for AI-driven decisions. Third, the platform’s multimodal capabilities extend the reach of AI across departments, enabling finance teams to interpret dashboards, risk managers to review charts, and product teams to reason over both textual policy documents and structured data. This cross-functional applicability broadens the footprint of AI within an organization and can accelerate enterprise-wide adoption.
In addition to direct enterprise value, Contextual AI’s approach aligns with broader industry trends that prioritize responsible AI and governance. The emphasis on groundedness, robust retrieval, and integrated data systems resonates with regulatory expectations for accuracy, explainability, and accountability. As governments and industry bodies continue to shape standards for AI deployment, platforms that can demonstrate verifiable sources, context-bound reasoning, and auditable outputs may be favored in procurement processes. The market dynamics also reflect heightened competition among AI vendors to offer enterprise-grade features that balance capability with control. Contextual AI’s GLM and RAG 2.0 framework respond to this demand by presenting a cohesive, end-to-end solution that addresses both the performance and governance aspects of enterprise AI adoption.
Use Cases: Transforming Enterprise Workflows Across Sectors
The enterprise potential of Contextual AI’s GLM and RAG 2.0 stack is vast, with applications spanning finance, healthcare, manufacturing, telecommunications, and media. In finance, for example, the ability to process policy documents, risk disclosures, and market reports with high factual fidelity can streamline compliance analyses, regulatory reporting, and decision support for advisory workflows. In healthcare, the platform can assist in synthesizing patient data, clinical guidelines, and research literature, while carefully maintaining privacy constraints and ensuring provenance. In manufacturing and engineering, parsing technical diagrams and schematics alongside textual specifications enables more accurate troubleshooting, design reviews, and process optimization. In telecommunications, interpreting policy documents, service-level agreements, and network performance data requires a reliable, grounded AI that can reconcile disparate data sources into a coherent operational view. In media and publishing, the system can help generate summaries of complex policy issues, verify factual claims within articles, and integrate data from internal dashboards to support newsroom decisions.
Moreover, the platform’s ability to connect to major data warehouses means that teams can leverage existing data ecosystems without sacrificing governance. Embedding strategies and retrieval pipelines can be tuned to reflect organizational taxonomies, data governance policies, and regulatory constraints. The combination of robust grounding, source-aware reasoning, and seamless data integration positions Contextual AI as a practical enabler of AI-assisted decision-making at scale. For enterprises, this translates into more accurate risk assessments, tighter regulatory alignment, improved operational intelligence, and faster, more reliable knowledge-work outputs. The practical value proposition centers on reducing the cost and risk of AI adoption while expanding the scope of processes that can benefit from automated reasoning and data-backed insights.
Roadmap and Future Outlook: Building a Comprehensive Enterprise AI Layer
Looking ahead, Contextual AI’s roadmap emphasizes delivering additional components that strengthen the enterprise AI stack and expand capabilities across document understanding, data integration, and agentic features. The planned release of the specialized re-ranker will come in close sequence with the GLM launch, enabling customers to optimize information sequencing and prioritization in a way that further reinforces factual accuracy and relevance. The expansion into document understanding aims to empower the platform to interpret long-form documents, contracts, policies, and regulatory filings with greater precision, enabling more reliable extraction of obligations, constraints, and key terms. These enhancements are anticipated to extend the platform’s reach into risk management, legal operations, and policy analysis where precise interpretation of complex documents is essential.
In addition to core document understanding, Contextual AI is actively exploring experimental features for more agentic capabilities. While these capabilities are described as experimental, their development signals a strategic interest in enabling AI systems that can perform structured tasks within defined boundaries, orchestrate multi-step workflows, and autonomously assemble information from interconnected data sources under governance guidelines. The company’s emphasis remains on safety, accountability, and measurable ROI, ensuring that any agentic features align with enterprise risk tolerances and industry regulations. The corporate vision is to deliver tools that empower business teams to automate and optimize routine activities while preserving human oversight, auditability, and decision-quality. By extending the platform beyond passive response generation into proactive, compliant workflows, Contextual AI aims to unlock new levels of operational efficiency, data fidelity, and strategic insight across diverse industries.
The commercial narrative centers on tangible business outcomes: faster time-to-insight, reduced hallucinations, improved data integrity, and clearer lines of accountability. Early adopters have reported benefits such as more accurate policy summaries, faster compliance checks, and streamlined knowledge work processes, underscoring the practical value of a grounded AI stack that integrates retrieval, reasoning, and data sources in a coherent framework. As organizations continue to embrace AI-driven transformation, the combination of groundedness, end-to-end optimization, and multimodal data handling positions Contextual AI as a compelling option for enterprises seeking to deploy reliable AI solutions that can scale across departments while staying within regulatory and governance boundaries.
The People Behind the Platform: Founders, Culture, and Strategic Focus
The company’s leadership—centered on Douwe Kiela and Amanpreet Singh—embodies a blend of deep research acumen and pragmatic product execution. Kiela, a recognized figure in the development of retrieval-augmented generation methods, brings theoretical rigor and a track record of advancing the state of the art in AI reasoning. Singh, with experience at Meta’s FAIR and Hugging Face, complements this with a practical perspective on building scalable, user-centric AI products that meet real-world enterprise requirements. Their collaboration reflects a deliberate strategy to bridge rigorous academic insight with the demands of enterprise deployment. The team’s ethos emphasizes reliability, governance, and user trust, aligning technical advances with the organizational realities of regulated industries and risk-sensitive operations.
Contextual AI’s early client base, including HSBC, Qualcomm, and The Economist, demonstrates a degree of market validation and practical validation of the platform’s capabilities. These engagements illustrate how a grounded, robust RAG solution can be integrated into diverse organizational contexts, from financial services and engineering to media and information services. The emphasis on ROI—an outcome-oriented framing—addresses a critical concern for enterprise buyers who seek measurable benefits from AI investments rather than purely exploratory or experimental technology. The company’s positioning as a provider of specialized, grounded AI components acknowledges the reality that many organizations require tailored solutions that align with their data governance, compliance frameworks, and industry-specific workflows.
The strategic message is clear: enterprises require AI that is not only capable but also trustworthy, auditable, and aligned with the operational realities of complex organizations. By focusing on grounding, provenance, and the seamless integration of data sources, Contextual AI argues that its GLM and RAG 2.0 architecture offer a practical path to scalable AI that can deliver demonstrable ROI while satisfying governance, compliance, and risk management requirements. The founders’ emphasis on building a stack that is both technically robust and business-ready reflects a broader trend in enterprise AI adoption—one that prioritizes reliability and governance alongside performance and feature breadth.
Conclusion
Contextual AI’s release of a grounded language model (GLM) with a high factuality score on a leading benchmark marks a meaningful milestone in the evolution of enterprise AI. By foregrounding groundedness, provenance, and reliability, the company aims to address one of the most persistent obstacles to enterprise adoption: hallucinations and un verifiable outputs. The RAG 2.0 architecture represents a deliberate rethinking of how retrieval, embedding, and language generation components should interact within an enterprise context, emphasizing joint optimization, intelligent retrieval strategies, and high-quality re-ranking to ensure that the most relevant, credible sources drive conclusions. The platform’s multimodal capabilities—enabling the reading of charts, diagrams, and structured data from major databases—further expand its utility across departments and use cases, from finance and healthcare to manufacturing and media. The roadmap includes expanding document understanding capabilities and introducing experimental agentic features, signaling a commitment to evolving the platform into a more proactive, governance-friendly AI tool that still respects enterprise risk constraints. With early traction from prominent organizations and a leadership team rooted in both research and product execution, Contextual AI positions itself as a compelling option for enterprises seeking to unlock AI-driven value without compromising on accuracy, accountability, or regulatory compliance.