Komprise has launched
Intelligent AI Ingest, a new Smart Data Workflow engine designed to help enterprises safely and efficiently feed unstructured data into AI systems. The tool addresses one of the biggest pain points CIOs face today: making sure the right data - not redundant, irrelevant, or sensitive files - ends up in retrieval-augmented generation (RAG) and large language model (LLM) pipelines.
Unstructured Data Ingestion - A Big Problem
Most enterprises are buried under petabytes of unstructured data spread across file shares, cloud buckets, and old storage systems. A large portion of it is duplicate, outdated, or irrelevant to AI workloads. Shoving that volume into an AI pipeline drags down precision, clogs context windows, and drives up processing costs. Every batch of irrelevant files compounds inefficiency and chips away at ROI. On top of that, there’s risk: sensitive information can slip into prompts, raising compliance and privacy concerns.
This is where Intelligent AI Ingest looks to shift the equation.
Krishna Subramanian, co-founder and COO of Komprise, told ChannelE2E that the new approach is a direct response to the shortcomings of existing tools.
"ETL tools are primarily focused on structured data and do not provide a way to curate across unstructured data silos, especially the petabytes typically locked away in data storage devices like NAS and cloud file and object storage. Copy-and-sync solutions for unstructured data lack the ability to view and efficiently curate data across storage silos,” she explained.
Her point highlights the limits of applying structured data techniques to unstructured sprawl. Enterprises that continue to rely on bulk ingestion often pay twice - first in higher processing costs and then in lower accuracy.
Subramanian added that the difference is visible in measurable outcomes. "Studies show that every additional 10,000 documents fed to a RAG reduces its efficiency by 10%. Our own tests have shown that 70% of the data in a typical storage location is noise that can be eliminated through our curation. Komprise addresses the scattered nature, volume and poor data quality of unstructured data with global search and tools to narrow the focus on relevant data needed for AI services by weeding out duplicates, obsolete, sensitive, irrelevant and non-authoritative data. By sending just the right data to RAG and LLM, you lower AI processing costs while increasing accuracy of results. Finally, internal tests show Komprise runs twice as fast as copy-and-sync solutions because of its optimized AI ingestion engine that cuts file data overhead.”
The point isn’t just faster ingestion. It is about improving AI outcomes by cutting out the noise that weakens performance.
How Intelligent AI Ingest Works
Komprise starts with a global file index that maps enterprise data at the metadata level. From there, teams can search and shape datasets with accuracy, filtering out low-value or sensitive information before it ever touches an AI model. The result shifts ingestion from a bulk transfer exercise to a governance step - one that improves model efficiency while reducing risk.
Sensitive data management is another pillar of the design, and Subramanian was quick to highlight how the company differentiates itself here.
"There are three unique things about how Komprise identifies and remediates sensitive data for AI. First, Komprise sits outside the data path, providing an efficient way to find sensitive data across the entire data estate, unlike security-management tools which monitor user access and can have performance implications on petabytes of unstructured data. Second, Komprise can find both standard types of sensitive data (like social-security number or passport number) as well as corporate-specific data (like project number codes). Third, Komprise provides workflows for sensitive data mitigation: you can exclude sensitive data from being sent to an AI process with just a single button click, for instance. Komprise differentiates by its ability to find and act on sensitive data at scale across petabytes of unstructured data.”
Instead of slowing down production systems by inspecting user activity, Komprise applies metadata intelligence from outside the path, giving IT teams the ability to act without disrupting workloads. That separation of duties - curation without interference - could make governance easier to operationalize in large enterprises.
Building an AI Data Ecosystem
Komprise is also emphasizing that Intelligent AI Ingest is not meant to operate in a vacuum. By working with ecosystem partners such as Nvidia and SUSE, the company ensures curated datasets can move directly into GPU-accelerated storage or Kubernetes-based platforms without custom development work.
Subramanian termed this design as intentional. “Komprise Intelligent Data Management is an open platform that works via standards such as NFS, SMB, S3/Object, REST and Iceberg to interoperate with partner solutions. Unlike agent or connector-based approaches that require custom development for each integration, the Komprise approach gives customers flexibility to leverage a wide ecosystem of partners.”
That openness extends to how Komprise goes to market. The company’s presence on AWS and Azure Marketplaces and resale partnerships with Pure Storage, NetApp, and IBM provide multiple paths for adoption. According to Subramanian, broadening the AI and data warehouse/data lake partner ecosystem is central to the company’s growth roadmap.
The ecosystem strategy also ties directly into the channel. “Resellers that are partners of Nvidia and SUSE can continue to get sales incentives, MDFs and campaigns through those programs and add to it the sales incentives, deal registration protection and training offered by Komprise,” Subramanian said. This alignment allows resellers to participate in joint AI solutions without having to choose between vendor programs, while also layering Komprise-specific benefits on top of their existing relationships.
For enterprises, the result is a curated and more secure path into AI. For partners, it creates a way to monetize AI readiness as an extension of existing infrastructure engagements. Taken together, Komprise is making the case that solving unstructured data sprawl is not just a technical prerequisite but a market opportunity.
