Data Noise Buster

Tired of errors, costly inference, SQL strings and time-consuming file management for your AI queries? ​

  • Dataset Targeting saves time and reduces data noise by turning your datasets* into named, reusable data assets that you can assign to agents, prompts, and projects ​with a simple @ mention ["Use @AI Research"]. ​

*Datasets are persistent, editable collections of files and folders aggregated across sources; agents restrict retrieval and reasoning to the targeted dataset(s), improving accuracy, cutting cost, and creating audit-ready lineage.​

In

@Point, Action, Result ​

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Curate Once, Reuse Everywhere

Build persistent, multi-source datasets with lineage and curation logs. No per-chat uploads, no vendor lock-in.

Dynamic Assignment & Memory

Reassign, swap, or update datasets in seconds at runtime. Agents keep reusable context and editable knowledge targets.

Targeted, Cost-Efficient Inference

Agents retrieve and reason only over the right datasets—reducing hallucinations, noise, and compute costs.

Business-User Friendly & Secure

Intuitive “@” targeting and visual file management. Zero-copy architecture with secure controls and traceable changes.

Rich Insights, Auditability

Gain end-to-end analytics, citations, and tamper‑evident logs for verifiable outcomes and faster reviews.

Streamlined Regulatory Adherence

Built-in controls and policy enforcement align with ISO 27001, SOC 2, HIPAA, GDPR, and the EU AI Act.

Knowledge Discovery, Dynamic Agents

Combine advanced RAG, SourceSnips citations, and adaptive agent teams to surface trusted answers and automate complex workflows.

About us
Efficiency & Cost Savings
60-80% Token reduction with targeted inference. Curated datasets reduce compute costs versus full-context processing. Reusability eliminates repetitive uploads.
Accuracy & Relevance
50% Faster audits in GRC. Smaller, curated scope reduces off‑topic retrieval and hallucinations; each agent bound to its most relevant corpus.
Multi-Agent Scale
Assign datasets per agent to split workloads (KYC triage, sanctions review) and merge results with clear provenance.
Flexibility
Datasets evolve independently of projects, ideal for regulatory changes or ad‑hoc analysis. Project independence supports structured and ad-hoc work.
Compliance & Governance
Named datasets provide traceability and persistent audit trails. Granular access controls per dataset and agent support privacy reviews (DPIA/FRIA).

Reusable Datasets in 3 Clicks​

01

Create or Edit a Named Dataset

Start by creating a new dataset with a descriptive name like "AI Research". Add an optional description to help team members understand the dataset's purpose and scope.

02

Curate Files and Folders Across Sources

Select and manage files from multiple sources including cloud drives, document repositories, and enterprise systems. Add or remove content dynamically as your needs evolve.

03

Target Dataset in Prompts with @ Notation

Use the intuitive @Dataset syntax in conversations and prompts. The agent automatically restricts its analysis to only the specified dataset, ensuring precise and relevant results.

04

Run Targeted Inference

Agent uses only selected dataset(s), ensuring focused analysis and predictable cost.

05

Multi-Agent Assignment

Different agents in a team carry different datasets and collaborate by design.

Multi-Agent Use Cases

GRC Audit Prep
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Each agent analyzes its corpus; supervisor agent compiles auditable report with citations and lineage.

KYC Operations
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Parallel analysis across customer data, sanctions screening, and transaction monitoring with decision package output.

Incident Response
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Procedure agents guide response; diagnostic agents analyze systems; datasets swap as scope narrows.

Microsoft Azure AI Foundry
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Anthropic Claude Projects
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Deepset Haystack
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Google Vertex AI Agent Builder
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Perplexity Internal Knowledge
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Sema4.ai GPT
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