CohGent^TM 

Solving Hallucination at the Source

Many enterprise GenAI deployments are failing due to hallucinations, a catch-all phrase for the incorrect, irrelevant or fabricated answers generated by models. Automating tasks with LLMs isn’t just about generating text, it’s about making sure the output is relevant and accurate.

Even with the good prompt design, right LLM & robust RAG pipelines, the final output depends heavily on the quality of your source material. Retrieval might fetch information correctly, but if the information itself is poorly written or difficult to parse, the LLM will struggle to produce reliable answers. Simply put, the less comprehensible your existing documentation is, the more likely the LLM will hallucinate.

Advantage CohGent^TM

CohGent is hallucination-reduction tool that enhances the comprehensibility of your documentation, for machines & humans.
At its core is a scoring engine that measures comprehensibility for each page using proprietary, patent-pending model. This model evaluates multiple dimensions such as structure, coherence, completeness, syntactic simplicity, and semantic precision to compute score. This score tells us how hallucination-inducing your content is.

Based on these scores, weak pages are flagged, specific sections are highlighted & prioritized for refinement. With these insights, teams can systematically improve corpus quality and success rate of AI automation programs.

Core Purpose

Deploy ai with confidence

• Measures Hallucination-Resistance: The engine assigns scores to assess how well content can be “understood” by LLMs without triggering hallucinations. It focuses on semantic precision, structural integrity, and coherence, going beyond surface readability to predict AI behavior.
• Zero-Hallucination Goal: Scores identify risks like ambiguity, contradictions or incompleteness that could lead LLMs to “invent” details during retrieval-augmented generation (RAG).

How It Works

1. Input Ingestion: Upload or scan an entire documentation corpus (e.g., PDFs, HTML, .md, wikis, manuals) in batch or real-time.
2. Automated Scoring: Uses a multi-factor proprietary algorithm on a scoring scale of 0-100. Aggregate corpus score provides an overall health benchmark.
3. Weak Spot Identification: Flags “weak pages” with low scores and highlights prioritized sections using heatmaps and annotations
4. Output & Recommendations: Generates actionable reports with refinement suggestions. Also integrates with workflows for AI-assisted edits, ensuring fixes are iterative and trackable.

Technical Underpinnings

• Availability via API: RESTful API endpoints for seamless integration, supporting batch scoring, real-time queries, and webhook callbacks; OAuth 2.0 authentication with rate limiting (up to 10,000 requests/min for enterprise tiers) and SDKs in Python & JavaScript
• Security & Compliance: Employs end-to-end encryption for data in transit and at rest, with SOC 2 Type II compliance and GDPR/CCPA adherence; role-based access controls (RBAC) prevent unauthorized access, and automated audit logs track all scoring and refinement activities.
• Scalability: Handles 1,000+ pages in minutes via parallel processing; cloud-agnostic for enterprise deployment.
• Integration Points: Hooks into RAG pipelines (pre-chunking/embedding) and tools like LangChain for end-to-end automation.
• AI Backbone: Powered by fine-tuned LLMs for scoring, with a custom prompt chain to simulate “query stress tests” on content snippets.
• Edge Cases Handled: Multilingual support, version control for docs, and bias audits to ensure equitable scoring.

Pricing

Designed exclusively for enterprise RAG pipelines, AI ops, technical documentation and knowledge governance teams. All tiers include encryption, compliance, and dedicated support.

Tier	Monthly Base	Included Tokens	Included API Calls	Overage: Calls	Key Features
Enterprise Core	$2,000	50 million	250,000	$0.004 / call	Scoring + section prioritization; 1 workspace; 99.9% SLA; email support
Enterprise Scale	$4,800	250 million	1 million	$0.003 / call	All Core + real-time API; custom model tuning; priority support; webhook callbacks;
Enterprise Infinite	$12,000+	1 billion+	5 million+	$0.002 / call	All Scale + air-gapped /on-prem deployment; dedicated TAM; custom compliance

Billing Details

• Token Definition: Input tokens processed (document content + metadata). Output tokens (e.g., refinement suggestions) are free.
• API Call: One HTTP request = 1 call (batch uploads count as 1 call regardless of pages).
• Commitment: Annual contracts unlock 15% discounts + reserved capacity.
• Add-ons:
  – Custom LLM fine-tuning: +$7,000/setup
  – Private VPC endpoint: +$1100/month
  – On-prem license: Contact [email protected]

Get started: Request API key at api.cohgent.ai — 10M free trial tokens included.

FAQs

1. What does CohGent do?
   CohGent identifies and quantifies the hallucination propensity of AI training corpora, then comprehensively mitigates the specific patterns that cause large language models to generate inaccurate or vague outputs. It enhances semantic reliability while preserving the original data structure, tokens, document boundaries, and content unchanged.
   
   
2. How is making a corpus less hallucinatory different from data cleaning?
   Data cleaning involves removing errors, duplicates, or inconsistencies (e.g., fixing typos or missing values) to improve data quality. In contrast, CohGent targets hallucination-prone elements like ambiguous contexts or incoherent associations that cause AI to generate untrue information, leaving the raw data intact.
   
   
3. Is it same as converting unstructured to structured data?
   No. Unstructured-to-structured conversion extracts and organizes data from sources like text or images into formats like tables or databases. CohGent analyzes and surfaces elements in corpora that trigger AI hallucinations, preserving the original format while improving output coherence.
   
4. Then, is it knowledge engineering?
   Knowledge engineering builds structured knowledge bases (e.g., ontologies or rule-based systems) to represent domain expertise explicitly. CohGent, however, works directly on existing corpora to reduce hallucination risks in generative AI, without creating new knowledge frameworks or rules.
   
   
5. How is CohGent different from data observability?
   Data observability monitors data pipelines for issues like freshness, volume, or schema changes to ensure system health, in motion. CohGent specifically addresses hallucination in AI corpora by detecting subtle patterns that lead to unreliable generations, not general pipeline monitoring. Learn More