Helm Gen™

Helm Gen™ makes use of a pivotal advancement in the realm of Large Language Models (LLMs) called Retrieval-Augmented Generation (RAG). RAG combines the strengths of LLMs with retrieval mechanisms, making it particularly valuable for business use cases because it only uses what you want it to use. Helm Gen™ uses RAG to provide a Generative AI solution that operates within brand bounds, while limiting ‘hallucinations’ by limiting the amount of content it is exposed to.

Think Chat GPT, but instead of exposing your brand to the entire history of the internet, we only give it the content you want it to have.

Ideal for

Banking

Retail

Telecommunications

Healthcare

Travel and Hospitality

Insurance

Education

Media

E-commerce

Benefits

Automate customer-facing brand functions

Provide customer support and self-service for common queries

Generate and qualify leads

Provide technical support based on brand FAQs

Generate real-time responses to account inquiries, transaction details

Process customer claims

Assist with financial education

Help your customers with bookings and research

Manage policies and accounts

Provide personalised learning, student support and financial education

Control over the domain-specific data your customers are exposed to

Data accuracy and recency

Lower cost compared to customised LLMs

HelmGen™ technical capabilities

Integration with external knowledge bases, including databases, PDFs, company FAQs, Webpages etcetera

API Integration with external data sources (web search, external databases)

Realtime information retrieval

Multi-modal retrieval support, able to retrieve text, images, meeting transcripts, spreadsheets, powerpoint slides and other media formats

Custom retrieval pipelines tailored to enterprise needs.

Bespoke guardrails (PII identification, hallucination detection, profanity guard, attempted jailbreak detection)

Hybrid Search (RAG + keyword search combined)

Metadata filtering

Dynamic Query Expansion, which automatically enriches user queries by adding relevant synonyms, related terms, or domain-specific jargon to improve retrieval accuracy

Domain-specific, fine-tuned retrieval models – custom-trained retrieval models for specific industries to enhance domain relevance.

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