We build AI systems that distill long documents, meeting recordings, research papers, and reports into clear, accurate summaries tailored to your audience and purpose.
Professionals spend a significant portion of their workday reading: reports, emails, meeting transcripts, research papers, regulatory updates, and news articles. Much of this reading is inefficient because the key information is buried within pages of context. AI summarization extracts the essential points, saving hours of reading time while ensuring important details are not missed.
Modern AI summarization goes far beyond extracting key sentences. LLM-powered systems understand the content deeply enough to produce abstractive summaries that synthesize information, highlight implications, and present findings in a format appropriate for the target audience. An executive summary differs from a technical summary of the same document, and AI can produce both.
Arthiq builds summarization systems for specific use cases: meeting transcripts that capture decisions and action items, legal documents that highlight key terms and obligations, financial reports that extract performance indicators and trends, and research papers that distill methodology and findings. Each system is optimized for its specific content type and audience.
Different content types require different summarization approaches. Arthiq builds specialized summarization pipelines optimized for each content type you process. Meeting summarization identifies speakers, captures key discussion points, lists decisions made, and extracts action items with owners and deadlines. Legal document summarization highlights obligations, deadlines, termination clauses, and liability provisions. Financial report summarization extracts KPIs, trends, and notable changes from prior periods.
For long documents that exceed model context windows, we implement hierarchical summarization that processes sections individually, then synthesizes section summaries into a coherent overall summary. This approach maintains accuracy across documents of any length while capturing cross-section themes and connections.
Multi-document summarization handles scenarios where information is spread across multiple sources. Research synthesis across multiple papers, competitive analysis from multiple sources, and regulatory change impact across multiple documents all benefit from our multi-document approach that identifies common themes, contradictions, and unique points across the input set.
A one-size-fits-all summary rarely serves every stakeholder. Arthiq builds summarization systems that produce audience-adapted outputs. The same meeting recording can generate an executive brief highlighting strategic decisions, a project manager summary focusing on timelines and dependencies, and a team member summary listing specific action items and deadlines.
Output format is fully customizable. Summaries can be structured as bullet points, narrative paragraphs, structured JSON for programmatic consumption, or formatted reports with sections and headers. Length is configurable from brief abstracts to detailed overviews, and the level of detail adapts accordingly.
We also implement template-based summarization where your organization defines the structure and required sections for specific document types. Monthly board report summaries always include the same categories. Contract reviews always cover the same risk areas. Templates ensure consistency while AI handles the content extraction.
Summarization must be faithful to the source material. A summary that introduces information not present in the original, omits critical details, or misrepresents the source is worse than no summary at all. Arthiq implements faithfulness verification that checks generated summaries against the source content, flagging any claims that cannot be traced back to specific source passages.
Our verification pipeline uses a combination of automated techniques and optional human review. Automated checks identify factual claims in the summary, locate their corresponding evidence in the source, and flag any discrepancies. Key statistics, dates, names, and figures receive additional validation against the source data.
For high-stakes applications like legal and financial summarization, we implement dual-model verification where a second AI model independently reviews the summary against the source and identifies any potential issues. This defense-in-depth approach catches errors that a single model might miss.
Summarization is a capability that delivers immediate, tangible time savings for knowledge workers across your organization. From the first day of deployment, your team spends less time reading and more time acting on the information that matters.
Arthiq delivers summarization systems integrated into your existing tools and workflows. Summaries can be generated automatically when documents are uploaded, when meetings end, or when reports are published. Our systems process content in real time and deliver summaries to your preferred channels.
Contact us at founders@arthiq.co to discuss how AI summarization can help your team process information more efficiently and make better-informed decisions faster.
Our team will build a summarization system that distills your documents, meetings, and reports into clear, accurate summaries your team can act on immediately.