Hiswai, a leading knowledge management company, set out to modernize how research reports are generated across sectors like finance, policy, tech, and emerging technologies. Traditional approaches to research compilation involved manual data gathering, repetitive structuring, and inconsistent quality. To overcome this, Hiswai developed an AI report generation system that automates everything from document ingestion to full report assembly, turning scattered knowledge into structured insights in minutes.

The problem: fragmented data and slow insights before the AI report generation system

Hiswai’s clients faced growing challenges in generating structured reports from large volumes of scattered data. Analysts were spending significant time manually researching, gathering documents, extracting relevant information, cleaning up the content, and formatting it into usable reports. This process was not only time-consuming but also inconsistent, with report quality varying depending on who created it.

The lack of automation limited scalability, slowed down insight delivery, and made it difficult for decision-makers to act quickly. Existing tools offered little support for contextual understanding or structured synthesis, leaving teams dependent on manual workflows and vulnerable to delays and errors. Hiswai needed a scalable, intelligent solution.  

They decided to build an AI report generation system that could ingest content, retrieve relevant context, and generate well-structured reports instantly.

The solution: an AI report generation system for scalable, intelligent reporting

To solve these problems, GoML engineered Hiswai a modular AI report generation system that automates ingestion, retrieval, structuring, and generation in three distinct phases:

Phase 1: Document ingestion and retrieval in the AI report generation system

• Trigger: A FastAPI endpoint (/generate-report) initiates the AI report generation pipeline.

• S3 ingestion: JSON files are pulled from S3, cleaned, deduplicated, and ingested into OpenSearch.

• Vector indexing: Each article is embedded using Bedrock embeddings and stored in a unique vector index.

• Hybrid search: Combines vector and keyword matching to retrieve the most relevant content.

Built-in retries and readiness checks ensure that the AI report generation system is robust and production-grade.

Phase 2: Automated table of contents creation via AI report generation system

• Smart prompting: Retrieved documents are used to build LLM prompts, submitted via AWS Bedrock.

• Dynamic structuring: The AI returns chapter titles, section names, and optional subsections based on the topic.

• Validation layer: Outputs are schema-validated and enriched with source document metadata.

The AI report generation system ensures every report has a coherent structure grounded in the source content.

Phase 3: Full report generation with enhancement logic

• Asynchronous section creation: Sections and subsections are generated concurrently using Bedrock LLMs.

• Intelligent expansion: Short content is passed through an enhancement planner to meet target word counts.

• Fallback resilience: If generation fails, placeholder content is inserted to maintain flow.

• Anti-repetition logic: Summaries are tracked and reused to avoid duplicating content across chapters.

The result is a comprehensive, readable, and reliable report created entirely by the AI report generation system.

The impact: scalable reporting with the AI report generation system

Hiswai’s AI report generation system transformed how enterprise research reports are produced and consumed:

• 80% faster report creation cycle

• 100% consistent structure across reports

• Highly scalable, works across topics and datasets

• Reduced analyst effort, freeing up time for high-value tasks

Lessons for knowledge and research teams

Common pitfalls to avoid

• Relying on manual compilation of complex reports

• Lacking prompt engineering for LLM reliability

• Overlooking validation and fallback content

Advice for enterprise content teams

• Start with a PoC on your most time-intensive reports

• Leverage hybrid retrieval to improve LLM accuracy

• Design prompts and structure templates around real users’ needs

Ready to deploy your own AI report generation system?

Let GoML help you automate research report generation with scalable GenAI pipelines, just like we did for Hiswai.

Reach out today to build your enterprise-grade AI report generation system.