Intellia

Dominate Your Market with AI-Driven Competitive Insights​

Your AI-Powered Competitive

Intelligence Platform for Pharma

In the hyper-competitive pharmaceutical landscape, having instant access to organized intelligence is paramount. Intellia is the AI-driven competitive intelligence platform that not only delivers real-time insights but also acts as your central knowledge command center, giving pharma insights and CI teams an unparalleled advantage. We cut through the information overload, delivering laser-focused insights and ensuring all your critical knowledge is unified and easily accessible.

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Where AI Transforms Pharma CI

Intelligent Article Summarization

Transforms lengthy information into concise summaries, boosting team efficiency and accelerating strategic decision-making.

Smart News Deduplication and Curation

AI filters and groups news, removing redundancies so your team focuses on unique, impactful developments.

Comprehensive Data Coverage, Zero Effort

Monitor the entire competitive landscape – news, trials, conferences, and your internal documents for a complete competitive view.

From Data to Decisions, Faster

Intellia transforms raw data into clear, actionable insights and visualizations for confident, rapid decisions.

AI-Powered Library and Knowledge Engine

Your secure repository for precise and contextual information retrieval - instantly surfaces critical CI, eliminating scattered searches.

Experience the Distinct Intellia Advantage​

Focus on Strategy: Reclaim analyst time from research to drive strategic initiatives.

Gain Market Agility: React swiftly with AI-powered insights from the web and internal data.

Identify Opportunities & Threats: Proactively monitor the market and your knowledge for key trends.

Unlock Hidden Insights: Connect web and internal data to reveal unique strategic advantages.

Our Success Stories

AI-Powered Knowledge Assimilation, Curation, and Insights Retrieval for Competitive Intelligence (CI) Workflows

Daily CI workflows require extensive manual effort in sourcing intelligence, managing repositories, and extracting insights. As the repository size grows from tens to hundreds of files, spanning multiple modalities like text, images, and recordings, managing and querying intelligence becomes overwhelming. Artificial Intelligence (AI) presents a transformative solution, enabling automated tagging, summarization, and context-based search for efficient insights retrieval.

Problem Statement

Pharma CI analysts at DataZymes faced significant challenges in tracking an increasing number of indications while supporting multiple clients. Manual sourcing of intelligence consumed nearly a third of the workday, leaving limited time for research, curation, and reporting. Long workdays became routine, as analysts needed to accommodate late evening client calls, leading to burnout and inefficiencies.

Solution Approach

Expanding the team through hiring was an obvious but costly and time-consuming solution. Instead, a structured segmentation of the CI workflow identified key bottlenecks and AI-powered solutions:

Implementation of AI-Powered Solutions

1. Automation of Intelligence Gathering
Event-triggered automation was implemented using Python scripts on a cloud platform, leveraging available API endpoints (e.g., clinicaltrials.gov), RSS feeds, and email alerts. This significantly reduced manual web browsing time, bringing it down by ~60% per indication.

2. Intelligence Storage and Management
Sourced intelligence was automatically categorized, labelled, and stored in structured databases based on indications, drugs, targets, and news sources. PDFs and images were also indexed for better retrieval, enabling streamlined intelligence management.

3. AI-Powered Intelligence Search: Retrieval-Augmented Generation (RAG)
A RAG-based search solution was implemented by indexing both structured and unstructured databases. Using vector embeddings, the system retrieved relevant content and parsed it through an LLM, enabling faster and more accurate insights. This solution cut research and compilation time by 50%.

4. AI-Assisted Alert Curation
An LLM-driven text summarization solution enabled analysts to generate paragraph or bullet-based summaries of multiple news articles with minimal input. Analyst feedback during development refined the model, leading to a 67% reduction in alert curation time.

These AI-powered solutions were deployed as microservices and integrated into a user-friendly front-end interface to assist DataZymes’ CI team.

Outcomes

Following extensive validation and benchmarking, these solutions were incorporated into Intellia – DataZymes’ CI platform. The AI-enabled features improved analyst efficiency by up to 50%, allowing smaller teams to track more indications with greater accuracy. Additionally, the optimized workflow created a more sustainable work-life balance for analysts, reducing the strain of manual intelligence management.

Conclusion

Through the implementation of AI, DataZymes transformed its competitive intelligence workflows. The automation of intelligence gathering, AI-driven search, and summarization solutions significantly enhanced efficiency, ensuring rapid and accurate insights delivery. The Intellia platform now offers a scalable and customizable AI-powered CI solution, paving the way for next-generation pharmaceutical intelligence workflows.

Indication review and prioritization to define value inflection strategy for an early stage candidate

A top US based pharmaceutical company focusing on developing and commercializing innovative therapies for treatment of oncology, was looking to conduct due diligence in the post-PD-(L)1 landscape, to explore the market opportunity for its own early assets in this space. With several Pharma companies focusing on the Immuno-oncology specialty, PD-(L)1 market has become rather saturated, thereby sparking new interest towards post-PD-(L)1 setting, and its underlying prospects from a clinical development perspective.

This treatment setting within the post-PD-(L)1 setting is interesting owing to pre-specified patient selection criteria such as patients responding to PD-(L)1 therapies (often referred to as “Responders”) vs. those who don’t respond (Non-Responders), also categorized as Relapsed, Refractory or progressed patients. Furthermore, a comprehensive understanding of the definitions by clinical trial investigators vis a vis the communication used by the company is necessary to derive the exact meaning of each term used for such patient stratification.

With several looming questions, the client’s Insights Team sought to answer the following business questions –

  • To understand the evolving landscape of treatment post-PD-(L)1 across solid tumors
  • To identify the emerging trends / competitors in post PD-(L)1 oncology
  • To recommend the most potential and promising indication for investigation in post-PD-1 setting

Approach & Output

The DataZymes team followed a systematic approach to prioritize indications based on clinical attractiveness.

1. Creation of a Master database

A comprehensive database identifying each clinical trial being studied in post-PD-(L)1 setting. This included:

  • Identification of the relevant solid tumors wherein PD-(L)1 therapies are considered the Standard of Care
  • Segmentation of clinical trials relevant to analysis, based on further deep-dive into its inclusion & exclusion criteria

2. Mapping & profiling

Mapping each trial with parameters including molecule, MOA, indication, clinical data, responders/non-responders.

3. Trend Analysis

Presented high level analytics to highlight specific trends and insights:

  • Helped in shedding light into the overall potential within the post- PD(L)-1 setting with the key indications which are investigated the most.
  • Further insights into the most explored MOA, the combination therapies that are being studied, key players developing in the space, as well as other details such as clinical efficacy and safety benchmarks.

Based on the findings and messages, the following were the actionable insights derived:

  • The percentage of success or failure of the client molecule in the proposed indication and patient segment.
  • Competition intensity – Threats and challenges by indication.

Impact

The output provided client ability to strategize by triaging indications and identified new indications that were not previously championed internally.

Realign their clinical investigation to focus towards a more potential and optimal patient segment and clinical development strategies.

‘This initial analysis has been very helpful, and has set the base for further probing & discussions’
- Associate Director, Market Insights