Triomics GTM brain

Triomics sells oncology AI agents that turn longitudinal patient records into cited outputs for cancer care and clinical research teams.

category

Oncology clinical workflow AI

market profile

  • text: Oncology teams review lengthy fragmented records to screen trials, prepare visits and abstract registry data.
  • text: Memorial Sloan Kettering announced deployment for active trial matching, while Mount Sinai described a systemwide deployment.

personas

  • title: Cancer center clinical research director
    pains:
    • Coordinators manually review complex records against many changing trial criteria.
    • Eligible patients may be missed when screening cannot cover every visit.
  • title: Oncologist or practice leader
    pains:
    • Visit preparation requires assembling treatment timelines and biomarkers from records.
    • Chart preparation can spill into time outside the clinic.
  • title: Cancer registry and quality director
    pains:
    • Manual abstraction pulls skilled staff away from other work.
    • Reviewers need traceability back to the chart for every field.
  • title: Life sciences trial operations lead
    pains:
    • Feasibility and site selection depend on hard-to-extract patient information.
    • Finding eligible patients can delay enrollment.

icp

  • text: US academic cancer centers and community oncology practices with substantial chart volumes, research operations or registry reporting obligations.
  • text: Oncology trial sponsors seeking site identification, prescreening and feasibility support are a separately marketed segment.

message pillars

  • text: Build for the particular complexity of cancer records.
  • text: Use one longitudinal patient view across workflows.
  • text: Provide evidence for abstraction and human review.
  • text: Give coordinators a screened worklist before clinic.

objections

  • objection: Can clinicians trust generated patient facts?
    response: Evaluate citations and accuracy on representative records with clinical reviewers before workflow deployment.
  • objection: Will it fit our EHR and clinical process?
    response: Map integration, handoffs and human review in a pilot with coordinators, clinicians and IT.
  • objection: How will sensitive data be protected?
    response: Have institutional privacy and security teams review data handling and contracts; use a de-identified record for the initial demo.

competitors

  • name: Massive Bio
    why: AI-driven oncology trial matching competes for patient screening and enrollment workflows.
  • name: Tempus
    why: Oncology data and clinical trial matching overlap with Triomics' research applications.

keywords

  • oncology trial matching
  • cancer registry abstraction AI
  • oncology chart review automation
  • oncology pre-charting
  • clinical trial site feasibility
  • oncology EHR data extraction

voice

  • clinically grounded
  • specific and evidence oriented
  • practical

signals

  • text: A cancer center is expanding trial accrual or seeking to screen more patients without adding equivalent coordinator time.
  • text: A practice is investing in registry abstraction or pre-visit chart preparation capacity.

stack

  • OncoLLM

pricing

model: unknown

motion

sales_led

channels

  • website
  • clinical publications
  • cancer center partnerships
  • LinkedIn

confidence

high

Founders

  • name: Sarim Khan
    title: Founder
    role: Founder
    bio: I am a chemical engineer with previous research experience in tissue engineering and neuroscience. The lack of vertical integration in the clinical trial space inspired me to work on Triomics.
  • name: Hrituraj Singh
    title: Founder
    role: Founder
    bio: Before starting Triomics, I was a Researcher at Adobe Research, where I worked on language models and reinforcement learning problems. I now use the same technologies to interpret oncology patient charts.

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