Dognosis BiosciencesDognosis

Capabilities

What we operate, and what we build.

Two layers: the field operations we run today, and the software we build to make those operations measurably better than the paper-and-WhatsApp baseline.

Field operations

The operations layer, codified.

We run population-scale screening camps end-to-end. Every camp is an opportunity to convert operational habit into operational IP — through documentation discipline that compounds across studies and sponsors.

Camp operations

  • Site selection and community engagement
  • Recruitment for asymptomatic, high-risk, and family-history cohorts
  • Eligibility screening, informed consent, and on-site participant flow
  • Standardised camp staffing and supervision structures

Last-mile logistics

  • Cold-chain protocols designed for Indian climatic realities
  • Sample integrity validation under transport stress
  • Vendor networks and on-the-ground operational partnerships
  • Per-camp logistics tailored to semi-rural infrastructure

Sample handling & chain-of-custody

  • QR-tagged collection and tracking from camp to laboratory
  • Time-stamped chain-of-custody at each handoff
  • Documented contamination and seal-integrity checks
  • Source-document discipline for regulatory traceability

CRC capability

  • Documented training curriculum for Clinical Research Coordinators
  • Productivity benchmarks and supervision frameworks
  • Field-condition operating procedures, in regional languages
  • Tacit knowledge codified into written artefacts

Software systems

Five candidate systems we build, one at a time.

We build deliberately — when there is a compelling product reason and a funding source that fits. Each system is sponsor-agnostic by design, and each addresses an operational gap visible from running real camps.

01

Recruitment & eligibility intelligence

Problem

Clinical studies and screening programs in India fail on enrolment, not on science.

What we deliver

A multilingual intake interview that takes a participant's history, structures it against eligibility criteria, and surfaces likely matches. Combines available patient information, community health worker networks, and ML-based pre-screening for asymptomatic high-risk and family-history cohorts.

02

Field-level data capture

Problem

EDC systems are built for hospital coordinators with broadband and laptops — not for camp-based collection in semi-rural India.

What we deliver

Offline-first, voice-enabled, multilingual electronic case report forms with built-in source-document capture. Designed for CRCs whose primary input device is a phone, in conditions of intermittent connectivity and multiple regional languages.

03

Sample integrity verification

Problem

Sample integrity at scale depends on field-level discipline that paper logs can't enforce.

What we deliver

Phone-camera-based verification of seal integrity, condensation or contamination signs on collection masks, ambient temperature logging, and time-stamped chain-of-custody at each handoff. Generates structured operational data and a defensible quality-assurance trail.

04

Adverse event detection

Problem

Outcome tracking is a regulatory expectation. At population scale, follow-up arrives as call notes and WhatsApp messages — not structured data.

What we deliver

An NLP layer that ingests unstructured follow-up communications and surfaces protocol-defined adverse event and serious adverse event signals. Generalises across studies and modalities.

05

Yield optimisation

Problem

Where you run the next camp determines whether you hit enrolment — and most decisions are made on intuition.

What we deliver

A predictive model over aggregate operational data — site yield, cohort types, cost, dropout patterns, catchment characteristics — that recommends where to run the next camp. Operational machine learning, owned by DBPL.

Operating standards

Built for regulatory scrutiny and grant accountability.

ICH-GCPNDCT Rules, 2019Offline-firstMultilingualQR-based custodyCold-chain protocols