How automated decisions are made on IVL

What it does: Produces a composite 0–1000 score representing IVL's opinion of a startup's strength on traction, team, capital efficiency, market timing, and data completeness.

Inputs: Public funding records, registry filings (MCA), website/product signals, founder LinkedIn data, sector benchmarks, and (where available) self-disclosed founder questionnaires. No biometric or health data is used.

Logic class: Rule-based weighted aggregation across ~13 signal families, with a large-language-model (Claude/Gemini) layer applied for qualitative attributes (e.g. team depth narrative summarisation). Final score is deterministic given the same input snapshot.

Consequence for the listed startup: The score affects discoverability in public rankings, eligibility for the Pioneer cohort, and the order in which investors see the company. It does not determine access to funding, credit, employment, insurance, or any service that would trigger high-impact ADM thresholds.

What it does: Measures how complete the metadata IVL holds about a startup is across 13 fields (website, founders, funding rounds, sector, stage, MCA filings, etc.). It is a hygiene indicator, not a quality judgment.

Inputs: Presence/absence of values in our own database. No external inference.

Logic class: Simple field-count percentage.

Consequence: Startups with low completeness are flagged for enrichment. It does not affect VedAI scores or any user-visible ranking.

What it does: Suggests founders for invitation into the 25-person Pioneer soft-launch cohort, and proposes startup ⇄ investor pairings.

Inputs: VedAI score, sector, stage, location, declared availability, prior IVL platform engagement signals.

Logic class: Filter + rank using the same VedAI weights. No protected-attribute features (gender, religion, caste) are used as inputs.

Consequence: A shortlist is generated; final selection is made by a human (Shelvin Narayan or delegate). The matching system never sends invitations directly.

If a VedAI score, completeness flag, or cohort decision affects you, you can:

• Request a written explanation of the inputs that produced the result.
• Submit a correction request via /data-corrections.
• Ask for a human review. We commit to a 14-day SLA for first response.
• Lodge a complaint with the Data Protection Board of India under the DPDP Act 2023 if you are not satisfied with our response.
• IVL's grievance officer is Thiru Pendyala — thiru@indianventurelabs.ai. We commit to acknowledging grievances within 7 business days as required by the DPDP Act.