Resources

How Sangam works

How the base is built, and how an answer and a fit score are made.

What the base is

Sangam is three layers, each built on the one below. Nothing in a higher layer is allowed to state a fact the data layer does not hold.

Data
The vetted registry: organisations, funders, registrations, grants, scores and documents. Every row carries its source and the date it was fetched.
Query
An agent that answers only from the data layer, shows the steps it took, and cites every claim back to a row.
Match & draft
Rubric-based fit scoring and proposal-readiness work, where human vetting remains the baseline the model is measured against.

Where the data comes from

Every row records its source URL, the source's name and when it was fetched, so any field can be traced back to where it came from.

NGO Darpan
The government registry of NGOs: registration, credentials such as 12A, 80G and FCRA, office bearers and working sectors.
Funder filings and publications
CSR reports, annual reports and statutory disclosures, read by Sangam Scout from sources whose terms allow it.
News and signals
News coverage of organisations and funders, collected and held for review before it is shown.
Organisations' own documents
What an organisation uploads to its Doc vault. Private to that organisation unless it chooses otherwise.

From source to Registry

01 Ingest
Statutory registries, funder publications and news land as raw documents with full provenance.
02 Vet
A person checks registrations against their sources, corrects errors, and records every adjustment in an audit log. Nothing enters the base unattributed.
03 Tag
Organisations, funders and programmes are tagged against the shared taxonomy — sectors, SDGs, geographies — so matching is mechanical where it can be.
04 Publish
Vetted entries appear in the Registry, and the agent answers only from them, citing each claim.

How an answer is made

Ask Sangam is an agent that reaches the base only through read-only database tools. For each question it:

  1. Reads the question and works out which organisations, funders or scores it is about.
  2. Checks which tables hold that information, then writes a read-only query against them.
  3. Answers from the rows that come back, putting a citation next to every fact; each one links to its Registry entry.
  4. Says so when the base has no answer, instead of filling the gap from general knowledge.

Every answer shows these steps above it, so you can see which tables were read and why. It reads a web page only when you ask it to, and then says where the text came from.

How a fit score is made

A versioned rubric — v1 has seven criteria worth 100 points; v2 splits the same criteria into weighted sub-criteria — is applied to each organisation and funder pair. Every criterion score carries a written justification, and any manual correction is kept as an adjustment with its reason, so the audit trail ships with the score.

What confidence means

High
The question names entities with full, vetted profiles and the rubric applies cleanly. Expect precise, cited numbers.
Medium
A real match exists, but parts of the evidence are thin or unverified. The answer says which parts.
Low
The question falls outside the base. Sangam says so — no rubric is applied and nothing is invented.

Known limits

The base covers a small vetted cohort today, and signal collection is manual by design. Fit scores are single-rater assessments, not ground truth — which is why every number links back to its criteria, justifications and adjustment history.