Funding Landscape

This workflow produces a capital-flow intelligence package for a space, not a drug-program landscape.

Use When

• an investment team wants recent funding activity in an indication, mechanism, or target
• a BD or corp dev team wants the round-size and investor-mix picture for a space they are evaluating
• a portfolio strategist wants to gauge capital momentum, sentiment, or new-entrant pressure in a competitive area
• the question is about money flowing into a space, not about which drugs are in development there

For the drug-program-centric question (which assets are in trials, at what phase, with what mechanism), use competitive-pipeline instead.

Primary Primitives

• enumerate-entities to scope the right indication, mechanism, target, or investor set
• trace-financings for round-level capital activity
• synthesize-evidence for any quantitative claim that will be cited (totals, top-investor lists, deal cadence)

Optional Primitives

• trace-events for post-round announcements, deals, or regulatory milestones tied to recipients
• profile-entity for a focused dive on a notable recipient or investor surfaced by the round set

Output Contract

Return a funding intelligence package that can include:

• scope summary (which indication / mechanism / target / investor set the package covers, and the time window)
• total raised in window (USD), with the confirmed and unconfirmed slices reported separately
• round count and round-type distribution (Pre-Seed through IPO and beyond)
• top recipients by total raised, each with round count and most recent round
• top investors by activity in the space (count of rounds participated, lead-investor signal where derivable)
• deal cadence (rounds per quarter or per month) so the reader can see acceleration or cooling
• notable rounds (top by value, oversubscribed, strategic investor presence, post-IPO follow-on)
• market signal interpretation (acceleration vs cooling, sub-mechanism momentum, investor concentration)
• explicit gaps and boundary conditions

Aggregation Shortcuts

When the question is about distributions, rankings, or trends rather than individual rounds, prefer aggregate_records(entity_type="financing", ...) over enumerate-and-count. Canonical calls:

• Round-type distribution: query="rounds in {scope} by financing type with total value and count"
• Deal cadence: query="rounds in {scope} by quarter since {date}"
• Top investors: query="rounds in {scope} grouped by investor, top 25"

The investors field is M2M-exploded (one round with three investors counts in all three groups). total_value_input_count separates confirmed-value rounds from unconfirmed. Use get_financings for the round-level evidence that backs any claim you cite.

Government grants (NIH, NCI, NIAID, Innovate UK, Bpifrance) will top investor-count rankings if not filtered. For BD/corp dev personas, exclude grant funders or report them in a separate slice.

Core Research Pattern

1. Scope the right ontology entry. Resolve the indication / mechanism / target (or, for a broad area, a top-level indication) with match_entity, or use the recipient-side filters (indication / modality / mechanism / target) surfaced through search_entities("financing", ...). Define the time window explicitly. Decide whether the scope is recipient-side, investor-side, or both.
2. Enumerate the round set. For structured filters use get_financings directly; for ontology-anchored or fuzzy queries use search_entities("financing", ...). Page through the full matching set, not a sample. For the aggregate shape (round-type distribution, deal cadence, top investors), use aggregate_records to get the picture in a single call before drilling into individual rounds.
3. Augment with documents and events. For high-signal rounds (top by value, strategic investor, recent), pull supporting document_ids via read_document, and catch newer rounds with get_financings scoped to the company (financing recency is not in get_recent_events).
4. Reconcile before finalizing. Compare round count and total raised against any prior expectation or anchor knowledge. If counts look suspiciously low, broaden the scope or rerun with a sibling ontology axis (parent indication instead of subtype). If they look noisy, separate the confirmed-value slice from the unconfirmed slice.

Workflow Rules

• treat the unconfirmed-value slice as a separate visible bucket; never aggregate total_value_usd: null rounds into "total raised" without a footnote
• when reporting deal cadence, anchor on quarter-ending dates and call out the trailing-quarter window explicitly
• when reporting top investors, distinguish lead-investor presence (derivable from round documents) from participating-investor presence (the raw investor list)
• pair recent funding cadence with get_recent_events so the reader sees both the round and any post-round announcements (clinical readouts, deals, FDA actions) that contextualize the capital
• for VC and corporate venture investor lookups, the natural axis is aspects=["investments"] on research_entity, not the recipient-side filters
• do not present a funding landscape as proof of clinical or regulatory progress; the two layers are independent and the reader needs both
• distinguish private capital (financing rounds) from public-market signals (market cap, cash, runway from financial statements). The latter lives on get_financials; mention it when comparing public-comp valuations
• when the user is a BD or corp dev team sizing acquisition targets, surface a runway-implication slice: companies with thin recent funding, an old last round, or an aging Series B+ are candidate signals (combine with get_financials on public comps for full posture)
• when the picture needs per-company round histories or investor profiles, gather them in parallel with the research-assistant sub-agent, then reconcile the returned claim sets before aggregating

Fallback Rules

• if the indication fails to resolve, retry with the raw name and move disambiguating text into the optional context hint; if it still fails, broaden to the parent indication and note the scope drift
• if get_financings returns zero rounds for a structured query, retry the same intent through search_entities("financing", ...) and check whether SSF resolves the scope differently before concluding no activity exists
• if the round set is suspiciously sparse, run sibling-axis enumeration (parent indication, broader mechanism class) and preserve the broader set as discovery scaffolding before finalizing
• if the round set is large and noisy, partition into core confirmed rounds (with supporting documents read in-session) and watchlist rounds (structured-only or thin documents) rather than collapsing every round into the headline narrative
• if a notable round's investor list is dominated by stub companies (display_only=True thin profiles), preserve the asymmetry; do not promote a stub-only round to the same evidence tier as a fully-resolved round
• if host web fetch is blocked, augment with search_documents plus read_document against round-linked documents rather than repeated blocked fetches
• if a workflow gap remains after the above (e.g., the user wants a filter that does not exist on get_financings), record it explicitly in the output rather than silently approximating it with a filter that means something else

Persona Notes

• Investment teams typically want recent activity in a thesis space, lead-investor signal, and round-size distribution. Default the time window to trailing 12-24 months and lead with notable rounds plus deal cadence.
• Pharma BD / corp dev typically want acquisition-target screening or deal-target identification. Combine the recipient-side round set with get_financials on public comps and a runway-implication slice. Surface candidates with thin or aging funding as signal.
• In-house pharma portfolio / strategy typically want competitor capital posture and new-entrant detection. Anchor the scope on the same mechanism, target, or indication their internal asset operates in, and pair with competitive-pipeline for the program-level view.