You already have an LLM classifier.
You don't have typed events.
Most teams hack together three or four tools: a news wire, a filings parser, a scraper, a LLM classifier. It kinda works — until it doesn't. Hyperfeed replaces the stack with one normalized event stream, source-cited and schema-versioned.
A hallucinating pipeline you now own forever.
- ×Scrapers break every time a source redesigns — you’re paying engineers to fix them
- ×LLM classifier hallucinates event types 4–12% of the time, silently
- ×No ground-truth audit — when the model is wrong you don’t know until someone complains
- ×Costs scale linearly with volume — your Anthropic bill now outpaces the ROI
- ×No canonical entity graph — you still have to resolve "Meta" vs "META"
- ×Schema drift whenever someone changes the prompt, breaking every downstream consumer
Typed events. Sourced. Dedup'd. Replayable.
- ✓We own the scrapers. We fix them within hours of breakage — it’s our SLA
- ✓Human-in-loop audit on every critical event — published precision 96–99%, per family
- ✓Weekly audit report with precision/recall by family and source
- ✓Flat tier pricing — stop paying LLM costs that scale with market activity
- ✓Canonical entity graph — ticker · LEI · parent · subsidiary · aliases
- ✓Versioned schema (schema_version) — deprecations announced 90 days in advance
Where the gap shows up.
Every row below is a question your platform team will ask. Our answer in blue; the alternative in gray. No fine print.
| Question | Hyperfeed | Roll-your-own LLM |
|---|---|---|
| Precision on confirmed eventshow often is the classification correct | 96–99% (audited weekly, published) | Whatever your model is this week — you measure |
| Recall on official actionshow many real events make it into the feed | 98.2% measured against SEC ground truth | Unknown — you’d need to build the ground truth |
| Who fixes the scrapers when sites change?you or your vendor | We do. 4h response SLA on broken ingestors | Your on-call engineer, forever |
| Cost at 10,000 events/dayLLM + infra + engineer time | Flat $299/mo (Pro) | $4,000–$18,000/mo and climbing |
| How is a hallucination caught?before it hits your trading system | Multi-source cross-check + human audit on critical | When a user reports it |
| What’s your schema governance?deprecations, additions, breaking changes | Versioned schemas, 90-day deprecation | Prompt_v3.py — whoever merged last |
| How do you handle ambiguity?contradicting sources | assertion_type reflects the state; events merge on confirmation | Model picks one; you don’t know which |
| Latency from source to emitall-in, source to your code | 108s p50 | Whatever your stack is — usually 4–20min |
The same FDA rejection. Theirs vs ours.
Monday, 4:47pm ET. ALDX receives a Complete Response Letter for reproxalap. Here's what hits your systems from each API.
{
"id": "scrape_17450_aldx_20260413",
"classification": "regulatory_action", // too broad
"confidence": 0.73, // model's best guess
"entity_guess": "Aldeyra", // not resolved to ticker
"summary": "The FDA declined to approve Aldeyra's drug for dry eye disease...",
"embedding": [0.038, -0.21, 0.77, /* 1,534 more floats */],
"source_url": "https://www.reuters.com/business/healthcare...",
"scrape_version": "v14 (last broken 2026-03-28)"
// no drug id, no submission type, no cycle number
// no lifecycle, no audit, no ground truth
// your on-call engineer is now paged
}{
"event_id": "evt_20260413_aldx_fda_crl",
"event_type": "fda_approval_declined",
"assertion_type": "fact",
"audited": true,
"schema_version": "2026-04-20",
"entity": {
"ticker": "ALDX",
"name": "Aldeyra Therapeutics",
"lei": "529900W0O7QKGDLPGW09"
},
"payload": {
"regulator": "FDA",
"product_id": "reproxalap",
"submission_type": "NDA",
"cycle_number": 3
},
"confidence": {
"overall": 0.99,
"human_reviewed": true,
"audit_id": "aud_20260413_2847"
},
"family_precision_30d": 0.984
}Three reasons, in their own words.
We stopped burning engineers on duplicates.
News APIs emit the same story from six outlets as six separate records. Every consumer team writes their own dedup logic. Hyperfeed merges on entity + event_type + effective_at and attaches all sources as evidence. One event, many sources.
After:
event_id is the only key we need.Allegations aren't the same as facts.
News wires flatten "WSJ reports" and "company confirms" into one severity level. That's fine for humans. It's a disaster for auto-execution. Hyperfeed tags every event with assertion_type — allegation, trusted_report, fact.
allegation to human review, auto-routes factto systems. We couldn't do that with a news wire.Stories change. Your database should too.
An FDA rejection reported at 4:47pm becomes "official_announced" at 5:03 when the company files its 8-K. Hyperfeed represents this as one event with status transitions. A news wire represents it as two unrelated stories with different URLs.
lifecycle object: detected_at, announced_at, effective_at, confirmed_at, refuted_at. You can replay any event from any point in time.“We were paying six vendors for the same data at six different latencies. We replaced five of them with Hyperfeed.”— Head of Market Data, L/S equity fund · $4B AUM
Swap in. Dual-run. Cut over.
We don't ask you to rip out your existing pipeline on day one. Most teams dual-run for two weeks, then cut traffic when they've validated the schema against their own backtests.
Subscribe to the family you need.
Pick a single event family — usually regulatory or leadership. Point a webhook at your queue. Done.
POST /v1/subscriptions
{ "family": "regulatory",
"webhook_url": "https://you.co/hook",
"assertion_types": ["fact","trusted_report"] }Dual-run and diff.
We ship a difftool that runs against your existing pipeline's output and highlights where hyperfeed caught events sooner, merged duplicates, or classified differently.
hf diff \ --their-export their_feed.jsonl \ --date-range 2026-03-01:2026-03-31 > 142 events - hyperfeed earlier > 38 duplicates collapsed > 7 class conflicts
Cut over. Keep the old feed as backup.
Flip your primary. Most teams keep the legacy feed on standby for 30 days. After that, the vendor contract lapses. Most teams find Hyperfeed replaces 2–3 existing tools at a fraction of the combined cost.
hf subscribe \ --families all \ --deliver kafka://your-cluster/events \ --replay-from 2026-01-01 > 14,207 historical events backfilled
Stop parsing headlines. Start reading events.
See the diff yourself. The 7-day delayed feed shows the same events your current vendor misses. Compare side-by-side, no meeting required.