Turn filings, news, and wires into structured market events at ingest.
Raw text feeds carry market-moving events buried in prose. This architecture runs every document through the Market Intelligence analyzer at ingest - corporate actions, credit events, executive changes, and regulatory signals arrive at downstream systems as structured, scored data.
Illustrative reference architecture. The scenario, volumes, and economics below are modeled - not a named customer case study. Validate every number against your own workload.
The scenario
A market-data platform aggregates filings, news wires, and regulatory announcements for institutional users. Events are distributed as full-text documents; every downstream consumer re-implements its own extraction, tagging is inconsistent, and a CEO departure in a German filing can sit unread until someone's NLP pipeline notices.
Before / after architecture
Before
With Sentiqra
Decision ownership stays with you
- Sentiqra tags events; your materiality rules, instrument mapping, and alerting thresholds decide what is worth acting on.
- Event tags carry configuration ID and fingerprint - a signal attached to a filing replays identically in a compliance review.
- Documents over the per-request input limit are prepared customer-side under sentiqra/prepare/v1 (/docs/input-preparation): deterministic chunk boundaries, a manifest binding each request to source offsets, and an explicit aggregation step - a chunk receipt verifies the submitted snippet, and document-level tagging is your recorded aggregation, not an implied whole-document prediction.
- You choose which feeds to analyze: premium wires on everything, long-tail blogs on a sampled basis.
Illustrative economics
Both columns are modeled from explicit assumptions. Do not treat these as guaranteed savings - model them against your own workload.
Current approach (modeled)
- Every consumer maintaining its own extraction/NLP stack
- + missed-event cost: material changes discovered late
- + inconsistent tagging across products built on the same feed
With Sentiqra (modeled)
- N documents/day x ~K prepared chunks per document (K = document length / input limit)
- = N x K analyzer runs/day at current Sentiqra pricing
- One consistent event taxonomy for every downstream product
Centralizing extraction replaces per-consumer NLP stacks. Model document volume and how many consumers currently duplicate the work. See current pricing.
Failure behavior
Enterprise architects care about what happens when analysis is unavailable almost as much as accuracy. Define these states explicitly.
Sentiqra available
Documents ship enriched - raw text plus event signals with confidence scores.
Analyzer latency spike
Ingest queue buffers; documents distribute tagged-slightly-late rather than untagged.
Sentiqra unavailable
Fallback: distribute raw documents untagged, mark degraded state - consumers see the same feed they had before.
Quota / spend cap reached
Explicit state - prioritize premium feeds, degrade long-tail coverage gracefully.
Source feed outage
Nothing to tag - replay backlog idempotently when the feed recovers; request IDs dedupe.
Illustrative operational impact
One taxonomy for every consumer
An executive-departure signal means the same event code in English, German, Japanese, and Chinese documents - downstream products stop disagreeing.
Material events surface at ingest
Credit events, covenant breaches, and leadership changes are tagged when the document arrives - not when someone's batch job runs.
Compliance-replayable enrichment
Every tag on a distributed document can be regenerated - configuration ID, fingerprint, identical signal set.
What to measure in your evaluation
Run both approaches against the same labeled production sample. Compare:
The right migration decision should come from your workload - not a generic savings percentage.
Build the reference architecture
Start with 5,000 free analyzer runs and evaluate Sentiqra against your current pipeline.
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