Internal Product · Live in Production
SyncDaily: An autonomous ingestion, AI curation, and SEO publishing pipeline running continuously in production.
SyncDaily ingests high-volume technical sources, deduplicates stories, runs multi-stage LLM summarization with schema validation, and serves low-latency REST and sitemap feeds.
Operational Context
Why off-the-shelf demos fail this workflow.
Most AI content and research pipelines work on five hand-picked examples in a notebook, then break within 48 hours of unattended operation: upstream RSS/HTML layouts change, duplicate stories flood the queue, LLM outputs drift from the required schema, and unthrottled API calls spike monthly token spend.
We built SyncDaily as an unattended production system to solve those exact failure modes: ingesting external feeds around the clock, clustering related coverage, generating structured briefings under strict validation rules, and exposing fast, cacheable API endpoints.
System Architecture
Four-stage production pipeline
01 · Ingestion & Deduplication
Scheduled workers fetch upstream sources, normalize text, and cluster duplicate coverage before spending LLM tokens.
02 · Guardrailed LLM Curation
Structured prompts extract key facts, categories, and reading metadata with strict JSON schema validation and retry caps.
03 · Editorial & Quality Gate
Automated checks verify completeness, length bounds, slug uniqueness, and metadata integrity prior to publication.
04 · Edge-Cached API & Sitemaps
Fast Python/REST service delivers cursor-paginated feeds, slug lookups, and chunked XML sitemaps with stale-while-revalidate caching.
Key Capabilities & Stack
Engineered for reliability and clear handover
Idempotent ingestion queues
Upstream timeouts, malformed feeds, and duplicate URLs are caught and retried cleanly without corrupting downstream tables.
Token-budgeted LLM pipeline
Pre-filtering and deterministic clustering ensure model calls run only on net-new items, keeping daily inference costs predictable.
Cursor pagination & SEO endpoints
Dedicated REST endpoints power instant article lookups, cursor-based feeds, and automated search-engine sitemap generation.
Production Takeaways
Lessons we apply to client builds
Takeaway 01
Never call an LLM before deterministic deduplication and input cleaning—pre-filtering cut wasted model calls dramatically.
Takeaway 02
Treat every model output as untrusted external input: validate against a strict schema and quarantine failures instead of publishing broken records.
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