Case study · AI startup · CPG product innovation
Web data infrastructure for a multi-geography CPG product innovation AI.
A deep-tech CPG product innovation AI platform needed multi-language, multi-country consumer-signal pipelines at a scale and cadence their in-house team could not sustainably run. We built the recurring extraction layer their NLP engine feeds on.
Engagement summary
Multi-source NLP pipelines feeding a tens-of-billions-signal foresight engine.
A technical co-founder brought us in early. The client had a foresight engine designed to absorb tens of billions of consumer signals across retail, social, and review surfaces — but keeping the pipelines healthy across languages and regions was taking engineering away from model work.
The problem
Multi-language, multi-country consumer signal extraction is a full-time job — and not the one the team wanted to be doing.
CPG buyers want signal from the markets they operate in, not from English-language sources alone. That means scraping retail listings, social discussion, and review platforms across languages, character sets, and regional layout conventions.
Each region adds its own regulatory posture, anti-bot behaviour, and schema drift. Maintaining stable extraction at this scale is a recurring ops job that competes with model work for engineering time.
The client's engineering team wanted to spend their cycles on the foresight engine — the model, the feature store, the API layer their CPG customers consume. Not on proxy rotation and CAPTCHA-of-the-week.
They needed a pipeline partner who could deliver structured, clean, multi-language signal at cadence — on an SLA that made their model training schedules plannable.
The solution
Managed multi-language pipelines feeding the foresight engine on a contract schedule.
We operate recurring scraping pipelines across retail listings, social discussion, and review surfaces in more than twenty countries. Content is extracted in its native language, cleaned for NLP readiness, and delivered as structured feeds on the cadence the client's model training schedule requires.
Language coverage includes right-to-left scripts, logographic scripts, and regional Latin variants. Each pipeline has its own QA gates — missing fields, suspicious null rates, and geographic anomalies all trigger review before delivery.
When a source site changes, we patch the pipeline without interrupting the model team's cadence. The client's NLP engineers see clean feeds landing on schedule, not broken scrapers.
CPG CONSUMER SIGNAL PIPELINE — RECURRING MONTHLY
Retail listings ┐
Social posts │──▶─ Multi-language extraction (20+ countries)
Review platforms ┘ │
▼
┌──────────────────────────────┐
│ Schema normalization + QA │
│ (null-rate, anomaly, geo) │
└───────────────┬──────────────┘
▼
┌──────────────────────┐
│ Structured feed → │
│ client's NLP engine │
└──────────────────────┘
Recurring cadence · Multi-language · SLA-backed · Model-team-ready The numbers
What this looks like in production.
-
Geographies
20+
countries covered with native-language extraction.
-
Signal ingest
Tens of billions
of consumer signals analyzed by the client's foresight engine.
-
Buyer type
Technical
co-founder; bought on engineering reliability, not ops overhead.
// this pattern repeats
If your model team is spending time patching scrapers instead of improving models — this is the division of labour you want.
Multi-language, multi-geography, SLA-backed recurring feeds. You spend cycles on the model. We spend cycles on the source sites.