Prompt-response pairs, citations, model metadata, and token counts from ChatGPT - structured for training, RAG, and model evaluation workflows.
Data Solutions | LLMs
LLM Data APIs Engineered for Scale and Low Latency
Bringits delivers structured LLM data from leading AI models - including prompts, responses, citations, and model metadata - at the scale and freshness your business requires.
One integration pattern. One normalized schema. Production-ready LLM datasets from day one.
Supported platforms
LLM datasets for leading AI platforms
Access structured AI model data through dedicated APIs, normalized schemas, and production-ready delivery across leading LLM platforms.
Structured responses from Google Gemini with citation extraction, model version tracking, and normalized fields for cross-platform benchmarking.
Overview
Production-ready datasets, built for high-volume performance
Bringits delivers production-ready data pipelines for leading AI models - structured, normalized, and delivered on your schedule at the volume your business requires.
Dedicated per-model APIs
Each AI platform has a dedicated API with model-specific response structures, metadata, and citation handling - normalized into predictable, structured data.
Reliability at any volume
99.9% delivery reliability with infrastructure built to scale from pilot volumes to millions of prompt-response records.
Flexible delivery
Choose bulk datasets, recurring feeds, or on-demand API calls, delivered to S3, Snowflake, BigQuery, webhooks, or directly through our API.
Benefits
Why teams choose Bringits for LLM data
One data layer for multiple AI models - structured, reliable, and delivered consistently without the infrastructure overhead.
Multi-model coverage, one integration
Access leading AI models through a consistent integration pattern and unified delivery, without building separate pipelines for every platform.
Managed delivery reliability
When AI platforms change their models, response formats, or interfaces, Bringits adapts the pipeline so your data workflows keep running.
Structured JSON, ready to load
Prompts, responses, citations, model metadata, and other relevant fields - normalized and ready for downstream use.
Predictable cost at scale
Pay for delivered data, not infrastructure overhead. Scale collection volumes with predictable costs.
Managed infrastructure
Bringits manages the infrastructure behind every request, so your team can focus on analyzing and using the data instead of maintaining collection systems.
Features
AI model data built for speed and scale
Dedicated API per LLM platform
Model-specific endpoints built around each platform's response structure, metadata, citations, and model behavior.
High-throughput, low-latency delivery
Fast responses for on-demand requests, with recurring data feeds that scale to millions of prompt-response records.
Multiple output formats
JSON, NDJSON, CSV, or Parquet, with consistent encoding and structured fields across supported AI models.
Cloud-native delivery
Stream data through webhooks or deliver batches to S3, Snowflake, Google BigQuery, and Azure Storage on the cadence you need.
Normalized cross-platform schema
Consistent structures for prompts, responses, citations, and model metadata across supported platforms - one schema built for downstream analysis.
Flexible collection cadence
Run data collection on demand or on recurring schedules using the same pipeline - from targeted model queries to large-scale datasets.
Pricing
Start free, scale when you're ready
From a no-commitment free tier to enterprise scale.
Pay only for records that deliver.
Free
Free tier $0
- 1,000 LLM records every month - free
- Zero commitment - no credit card required
- Go live in minutes, not weeks
Most popular
Pay as you go $1 / 1K records
- Pay only when data lands - never for failed requests
- Priority support when you need it
- No lock-in - scale up or pause anytime
Enterprise
Custom Let's talk
- Built for high-volume data workloads
- Better rates as your volume grows
- A named partner from day one
- Premium SLA - uptime you can build on
FAQ
Questions we get from LLM data teams
Which LLM platforms do you support?
ChatGPT and Gemini today, each with a dedicated API and production-grade delivery. We add new platforms on request as your data needs expand.
What is included with every API?
Everything needed to run at scale - dedicated endpoints, a normalized schema, high-throughput delivery, and managed infrastructure. One contract, no separate infrastructure vendors to manage.
How is this different from Bright Data or Oxylabs LLM scrapers?
Bringits focuses on finished, normalized datasets delivered on a schedule against an SLA, with a dedicated API per platform and one schema across all of them - so your team works with the data, not the collection stack.
What data fields do you capture from LLM responses?
Prompts, responses, citations (URLs and snippets), model metadata, and timestamps - normalized across ChatGPT and Gemini.
What can teams use LLM data for?
Tracking brand visibility and citations in AI answers, benchmarking model responses, monitoring answer changes over time, and building evaluation or training datasets.
Can we run the same prompts across ChatGPT and Gemini?
Yes. Submit a prompt list once and receive comparable structured responses from both platforms. Same schema, side-by-side benchmarking out of the box.
How fresh is the data?
Up to you. On-demand API calls return fresh responses, and scheduled feeds support daily, weekly, or custom cadences for monitoring and dataset refresh workflows.
Can we trial it before committing?
Yes. Start on the free tier with 1,000 LLM records every month, or scope a pilot with your own prompts to evaluate response quality, field coverage, and delivery speed on real data.
Stop fighting rate limits. Start collecting LLM data.
Start free with 1,000 LLM records every month.
