Stale storage
Your embedding job re-indexes on schedule, but nothing keeps the table underneath it current.
Solutions / AI Data Foundation
Your agents answer from data that is seconds old. Not days old.
Dataddo keeps the storage behind your RAG pipelines, chatbots, and agents continuously filled with current, validated, PII-safe data - and lets agents pull the latest directly, with no storage layer to run at all.
You choose how the data is delivered. We keep it flowing.
Architecture Blueprint: Building a data architecture for AI
Read the whitepaper →
The problem
Confident answers from broken data are the most expensive kind.
An AI initiative rarely fails on the model. It fails on the data underneath - and a confidently wrong answer from stale or corrupted data costs more than no answer at all. The AI use cases with the highest return - fraud checks, live recommendations, agents acting on the current state of an order or account - only pay off when the data behind them is fresh, correct, and safe to use. That is a data-layer problem, and it is the one Dataddo solves.
Silent corruption
An upstream export breaks, nulls flood a column, and your index absorbs it before anyone notices.
Leaked PII
Once a personal identifier is embedded in a vector store or fine-tuned into a model, you can't take it back.
Dataddo addresses each problem at the pipeline level, before the data ever reaches your AI.
Architecture
Three ways to feed your AI. Pick per use case, combine freely.
AI systems don't read SaaS APIs directly - they read from storage that something keeps up to date. Dataddo is that something, in whichever shape your use case needs:
RAG corpora & fine-tuning
Warehouse or object storage
Scheduled, quality-checked data delivered into BigQuery, Snowflake, Databricks, S3, or Azure Blob Storage. Your embedding and retrieval jobs read from it on their own schedule.
SaaS data integration →Operational AI
Real-time CDC replica
An always-current mirror of your database, typically sub-second behind your source, with deletes captured so your AI never answers from rows that no longer exist.
Real-Time CDC →Agents, zero infrastructure
Direct retrieval via MCP
Agents in Claude, ChatGPT, Cursor, or Gemini connect to the Dataddo MCP server and pull the latest extracted data from SmartCache (Dataddo's built-in storage that holds recent extractions for direct retrieval) - no warehouse to stand up, no database to operate.
UI, API, MCP →They compose: one support chatbot can answer order-status questions from the CDC replica, re-embed its knowledge base from the warehouse, and pull the latest campaign numbers through MCP.
| Warehouse / lake | CDC replica | MCP direct retrieval | |
|---|---|---|---|
| Freshness | Per extraction schedule | Real-time, continuous | Latest completed extraction |
| History | Full time series | Current state | Recent extractions |
| Storage you operate | Your warehouse | Your replica DB | None |
| Best for | RAG, fine-tuning | Latency-sensitive operational AI | Agents, zero infrastructure |
Get the data layer right and these use cases return their investment; get it wrong and every downstream AI decision inherits the error.
Capabilities
Fresh, correct, and safe - handled at the data layer.
Bad records never reach your index
The Data Quality Firewall validates every row at the flow level - null checks, type checks, anomaly rules, per column. In blocking mode, records that fail never land in the table your embedding job reads.
PII is removed before it can be embedded
You can't delete a name from a vector store or a fine-tuned model, so Dataddo handles it at the source: exclude sensitive columns entirely, or replace them with deterministic hashes your pipeline can still join and match on.
Incremental re-embedding, not nightly full rebuilds
Every row carries a stable key and an extraction timestamp, so your pipeline re-embeds only what changed since its last run.
Data that explains itself to your agents
Dataddo outputs the connector's own metadata alongside the data - what each dataset and field means, and which fields are sensitive - so text-to-SQL and retrieval agents get semantic grounding instead of guessing.
Replication that recovers itself
A built-in CDC Supervisor health-checks every replication process and restarts any that stall, resuming exactly where it left off - no committed change lost, nobody paged.
Fresh on your terms
Set the corpus freshness your answers need - down to short intervals where the source API allows - then stop thinking about it. When a source API changes, that's Dataddo's problem to fix, not yours.
Speed - measured
Real-time for agents. Seconds for full loads.
Speed is what makes data to AI real rather than aspirational. The highest-return use cases - fraud checks, live recommendations, agents acting on the current state - only work when the data is genuinely real-time; and the corpora behind RAG and fine-tuning only stay useful when full loads keep pace.
Dataddo delivers speed for both.
Freshness - operational AI & agents
Change data capture, sub-second at the median.
351 ms
p50 latency
568 ms
p90 latency
35,000/s
events sustained
SQL Server CDC, internal benchmark.
Learn more about real-time CDC →Throughput - RAG corpora & warehouse loads
Full loads in seconds, not overnight jobs.
1.8 s
per 1M rows · 10-column table · ~22x faster
28.9 s
per 1M rows · 250-column table · ~5x faster
SQL Server to BigQuery full load, versus the source's own bcp bulk-copy utility. Internal benchmark.
Learn more about high-performance loads →
Time to value
An afternoon. Not an engineering quarter.
Building an AI data foundation sounds like a six-month platform project. With Dataddo it isn't: connect a source, pick the destination your AI reads from, set your quality and PII rules once - and every extraction from then on arrives fresh, validated, and safe to embed. No pipeline code to write, no connectors to maintain, and when a source API changes, that's our problem to fix, not yours.
And your agents don't have to wait for infrastructure at all: point any MCP client at Dataddo and they're querying fresh, governed data - and managing the pipelines behind it - the same day.
The sooner your AI reasons over current data, the sooner it earns its keep - and with Dataddo that is an afternoon of setup, not a quarter-long platform project.
See data move to AI in your stack.
Book a technical session with an engineer - bring your sources, your warehouse, and your AI use case, and leave with an architecture. Or connect your first source now and have a governed corpus this afternoon.