For data scientists
From raw documents to retrieval-ready data
Embed, index and connect structured and unstructured data in one place. Choose the models, inspect every step, and hand agents memory they can search and traverse.
Accounts are free. No credit card needed.
Or explore a memoryEmbedded 40,118 tickets · 6 clusters · top 3 shown
| Cluster | Tickets | Share | Example |
|---|---|---|---|
| Billing sync errors | 9,412 | 23% | TKT-88213 |
| SSO login loops | 7,060 | 18% | TKT-90145 |
| Export timeouts | 5,233 | 13% | TKT-87702 |
The problem
The model is the easy part, the data plumbing isn't
Most of a retrieval or classification project is parsing, chunking, embedding and keeping it all in sync.
01
Unstructured data, everywhere
Tickets, PDFs, filings and notes don't fit a table, so they're left out of the analysis or handled by a one-off script.
02
Glue code for every experiment
Each new embedding model or chunking strategy means rewriting the loaders, the index and the eval harness.
03
Notebooks that don't ship
A result that works in a notebook still needs a pipeline, a schedule and access control before anyone else can use it.
What you build
The building blocks of AI on your data
Each one runs as a pipeline you can open, rerun on new data and hand to an agent.
Embeddings and vector indexes
Chunk and embed documents and rows with the model you choose, at the level you need, down to the clause.
A vector index agents and apps can search
Knowledge graphs
Resolve entities across sources into one graph, then ask how things connect and follow the answer hop by hop.
A graph of entities and relationships
Clustering and topics
Group tickets, reviews or notes by meaning, and see examples from each cluster before you name it.
Clusters with counts and example records
AI labels at scale
Classify, score or extract row by row with the model you pick, each value citing the fields it used.
New columns that cite their sources
Document extraction
Layout-aware OCR turns scanned and native PDFs into typed columns, ready for features.
Typed fields, each linked to its page
Model comparisons
Swap completion and embedding models per step and compare quality, latency and cost on your own data.
Side-by-side results on the same inputs
Try it
Explore a memory three ways
The same data as tables, as a graph of entities and as a semantic space. Switch views and ask it something.
Entities and the relationships between them. Ask how things connect, and follow the answer hop by hop.
Select an entity to traverse
How it works
Experiment fast, then ship the same thing
What you build to explore is already the pipeline other people use.
- 1
Bring structured and unstructured data
Warehouses, buckets, Hugging Face and Kaggle datasets, documents and SaaS exports.
- 2
Choose the models per step
Pick embedding and completion models per task, including open-source models or your own endpoint.
- 3
Publish to memory
Indexes and graphs land in Virtual Memory, where agents and apps query them with the access you set.
AWS S3
Snowflake
Databricks
HuggingFace
Kaggle
PostgreSQL
Google Drive
OpenAIBuilt with other teams
Your indexes become everyone's memory
What you build is what agents search, what analysts chart and what business teams ask about.
Security
Run sensitive data through approved models
Model choice and data access are configured, not hard-coded, so security can sign off once.
SOC 2 Type II
Corvic holds a SOC 2 Type II report. Ask for it during your vendor review.
Private VPC deployment
Deploy Corvic in a private VPC. Tenants are isolated from each other.
Your keys, encrypted data
Customer-dedicated key management. AES-256 encryption at rest and TLS 1.2+ in transit.
Access per person and per source
Permissions are scoped per person and per source, so people and agents only see what they're cleared for.
Zero-retention model options
Choose models with zero-data-retention options where the provider supports it, and swap them in config.
SSO and removal on demand
Sign in with SSO. Corvic is GDPR and CCPA compliant, and your data can be removed on demand.
Common questions
What data scientists ask first
Yes. Pick completion and embedding models per task, including open-source models or your own endpoint, and swap them in config.
Yes. Pipeline steps are SQL or Python you can open, read and edit, not a black box.
Pipelines run on a schedule, so indexes and graphs refresh from the source instead of going stale after the first build.
Indexes and graphs are published to Virtual Memory. Agents query them through the Agent Harness, scoped to the sources they're allowed to read.
Build your first index on your own data
Bring a dataset or a folder of documents and see it embedded, indexed and searchable. Free to start, no credit card.
Accounts are free. No credit card needed.
See Virtual Memory