Data Panda lifts data out of your CRM, support desk, product catalogue, knowledge base and warehouse, embeds it on a known schedule, and lands it in Qdrant collections. From there your assistant, your search box and your recommendation engine read from the same vector index, and the warehouse keeps the audit trail of what was indexed and how it scored.
Qdrant is a vector similarity search engine and database written in Rust, released under the Apache 2.0 licence. Andrey Vasnetsov and Andre Zayarni founded the company in Berlin in 2021 after Andrey built a production-grade vector engine from scratch and put it on GitHub. The team now ships the open-source database alongside Qdrant Cloud on AWS, GCP and Azure, a Hybrid Cloud option that runs the data plane in your own Kubernetes, and a Private Cloud variant for air-gapped deployments.
The data model is straightforward: a collection holds points, and each point carries one or more dense vectors, optional sparse vectors, and a JSON payload with the metadata you want to filter on. The HNSW index handles approximate nearest-neighbour search, payload indexing makes filters cheap, and quantisation cuts RAM use for large collections. REST and gRPC are both first-class, with official clients in Python, JS/TS, Go, Rust, Java and .NET.
For a Data Panda customer the role is clear: Qdrant is the vector layer, not the system of record. The warehouse is where the source documents, tickets, products and knowledge base live; Qdrant is where the embedded representation lives so an assistant or a search box can find the relevant slice in milliseconds. Pulling collection state back into the warehouse is what makes the loop measurable, because hit-rate, drift and query quality only show up when the vector side and the source side sit in the same model.
Collection state, query logs and drift tracked next to the source data so RAG quality is measurable.
Product, content and account similarity served from one vector index instead of nightly batch jobs.
Replace literal keyword search with hybrid dense plus sparse retrieval on the same content.
A list of concrete reports, automations and AI features we have built on Qdrant data. Pick the one that matches your situation.
Why should we run a vector database next to our warehouse instead of using vector search inside the warehouse?
Warehouse-side vector functions are useful for batch scoring and offline analysis, where a query that returns in a few seconds is fine. A user-facing assistant or search box needs sub-100-millisecond retrieval at the 99th percentile, with payload filters and hybrid scoring on every call. That is what Qdrant is engineered for: HNSW with one-stage filtering, sparse plus dense, quantisation. We typically keep both, with the warehouse as the source of truth and Qdrant as the live serving layer.
Cloud, Hybrid Cloud or self-hosted on our own Kubernetes?
Qdrant Cloud is fully managed on AWS, GCP and Azure and removes the operational work. Hybrid Cloud runs the data plane inside your own Kubernetes against the Qdrant control plane, which is the usual pick when the embeddings sit on customer data that cannot leave your perimeter. Self-hosted on the Apache 2.0 build is the cheapest at scale and the most flexible, but you carry upgrades, snapshots and replication. We size the three options against the actual collection size, query rate and data-residency rules before recommending one.
How do we know our RAG is getting better and not just shipping more often?
By pulling Qdrant collection state and query logs back into the warehouse and joining them to the source data. Hit-rate, the share of queries that return zero or low-score matches, the drift between embedding versions, and the questions that lead to negative feedback all become measurable on the same model as the source content. Without that loop, RAG quality is whatever the last person to test it remembers.
Finance gets a clean read on what the vector layer costs per use case, not per cluster. Collection size, upsert volume and query rate per workload (RAG, search, recommendations) join to the rest of the stack, so the AI line on the cloud invoice can be defended with usage data instead of estimates.
Sales sees lookalike accounts ranked by similarity to closed-won deals on CRM and product-usage signal, served from one Qdrant collection. Reps stop ranking the pipeline by gut feel because the warehouse and the vector index agree on what 'similar to our best customers' means.
Support and platform teams get an assistant that cites the right macro, KB article or past ticket instead of guessing, and they get the operational picture of the vector layer in the same warehouse the rest of the stack reports on. Empty-result queries, upsert lag, query-latency percentiles and embedding-version drift sit next to source-data freshness, so RAG quality stops being an opinion.
You keep the reporting tool you already have. We connect it to the warehouse where your Qdrant data lives.
OAuth authentication. Read-only by default. We sign a DPA and your admin keeps the keys.
Data flows into your warehouse on your schedule. Near real time or nightly, your call. You own the data.
We build the first dashboard, workflow or AI feature with you, then hand over the keys. Or we stay on for ongoing delivery.
We set up the foundation. Your team builds on top.
Best fit Teams that already have a BI analyst or data engineer and want to own the build.
We build the whole thing, end to end.
Best fit Teams without in-house BI or dev capacity. You tell us what you need and we deliver it.
You do. It lands in your warehouse, on your cloud account. We don't resell or aggregate it. If you stop working with us, the warehouse stays yours and keeps running.
Near real time for most operational systems. For heavier sources we schedule hourly or nightly. You pick based on what the reports need.
No. If you don't have one, we help you pick one and set it up as part of the first delivery. Common starting points are Snowflake, Microsoft Fabric, or a small Postgres start.
Source records from your CRM, support desk, product catalogue, knowledge base or warehouse get chunked where needed, embedded with the model you chose, and upserted as Qdrant points in the right collection. Each point carries the dense vector, optional sparse vectors for hybrid search, and a payload with the metadata you want to filter on (tenant, language, product, status, source id). The original record stays in the warehouse so the embedding can be rebuilt or swapped without losing the source.
Collection state (size, point counts, last upsert per source), query logs where you collect them, scoring metadata and any feedback events you capture from the assistant or the search box. That is what makes hit-rate, drift between embedding versions and content gaps visible on the same model as the source data, instead of guessing from sampled traces.
We review your Qdrant setup and the systems around it. Together we pick the first thing worth building.