Our capabilities
Retrieval and semantic search
Search over your own documents that returns the right passage and cites where it came from, rather than a confident paragraph you cannot verify.
The problem
Most retrieval projects fail at ingestion, not at the model. Bad chunking and stale indexes produce answers that look right and are not.
What the work involves
- Ingestion that handles the formats you actually hold, including scanned PDFs
- Chunking tuned against your documents rather than a tutorial default
- Hybrid retrieval, keyword and vector, because pure vector misses exact terms
- Reranking, and an honest measurement of whether it earned its latency
- Citations back to the source passage, so answers can be checked
- Incremental reindexing, so the answer changes when the document does
Typical stack
pgvector
PostgreSQL
Elasticsearch
Qdrant
LangChain
Python
Indicative rather than fixed. The right choice depends on what you already run, and inheriting a stack your team knows usually beats introducing a better one nobody can maintain.
Related capabilities
Let's work together
Got a retrieval and search problem?
Technical detail welcome. The more concrete the problem, the more useful the first reply.
- UK based
- Direct engineer contact
- Documented handover