Business search and insights
Daspire
A vector-powered analytics engine that turns raw business information into searchable, decision-ready insight.
Client
Private client
Location
Remote
Engagement
Data and AI platform
Faster queries
10x
[ 01 ]
The product and architecture challenge
Useful business knowledge existed across raw records that were difficult to search and slow to interpret. Traditional keyword search could retrieve matching words without understanding the question behind them.
The platform needed to return relevant evidence quickly and produce summaries that remained traceable to source data.
[ 02 ]
System design and delivery approach
[ 01 ]
Prepare the knowledge layer
We designed ingestion and chunking around the shape of the source data, then attached metadata needed for filtering and traceability.
[ 02 ]
Combine semantic and structured retrieval
Vector search handled meaning while filters narrowed results by the business dimensions users actually work with.
[ 03 ]
Summarize with evidence
AI-generated answers were built around retrieved context so users could move from a conclusion back to the underlying information.
[ 03 ]
What worked in production
Retrieval quality improved when data preparation was treated as product work, not a background script.
A focused API boundary let the search engine evolve without forcing a rewrite of the interface.
Source-aware summaries made the results more useful for real decisions.
[ 04 ]
Product outcome and impact
OUTCOME 01
The resulting query workflow was reported as ten times faster than the process it replaced.
OUTCOME 02
Teams could ask natural questions while retaining the filters and source context required for business use.
What we delivered
- Data ingestion pipeline
- Vector search API
- AI summaries
- Analytics interface
Behind the scenes
For readers who want the technical detail, these are the specialist building blocks supporting the experience. Clients do not need to select or manage them.
[ 05 ]
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