Pure vector search misses part numbers, error codes, clause references and names — exactly the terms business questions hinge on
We combine semantic and keyword retrieval with reranking, tuned on your content rather than a public benchmark
Chunking is treated as a real design decision, because a split at the wrong boundary loses the context that made a passage useful
The moment an assistant can surface any document to any employee, you have built an efficient way to leak salary bands
Retrieval is filtered by the requesting user's rights, so the model never sees what they cannot open
Filtering the answer afterwards is not a security model and does not survive a determined question
Most document sets contain three versions of the same policy, two retired and none marked
Recency and authority signals make the current one win, and we hand you the list of conflicts we found
Several teams have treated that list as the more valuable deliverable
Froggik
"DESH Team maintained effective communication throughout the project."
Thanks to DESH Team's work, the client saw increased product recognition within the cryptocurrency community. The team managed the...
Viktoriia Bernatska
Co-Founder
ChainCrafters
"I liked their corporate policy and how they turned to customers and their wishes."
DESH Team delivered the project on time, effectively improving the site's UX and flow. The team took the time to understand the cl...
Kolya Vovkun
CEO, Founder
Dropshipping
"I really like how they treat their clients."
DESH Team successfully completed all deliverables; the branding was a great fit for the client's company, and the website was done...
Tetyana Yarchak
CEO
Fine tuning teaches style and format well and facts poorly, and it cannot cite a source or respect permissions. When your content changes weekly, retrieval updates immediately while a fine tune needs another training run. For most business knowledge, retrieval is the correct tool.
That surfaces immediately, which is uncomfortable and useful. We add recency and authority signals so the current version wins, and hand you the list of conflicts we found along the way.
Retrieval is filtered by the requesting user's permissions before generation. For environments where content cannot reach a third-party model, the whole pipeline runs in your own VPC with open-weight models.
A scored evaluation set built from real questions with verified answers, run on every change to chunking, retrieval or prompts. It is the only reliable way to know whether a change helped or quietly hurt.
A working system over a defined content set typically ships in six to ten weeks, including the evaluation set. Ingestion complexity drives the range more than anything else, and messy sources add time.
