A retrieval-augmented support copilot that grounds every answer in a company's own knowledge base — with citations, freshness signals, and human review pathways when confidence drops.
Every part of NeuraDesk was designed around one goal: an answer a support lead can defend, with a source they can click through to.
Dense embeddings plus BM25 sparse retrieval, so lexical matches and semantic matches both surface — with a reranker deciding the top set.
Every response cites the source passages that produced it, linked back to the doc, section, and last-modified date.
The copilot flags when a source is stale, and downweights outdated content in retrieval based on document freshness rules.
When retrieval quality falls below threshold, the copilot says it does not know and escalates to a human agent instead of guessing.
Thumbs, freeform notes, and "wrong source" flags feed a review queue and drive the next round of retrieval and prompt tuning.
Ops teams run golden question sets, view retrieval traces, and compare model or prompt changes side-by-side before rolling them out.
Four phases that put retrieval evaluation on the critical path from week one.
We inventoried sources, permissions, and freshness, and defined the questions the copilot had to answer well.
Chunking, embedding, and index choices were locked against a working baseline on real documents, not a demo set.
A golden question set drove iteration on rerankers, prompts, and grounding rules until quality held across categories.
Shipped behind feature flags with tracing, cost dashboards, and regression alerts wired in from day one.
Retrieval libraries, vector search, model providers, and the orchestration and observability that keep answers honest in production.
Workflows where the right answer lives in the company's own documents and users need it faster than manual search allows.
Deflects repetitive questions with grounded answers and hands complex tickets to agents with a suggested draft.
Employees ask questions across wikis, playbooks, and design docs and get answers with links to the source section.
Battlecards, competitive intel, and pricing answers surfaced fast during live calls, cited to the source.
Grounded Q&A over policy libraries and internal guidance with an audit trail of which clause was used.
New hires get answers about processes, tools, and people from a single surface instead of hunting across systems.
Turns long ticket threads and doc chains into concise summaries with links, ready for a shift handoff.
Qualitative shifts the support and knowledge teams felt after NeuraDesk was live.
Every response comes from the actual knowledge base — not the model's memory — so the answer moves when the docs move.
Each answer carries the source passages that produced it, so QA and compliance can reconstruct any interaction.
Questions the team had been answering the same way for months got a consistent, grounded answer without a ticket.
The same policy question stopped getting three different answers depending on which agent picked it up.
RAG systems fail slowly. We built NeuraDesk around evaluation from the first week — because that is what separates a demo from a support tool teams actually trust.
Learn more about us →The people who designed the retrieval and grounding wrote the code that shipped. No handoffs to junior teams mid-project.
A golden question set existed before the first ingestion job ran. It shaped every retrieval, prompt, and model choice after.
Ingestion, retrieval, generation, monitoring, and cost — one team accountable across the whole pipeline.
Prompts, retrieval, and generation are decoupled so the client can swap embedding or generation models as costs shift.
Practical answers on scope, timing, data, and how a copilot like this reaches production.
Let's map your first agent. We'll scope a working prototype and share a realistic delivery plan.
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