Nearly every disappointing RAG system fails for the same reason: retrieval returns the wrong passages, and no amount of prompt engineering fixes an answer built on the wrong source material. TechEsperto builds retrieval systems where the retrieval itself is engineered and measured, with permission filtering applied at query time, citations attached to every claim, and evaluation against real questions from your users. If you have a proof of concept that works on demonstration queries and fails on real ones, that is usually a retrieval problem we can diagnose quickly.
generative AI development work treats ingestion as core infrastructure.
We scope RAG engagements around a defined question set and a defined corpus, because a system that answers anything about everything answers nothing well. The applications below reflect what enterprises most often ask us to build, always with retrieval quality measured against real user questions rather than curated examples.
Assistants answering staff questions from policies, procedures, and documentation with citations, scoped to what each user is permitted to access.
Retrieval over product documentation and support history, serving agents directly or powering customer-facing answers with a defined escalation path.
Search and question answering over specifications, API documentation, and engineering knowledge, where precision matters more than conversational fluency.
Retrieval across contract sets for clause comparison, obligation extraction, and review support, with citations back to the exact clause and document version.
Systems retrieving across large document collections with synthesis and citation, used where analysts currently spend hours locating relevant material.
Replacing keyword search with semantic retrieval where users search in natural language, connected through our API development practice.
RAG quality comes from the retrieval pipeline rather than the model. The capabilities below are where we concentrate engineering effort, and they are the difference between a system that answers correctly on real questions and one that performs well only on the examples used to build it.
Documents split along their actual structure, preserving tables, procedures, and heading context, since naive fixed-size chunking is the most common source of poor retrieval.
Vector search combined with keyword matching, because semantic search alone misses exact identifiers, part numbers, and terminology users search for directly.
Access control applied during retrieval so users never receive content from documents they cannot open, enforced at the index rather than in the response.
A reranking stage over initial results, which typically improves answer quality more than any change to the generation prompt.
Every claim linked to its source passage and document version, so users verify rather than trust and reviewers can audit answers after the fact.
Test sets built from real user questions with retrieval accuracy measured independently of generation, so regressions are caught at the layer that causes them.
RAG projects need retrieval proven before generation is tuned, because a good prompt over bad context produces a confident wrong answer. We build and measure retrieval first, then layer generation, then evaluate end to end against questions your users actually ask.
We review the documents, their structure and quality, and collect real questions users need answered, which defines both the corpus and the evaluation set.
Extraction, structure-aware chunking, and metadata enrichment built as a repeatable pipeline, since content changes and manual reindexing does not scale.
Hybrid retrieval with reranking, measured against the question set for whether the correct passage appears, before any generation work begins.
Response generation constrained to retrieved context with citations attached and explicit handling for questions the corpus cannot answer.
Answer accuracy, citation correctness, and refusal behavior measured against the evaluation set, giving a defensible quality baseline before launch.
Production deployment with query logging and feedback capture, so gaps in the corpus and retrieval weaknesses are identified from real usage.
RAG delivers most reliably where staff spend significant time locating information in documents nobody has read end to end. The sectors below are where we see the clearest cases, with regulated industries benefiting most because citation and permission control are requirements there rather than preferences.
Policy, product, and regulatory documentation where advisers need precise answers with citations they can rely on in client conversations.
Clinical protocols, formularies, and policy documents where staff need immediate answers and every claim must trace to an approved source.
Contract sets, case material, and precedent libraries where retrieval across large collections replaces hours of manual document review.
Specifications, maintenance manuals, and technical documentation where the correct answer depends on the exact model and revision in question.
Product documentation and support history serving both internal agents and customer-facing answers with defined escalation paths.
The relevant evidence for a RAG engagement is a corpus of comparable structure and permission complexity rather than a comparable industry. Our AI and data practice covers ingestion pipelines, retrieval engineering, and evaluation infrastructure, with engagements documented in our case studies library.
Engagements where tables, procedures, and versioned documents required structure-aware chunking rather than generic text splitting to retrieve correctly.
Projects where different users were entitled to different content, with filtering enforced at retrieval and verified as part of acceptance testing.
Projects where an existing system underperformed and the fix was in chunking, hybrid retrieval, and reranking rather than in the model or the prompt.
RAG cost is driven by corpus complexity, permission requirements, and how much document processing the source material needs. Clean structured documentation in one repository is a modest engagement; mixed-format content across several systems with per-user permissions is larger. We recommend starting with a scoped corpus and question set.
A short fixed-price engagement assessing document structure, building a working retrieval prototype, and measuring accuracy against real questions.
A defined corpus and question scope with milestones and acceptance criteria tied to measured retrieval and answer accuracy rather than feature completion.
A named team covering ingestion, retrieval, and application work billed monthly, which suits organizations expanding across multiple corpora.
Query analysis, retrieval tuning, corpus expansion, and model updates, scoped monthly since knowledge systems improve mainly through analysis of real usage.
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Cost depends on corpus complexity, permission requirements, and how much processing the source documents need. Clean documentation in one repository is far smaller than mixed-format content across several systems with per-user access rules.
A prototype with measured retrieval accuracy typically takes a few weeks. Production builds take a few months, with document ingestion and permission integration usually accounting for more of the timeline than the AI components.
Usually retrieval rather than the model. Common causes are fixed-size chunking that breaks document structure, semantic search without keyword matching, and no reranking stage. We diagnose by measuring retrieval accuracy separately from answer quality.
Yes, and it must. Access control is applied during retrieval so users never receive content from documents they cannot open. Filtering after generation is not sufficient, since the content has already been exposed to the model.
Through an ingestion pipeline with change detection that reindexes updated content automatically. Systems built on a one-time import drift out of date within weeks and lose user trust quickly once answers are visibly stale.
Yes. Retainers cover query analysis to find gaps in the corpus, retrieval tuning, expansion to new content sources, and revalidation when embedding or language models are updated by their providers.
Tell us what content you hold, where it lives, and what questions your users need answered. We will respond within one business day with a view on corpus readiness, retrieval approach, and a scoped prototype phase. Book a free consultation through ourcontact page.
Tell us what you’re building. Our team will get back to you within one business day with a clear, no-obligation plan.
