LlamaIndex developers build the retrieval layer of AI applications: parsing difficult documents, designing ingestion pipelines, selecting index types, routing queries, and measuring retrieval accuracy separately from generation. Companies hire them because most disappointing RAG systems fail at parsing, chunking, or ranking rather than at the model, and those failures are invisible without measurement.
When a retrieval system gives a wrong answer, the model usually did its best with poor context. The table was flattened during parsing, the clause was split across chunks, or the right passage ranked seventh. TechEsperto Solutions provides developers who find and fix that layer.
Our engagements cover difficult document parsing, ingestion pipelines with incremental updates, index design and topology, query routing across multiple indexes, query transformation for complex questions, and retrieval evaluation. A large share is remediation on systems that already work adequately and need to work well. Clients weighing approaches often start from our comparison of RAG and fine-tuning before committing to an architecture.
Layout-aware extraction that preserves table structure, reading order, and figure associations, with optical character recognition where documents are scanned. Parse quality sets the ceiling for everything downstream.
Change detection, upserts, deletions, and deduplication so a corpus stays current without full reprocessing. Includes caching so a chunking change does not mean re-parsing thousands of documents.
Choosing between vector, summary, keyword, and graph representations, often combining several. Topology is decided against your actual question types rather than by defaulting to a single vector store.
A router directing each question to the index or engine suited to it. Comparison questions, aggregate questions, and lookups take different paths, which is what makes all three work well.
Decomposing multi-part questions into sub-questions, generating hypothetical answers to improve matching, and multi-step retrieval where one lookup informs the next. Applied selectively, since each adds cost.
A labelled question set, retrieval metrics separate from answer quality, and automated regression on every corpus or configuration change. This is what turns tuning from guesswork into engineering.
Screening tests whether a candidate can diagnose a retrieval failure. Anyone can build a vector index over clean text. Far fewer can explain why a table-heavy PDF corpus performs badly, choose chunk boundaries from document structure, or design metadata filters that make permission-aware retrieval possible. Where a corpus sits alongside structured data, projects are frequently staffed with our data warehousing team so both sources can be queried coherently.
Different readers suit native PDFs, scanned images, spreadsheets, presentations, and web content. Choosing per format and validating output against the source is the only reliable way to avoid silent content loss.
Structure-aware splitting on headings, sections, or semantic boundaries rather than fixed lengths, with overlap tuned against measured retrieval performance rather than set to a default and forgotten.
Document type, date, owner, department, version, and access level captured at ingestion. Good metadata enables filtering that narrows the search space dramatically before any similarity comparison happens.
Vector search for semantic similarity, summary indexes for broad questions, keyword indexes for exact identifiers, and graph representations where relationships between entities carry the answer.
Cross-encoder reranking on retrieved candidates, similarity cutoffs to discard weak matches, and metadata-based reordering. Returning nothing beats returning irrelevant context that produces a confident wrong answer.
Stable identifiers, content hashing, and version tracking so updates replace rather than duplicate. Corpora without this accumulate contradictory copies of the same document within months.
Most clients begin with a retrieval audit, because it identifies whether the problem is parsing, chunking, ranking, or genuinely the model, and that determines everything afterwards. Options then include a parsing and ingestion build, index redesign on an existing system, a full retrieval application, an embedded engineer, and a corpus maintenance retainer. Our standard arrangements are described in our engagement models. Transparent pricing, an executed NDA, and full intellectual property transfer apply throughout.
We take a sample of your documents and questions, measure where retrieval fails, and report whether parsing, chunking, ranking, or configuration is responsible. Findings are actionable by your own team.
Layout-aware parsing, structure-aware chunking, metadata extraction, incremental update handling, and parse failure alerting. Usually the highest-value work available, since everything downstream depends on it.
Restructuring topology, adding routing, and introducing reranking on a system that already works. Measured against your current baseline so the improvement is demonstrable rather than asserted.
End to end delivery covering ingestion, indexing, routing, generation, citation display, and evaluation infrastructure. Priced against acceptance criteria including a retrieval accuracy threshold.
One specialist inside your sprints for continuous retrieval work. Your team absorbs parsing and evaluation practice through daily contact, building capability that remains after the engagement.
Covering ingestion monitoring, parse failure investigation, freshness verification, index rebuilds, and regression runs. Corpora degrade quietly, so this is the arrangement clients regret skipping most often.
Improvement without measurement is rearrangement. We build a question set from what users actually ask, then measure retrieval separately from generation so it is clear which stage is failing. Hit rate and rank position matter more than answer quality at this stage, because a system retrieving the right passage at position eight will improve enormously from reranking and not at all from prompt work. Chunking variants are tested against the same questions, reranking is added only where it earns its latency, and every corpus change runs through regression. Clients working with TechEsperto Solutions therefore get accuracy figures they can defend.
Sampled genuine questions with the correct source passages identified by people who know the material. Synthetic questions generated from the documents flatter the system and hide its real weaknesses.
Whether the correct passage was retrieved is a different question from whether the answer was good. Conflating them means weeks spent on prompts while a ranking problem goes unaddressed.
Knowing the right passage appears in the top twenty but at position eleven points directly at reranking. Knowing it never appears at all points at parsing or chunking. The metric names the fix.
Several strategies run over the same corpus and evaluated against the same questions. Results frequently contradict intuition, which is precisely why the comparison is worth running rather than reasoning about.
Reranking improves accuracy and costs time and money. We measure the gain per query type and apply it selectively rather than adding a stage to every request by default.
New documents, parser upgrades, and configuration adjustments all run against the labelled set before release. Retrieval quality regressions are otherwise discovered by users, which is the expensive way.
Architecture follows corpus more than industry. A library of clean technical manuals needs a different design from a scanned archive or a set of spreadsheets. Our developers have built retrieval over product documentation, commercial agreements, scanned records, tabular reports, standards libraries, and support histories. Each has characteristic failure modes we now anticipate rather than rediscover. Clients with substantial technical documentation often work with us across both retrieval and their broader manufacturing systems.
Deep heading hierarchies, cross references, and diagrams with meaningful captions. Structure-aware chunking works particularly well here, and preserving section context in metadata improves answers considerably.
Definitions referenced throughout, clause numbering that matters, and amendments modifying earlier terms. Retrieval must return the clause with its definitions attached, or answers will be confidently incomplete.
Optical character recognition quality sets the ceiling, and it varies within a single collection. Confidence scoring and flagging low-quality extractions prevents unreadable pages from being treated as empty ones.
Meaning lives in structure, and flattening a table into prose destroys it. Preserving row and column relationships, or routing these questions to a structured query engine instead, is the deciding design choice.
Versioned documents where the applicable edition depends on date and jurisdiction. Metadata filtering matters more than similarity here, since retrieving the superseded version is worse than retrieving nothing.
Short, informal, repetitive, and full of near-duplicates. Deduplication and recency weighting matter more than depth, and resolution outcomes are usually the valuable part rather than the discussion.
validation, alerting, idempotency, and monitoring. Parse failures raise alerts rather than disappearing. Every corpus has a stated freshness policy. Permission filtering happens at retrieval time rather than being assumed. Certified developers, meaningful time zone overlap, full intellectual property transfer including parsers and evaluation sets, and long-term support commitments apply as standard. Our tooling choices are documented on our technology stack page.
Small stable corpora sometimes fit entirely in context with no index at all. We recommend those routes when they apply, even where it shortens the engagement.
The entry point is a free retrieval audit. Send a representative document sample and twenty questions your users actually ask, and we return a written assessment of where retrieval fails and why, with fixes ranked by effort against benefit. Where parsing is the constraint, ingestion builds are available at a fixed price. Your engineers interview the matched developers, onboarding completes inside a week, and retainers remain optional.
A sample corpus, a set of real questions, and a few days of measurement. Several clients have implemented the recommendations with their own team, which is a legitimate outcome.
We report parse quality per format, structural consistency, metadata availability, and the realistic accuracy ceiling given the material. Prevents committing budget before knowing what the documents allow.
Where parsing and chunking are the constraint, we quote a fixed price against defined acceptance criteria including retrieval hit rate on your labelled question set.
Profiles arrive with relevant retrieval and document processing experience. Your team assesses them against your standards, declines at no cost, and matching continues until the fit is right.
Document access, vector store provisioning, repository access, and sprint planning handled immediately so measurable retrieval improvement appears inside the first fortnight.
Support starts light and grows with corpus size. Ingestion monitoring and parse failure investigation are the components clients take up first, since both prevent quiet degradation.
Our work and story have been picked up by news outlets and databases worldwide.
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Audits are free. Ingestion builds are quoted at a fixed price once the sample assessment defines scope, index redesigns are priced against a measured baseline, and embedded engineers are quoted monthly. Model and embedding consumption plus vector store hosting are billed to your own accounts, and we report expected monthly running cost before you commit.
An audit produces findings in one to two weeks. Parsing and ingestion rebuilds typically show measurable improvement in three to five weeks. Full application builds run six to ten weeks including evaluation infrastructure. Corpus size matters less than format consistency, which is usually the real constraint.
Most retrieval projects run with one senior engineer plus part-time subject expertise from your side for labelling. Large or badly inconsistent corpora add a data engineer for ingestion. Application builds add front-end capacity for citation display.
Retrieval metrics against your labelled question set after each iteration, alongside parse success rates and cost per query. Weekly sessions review the questions that still fail with your domain experts, which directs the next change more usefully than any status document.
In your own infrastructure and region. We deploy vector stores into your cloud account and never copy corpora to our systems. A mutual NDA precedes any document access, permission metadata is captured at ingestion, and labelled question sets and parser configuration belong to you contractually.
We staff for at least four hours of daily overlap with your business day across North American, UK, European, and Australian schedules. Evaluation reviews with your subject experts sit inside that window while ingestion runs and development continue outside it.
Retainers cover ingestion monitoring, parse failure investigation, freshness verification, periodic index rebuilds, and regression runs against your labelled set. Corpora degrade gradually rather than visibly, so most clients take at least a light arrangement once a system is in daily use.
Managed services are faster to launch and reasonable where documents are clean, requirements are standard, and you can accept their parsing and chunking decisions. Building your own becomes correct when documents are difficult, permission filtering must reflect your own entitlement model, retrieval accuracy needs measuring and tuning, or vendor pricing scales badly against your corpus. We assess the sample and say which applies rather than assuming a custom build is warranted.
Tell us what you’re building. Our team will get back to you within one business day with a clear, no-obligation plan.
