RAG Development Services That Measure Retrieval Quality
Almost every grounded system that disappoints is failing at retrieval rather than at generation: the right passage was never fetched, so the model answered from somewhere else. Our RAG development services measure that step directly, because it is invisible in the output.
Book a Retrieval Diagnostic
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4.8Where a Grounded System Actually Fails
The complaint is always about the answer. The fault is almost always one step earlier, and looking in the wrong place is why these systems get rebuilt rather than fixed. We run retrieval systems in production with human review and feedback loops behind them. A formal groundedness evaluation harness delivered as a standalone piece of client work is not something we have shipped before, so it is scoped and priced as new work rather than presented as proven elsewhere. Any supplier claiming otherwise is worth asking which client it was built for and what it measured.
A grounded system does two things: it fetches passages it believes are relevant, then it writes an answer using them. When the output is wrong, teams inspect the writing. But if the fetch returned the wrong passages, or returned nothing useful, the model had no material to work from and produced something plausible instead. The generation behaved correctly on bad input.
Measuring the fetch separately is the whole discipline. Did the correct passage appear at all, and where did it rank? That question is answerable, it is cheap to answer once a question set exists, and almost nobody asks it. Teams tune prompts for weeks against a retrieval problem that no prompt can reach.
If the fetch is fine and the writing is not · If the source material itself is the problem
Our RAG Engineering Team, in Numbers
The team, delivery record and retention behind the grounded systems we build.
60+
AI Engineers
50+
AI Solutions Delivered
80+
AI-Integrated Workflows
30+
Industries Served
95%
Client Retention
Our RAG Development Services, and What Each One Fixes
Each service below can be engaged on its own. The diagnostic decides whether any of the rest are needed, and a large share of enquiries here turn out to need only that and the source work.
RAG Consulting and Retrieval Diagnostics
We build a fixed question set with the passage that should answer each one, then measure whether it was fetched and where it ranked. It ends the misdiagnosis: you learn whether you have a retrieval problem, a source problem or a generation problem, and those have different owners and different prices.
Knowledge Base Preparation and Source Assessment
We read what the system is grounded in, including currency, contradictions, duplicate versions and documents never written to be answered from, and tell you what has to change. This is what sets your ceiling, because no retrieval design compensates for three versions of a policy with nothing marking which is current.
Chunking and Vector Index Design
We decide how your documents are split, what each chunk carries with it, and what gets indexed alongside the text. It is the commonest single cause of poor retrieval, cheapest to correct early and most expensive to correct late.
Hybrid Search and Re-ranking Implementation
We combine semantic search with keyword matching and filters, then re-rank, because each approach fails on cases the other handles. This is what solves the exact-term problem: pure semantic search reliably misses part numbers, policy codes and names, which are often what people search for.
Permission-Aware Retrieval and Metadata Filtering
We make retrieval respect who is asking, which version applies and what date range is relevant, enforced before ranking rather than after. Without it there is a disclosure risk, because a grounded system that ignores permissions will quote a document to somebody who should not see it.
Groundedness Evaluation and Citation Engineering
We make every answer traceable to the passages used, and measure how often it is genuinely supported by them. It settles the confidence problem, and a citation is also the fastest way for your own team to spot that a source document is out of date.
RAG Maintenance, Freshness and Re-indexing
We keep the index current as documents change and detect the ones that changed without telling anybody. This prevents silent decay: a grounded system pointing at stale material degrades invisibly, and is worse than one that admits it does not know.
RAG Projects We Have Delivered, and What the Measurement Showed
On these cards the diagnosis is more interesting than the fix. Several began as generation complaints and turned out to be source problems that no amount of engineering could have solved.
Marketplace
Building the AI Layer Behind a Live Marketplace Without Touching Checkout
The marketplace was already live and taking payments, which ruled out rebuilding it. The AI layer runs as a separate FastAPI service, so models can change without redeploying the code that handles checkout.
Read case study: Building the AI Layer Behind a Live Marketplace Without Touching CheckoutMarketplace
An AI Freelance Marketplace Platform That Turns Site Audits Into One-Click Hires
Nine years with one client, and the most recent phase changed what the product is. Legiit went from listing gigs to telling a business what is wrong with its website and which seller can fix it.
Read case study: An AI Freelance Marketplace Platform That Turns Site Audits Into One-Click HiresReviews From the Teams We Build For
The teams we build for describe the work in their own words, on platforms that verify the engagement before a review is published.
Our experience working with Aipxperts has been exceptionally satisfying. From start to finish, they handled the project with professionalism and responsibility. Communication was seamless, and they effectively addressed our requirements, delivering high-quality results on time. Their technical expertise was particularly impressive, as they effortlessly solved complex problems. We highly recommend Aipxperts for their outstanding service and dedication to client satisfaction.
Hardik was very helpful in advice and completing the work.
RAG Development by Industry, and What Your Documents Do to It
Each entry names the RAG work we most often deliver in that industry and what its source material does to retrieval, because the documents set the ceiling long before the engineering does.
Healthcare administration
Policies, consent rules and procedures that differ by site and by date, with permissions that must be enforced at retrieval rather than in the answer. In healthcare administration, filtering matters more than ranking does.
Fintech and financial services
Product terms that change by date and by jurisdiction, where quoting a superseded version is a compliance event rather than an inconvenience. Version metadata is the whole design in fintech and financial services.
Manufacturing
Long technical manuals with revision histories, where two revisions of the same procedure look nearly identical to a semantic index. Blending them is the characteristic manufacturing failure.
Energy and utilities
Standards, safety documentation and asset records that reference each other constantly. For energy and utilities, retrieval has to follow those references rather than treat each document as self-contained.
Food delivery and hospitality
Menus, allergen information and partner terms across thousands of outlets, each maintained by somebody else and changed without notice. Across food delivery and hospitality, freshness and per-outlet filtering matter more than ranking sophistication.
Logistics and warehousing
Carrier terms, tariffs and service definitions that arrive from third parties in inconsistent formats and change without notice. In logistics and warehousing, freshness detection matters more than retrieval sophistication.
Telecom
Plan and tariff documentation at a scale where the correct answer depends on which of several hundred variants a customer holds. Filtering by entitlement before ranking is non-negotiable on a telecom estate.
Why Choose Aipxperts for RAG Development
Every RAG development company promises grounded answers. These are the specific things we do differently, and one of them regularly ends an engagement before it starts.
01You get two numbers, not one quality scoreTwo distinct numbers: whether the right passage was retrieved, and whether the answer was supported by what came back. Systems reported as a single quality score conceal which half is broken, which is why teams tune the wrong one.
02We check your documents before we design anythingContradictions, superseded versions and documents nobody maintains set a ceiling no engineering can lift. Finding that ceiling early is cheaper than discovering it after a build.
03Your search finds part numbers, not just paraphraseSemantic search alone misses exact terms, and keyword search alone misses paraphrase. Almost every real question set contains both kinds, so combining them and re-ranking is the starting position rather than an optimisation.
04Permissions are enforced in code, never in a promptA system that fetches a restricted document and is instructed not to mention it has already read it into a context somebody may see. The filter belongs before the fetch and it is implemented in code.
05We tell you when your documents cannot answer the questionsSometimes the honest answer is that the documents do not contain the answers people are asking for, and the useful project is writing them rather than indexing what exists. That recommendation costs us the build and is the correct one often enough to be worth stating.
Why Chunking Decides More Than Model Choice Does
The single most consequential decision in a grounded system is how documents get split, and it is routinely made by accepting a default. What follows is the short version of why that hurts.
A chunk is the unit of truth the system can retrieveIf an answer spans two chunks, retrieval returns half of it and the model completes the other half from somewhere else. If a chunk is too large, the relevant sentence gets diluted and ranks below something less useful. Both failures look identical in the output: a confident, partially wrong answer.Fixed-size splitting fights the documentSplitting every few hundred characters cuts through tables, separates a heading from what it governs, and detaches a condition from the rule it modifies. Splitting on the document’s own structure, and carrying the heading path into each chunk, resolves a surprising share of retrieval complaints on its own.What travels with the chunk matters as much as the chunkSource document, version, effective date, owning team, applicable region, permission scope. Without those, retrieval cannot filter and the system will confidently return a superseded policy that is textually a perfect match.Tables, lists and forms need separate treatmentA table split across chunks becomes meaningless, and a table flattened into prose becomes wrong. Documents containing them need handling designed for them, and their presence is one of the first things an assessment looks for.The way to settle it is to measure rather than to argueTwo chunking strategies, the same question set, and the ranking position of the correct passage under each. That comparison takes days and removes a debate teams otherwise carry for months.And the uncomfortable finding this often producesWhen the correct passage cannot be retrieved under any strategy, the problem is that the answer is not clearly stated anywhere in the source. That is a documentation problem, and no retrieval architecture repairs it.
The RAG Tech Stack We Build On
The embedding, vector store, keyword search, re-ranking and evaluation tools our engineers build RAG systems on. Where you already run a search platform, we build on top of yours rather than introducing a second index to keep in sync.
Data ingestion and preparation
Apache Airflow
Apache KafkaUnstructuredTesseract OCR
Python
Vector databases
PineconeWeaviate
Qdrant
MilvuspgvectorChromaFAISS
OpenSearch
Embedding and language models
Hugging Face Transformerssentence-transformers
spaCy
Retrieval orchestration
LangChainLlamaIndex
HaystackDSPy
LangGraph
Model serving
vLLM
Ray Serve
NVIDIA Triton
Amazon Bedrock
Google Vertex AI
Azure OpenAI Service
Evaluation and monitoring
RagasLangSmithArize Phoenix
OpenTelemetry
Prometheus
Grafana
Application layer
Python
FastAPI
Node.js
Django
Go
React
Next.js
TypeScript
Cloud and access control
AWSMicrosoft AzureGoogle Cloud
Kubernetes
Docker
TerraformOAuth 2.0Microsoft Entra ID
HashiCorp Vault
Verified Client Ratings
Independently verified on Clutch, GoodFirms, Upwork and Google, across more than a decade of web, mobile and enterprise delivery.
What Gets Indexed, and Who Can Retrieve It
Retrieval work reads more of an organisation’s internal documentation than almost any other engagement, which makes the boundaries below worth agreeing before indexing begins.
Where the index livesIn your environment, in your account, under your billing. An index of your internal documentation is a concentration of your organisation’s knowledge and it does not belong in a supplier’s infrastructure.Permissions travel with the contentAccess rules are captured at indexing and enforced at retrieval, so a query cannot reach material the asker is not entitled to. This is designed in from the first build, because retrofitting it means reindexing everything.What goes to a model providerThe retrieved passages and the question, and nothing beyond what the answer requires. Which fields are stripped is decided at design stage and recorded.Training, and the setting that gets checkedNothing sent to a provider becomes training data for a general model. Where a provider permits that by default, the configuration is disabled and the evidence handed over.Certification, and the evidence that matters more hereAipxperts holds neither ISO 27001 nor SOC 2 and no provider partner status. On retrieval work the inspectable evidence is the permission filter, the index location and the groundedness measurements, all of which sit in your environment.
Our RAG Development Process, and What Each Stage Rules Out
Each stage eliminates a possible cause. By the time indexing is rebuilt the actual problem is usually identified, and it is frequently not the one the client arrived with.
01Question set constructionTwenty to a hundred real questions, with the passage that should answer each one identified by somebody who knows the material. Nothing is ruled out yet, but this is the artefact everything else depends on.02Retrieval measurement against the current systemWhether the correct passage is fetched at all, and at what rank. If the fetch is already failing, generation is ruled out as the cause, and that single measurement redirects a large share of engagements.03Source assessmentCurrency, contradictions, superseded versions and whether the answers are actually stated anywhere. Where the material does not contain what people are asking for, no amount of engineering will help.04Chunking and indexing rebuildStructure-aware splitting, heading context carried into chunks, metadata attached and permissions captured. This addresses the commonest single cause, and often closes most of the gap on its own.05Hybrid retrieval and re-rankingSemantic and keyword combined, filters applied before ranking, re-ranking on top. It reaches the exact-term failures that semantic search cannot, whatever the embedding.06Groundedness and citation workAnswers traceable to passages, and measured for whether the passages actually support them. This eliminates the plausible-but-unsupported answer, the failure users notice last and trust least.07Freshness, monitoring and handoverRe-indexing on change, detection of documents that changed silently, and the question set left with your team. It prevents the silent decay that stops grounded systems being trusted after a good launch.
What Teams Ask Once a Grounded System Is Already Disappointing Them
These assume you already know what retrieval augmented generation is. If you are still deciding whether you need one, our AI development services page is the better starting point.
Share your project vision
Tell us what you want to build. A specialist, not a salesperson, replies.
Start Your RAG Development With Twenty Real Questions
Twenty real questions, with the document or passage that ought to answer each one. That is a diagnostic set, and it is enough to establish whether you have a retrieval problem, a source problem or a generation problem, which is three different projects with three different prices.
Send Your Question SetArticles on RAG and Retrieval
Recent writing from our engineers on RAG development: chunking, evaluation and the retrieval decisions behind grounded systems.
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