RAG DEVELOPMENT COMPANY
RAG systems that cite their sources and respect access
SmartX is a RAG development company that builds retrieval systems answering from your documents with citations. Answers respect each user's permissions, and every release is checked against an evaluation set built from real questions.
RAG Audit
- Eval set of real questions with expert-checked answers
- Failure analysis across ingestion, retrieval and generation
- Prioritised fix list with the expected quality gain for each
WHY IT MATTERS
Retrieval augmented generation services built on evidence
Most RAG demos work on ten clean PDFs and fail on ten thousand messy ones. We start with your real documents, measure retrieval quality before tuning prompts, and fix the weakest link first.
CAPABILITIES
How we work as your RAG development company
Document ingestion pipelines
Parsers for PDFs, scans, spreadsheets, emails and wikis that keep tables, headings and metadata intact, refreshed as source files change.
Hybrid retrieval and reranking
Keyword and semantic retrieval combined, then reranked, so exact terms like part numbers and clause references are found as reliably as concepts.
Permission-aware retrieval
Results are filtered by each user's existing access rights from SharePoint, Google Drive or your database before the model sees anything.
Citations and answer checks
Every answer links to the passage it came from. A grounding check blocks any claim the retrieved text does not support.
RAG evaluation suites
Question sets built with your experts that score retrieval recall, answer accuracy and citation quality on every change to the system.
Audits of existing systems
Already running RAG in production? We trace wrong or missing answers back to chunking, retrieval or prompts, then fix the cause.
USE CASES
Questions RAG answers well
Precedent search for law firms
Lawyers ask in plain language and get relevant clauses and past opinions from the firm's archive, each with a link to the source.
Policy answers for HR teams
Employees ask about leave, travel or expenses and get answers from the current policy, with the clause cited and old versions excluded.
Manuals for manufacturing maintenance
Maintenance engineers search equipment manuals and service logs on the shop floor, getting step-by-step answers that point to the right page.
Enterprise AI search across tools
One search box over Confluence, Jira, SharePoint and email, returning cited answers filtered by what each person is allowed to see.
HOW WE DELIVER
Five gates. You sign each one.
Every RAG Systems engagement runs on PRISM OS. Nothing moves to the next phase until you approve the evidence.
Plan
Agree the questions, users and accuracy bar that matter
You sign: Problem BriefResearch
Profile documents, formats, access rules and update frequency
You sign: Frozen PRD-LiteImplement
Build ingestion, retrieval and citations against an eval set
You sign: Working prototype + eval reportStabilise
Load-test, add grounding checks and lock down permissions
You sign: 3-click UAT sign-offMaintain
Re-run evals on new content and tune retrieval quarterly
You sign: IP handover + warrantyRAG Audit
A fixed-scope first step. You keep everything it produces, whether or not you build with us.
- Eval set of real questions with expert-checked answers
- Failure analysis across ingestion, retrieval and generation
- Prioritised fix list with the expected quality gain for each
WHY SMARTX
Why SmartX for RAG Systems
Retrieval measured first
We score what the system finds before judging what it writes. Most bad answers start with poor retrieval, so that is where we look first.
Permissions from day one
Access rules are designed into the index from the start. Nobody sees an answer built from a document they cannot open.
Messy documents welcome
Scans, nested tables, long email threads and legacy formats are normal inputs. We build parsers for them before we tune anything else.
Honest answers when unsure
When the sources do not support an answer, the system says so plainly and points to where a person can check.
TOOLS WE USE
The stack, chosen per problem
ENGAGEMENT MODELS
Choose how we work together
Every model keeps the same signed gates, fixed milestones and full code ownership.
Gated delivery pod
A senior pod owns the outcome from Problem Brief to production, priced per gate so you can stop at any signature.
Dedicated engineers
Engineers who join your team and sprints, with PRISM briefs and evidence reviews added on top of your process.
PRISM OPS retainer
Hosting, security and a monthly improvement budget, run by the same engineers who built the system.
SELECTED WORK
Evidence, not promises.
From idea to post-PMF growth for an EdTech startup
WeXL went through the Startup Factory stages, from idea validation and MVP to product-market fit and go-to-market.
An EdTech company, now in its post-PMF growth phase.
Read the case study“SmartX Startup Factory helped us get our ideas to market faster. Their team is very adaptive and understands lean startups. They tweaked the process to suit our business and helped in product delivery.”
FAQ
Questions before you book
Not covered here? Ask on the call.
What is RAG in plain terms?
RAG means the model looks up relevant passages from your documents before answering, then writes a reply based on them. Answers stay current and specific to your organisation, and every claim can be traced back to a source.
Which vector database do you recommend?
It depends on scale and what you already run. pgvector suits teams on PostgreSQL, Qdrant and Weaviate suit larger collections, and managed search suits teams already on Azure or AWS. We avoid adding infrastructure you do not need.
Can you fix a RAG system someone else built?
Yes. Our RAG Audit takes one to two weeks. We build an evaluation set from real questions, find where answers fail, and give you a ranked fix list. You can apply the fixes yourselves or with us.
How do you stop the model making things up?
We restrict answers to retrieved passages, require citations, and run a grounding check that flags unsupported claims. When retrieval finds nothing relevant, the system says it does not know instead of guessing an answer.
Does our data have to leave our environment?
No. We can deploy the full pipeline in your own cloud account, use models hosted in your region, or run open-weight models on your own servers when your policy requires it.
START WITH CLARITY
Test RAG on your own documents
Book a free discovery call, bring a handful of real questions, and we will tell you what it takes to answer them well.
- 1We review your context within one business day.
- 2A senior product engineer joins the call.
- 3You leave with a clear recommended next step.