Naive chunking
Documents split by character count, cutting tables and clauses in half so retrieved passages make no sense.
Answers you cantrace back.
AI assistants grounded in your own documents, with citations.
Retrieval-augmented applications that answer from your own material and cite where each answer came from, with the evaluation work that tells you whether they are right.
- Cited
- Every answer
- Evaluated
- Before launch
- Your data
- Not the open web
moriva.tech
Our approach
An evaluation-led approach, not a prompt and a demo
A RAG demo is easy: load some documents, wire up a model, ask a question it happens to answer well. The demo convinces everyone, and the problems only appear once real people ask real questions.
The hard part is retrieval quality, and the only way to know it works is to measure it. We build the ingestion and retrieval properly, then test against a question set drawn from your actual users before anything goes live.
Common problems
What we get called in
to fix.
The patterns we see most often when a business asks us to look at what they have.
Retrieval that misses
The answer is in the corpus, but the right passage never surfaces — so the model invents one.
No evaluation before launch
Nobody measured accuracy, so the first real test is a user discovering it is wrong.
No citations
Confident answers with nothing to check them against, which is worse than no answer at all.
What is included
End to end,
under one team.
From the first conversation through to the live build and the support after it.
Knowledge ingestion
Pipelines that take your documents and prepare them properly, respecting structure and tables.
Content-aware chunking
Splitting along real boundaries — sections, clauses, rows — rather than an arbitrary character count.
Vector store setup
Embedding choice and index configuration matched to your content and query patterns.
Retrieval and reranking
Hybrid search plus reranking so the passage that answers the question is the one that reaches the model.
Grounded generation
Answers constrained to retrieved material, with citations back to the source document.
Evaluation harness
A question set with expected answers, so accuracy is measured before launch and after each change.
Built for your stage
Right-sized for
where you are.
The same standards, scoped to what the business actually needs right now.
Internal knowledge search
Staff asking questions of policies, processes and manuals instead of hunting through folders.
Customer-facing assistant
Public answers grounded in your published material, with a clean handover to a person.
Document analysis
Extracting and comparing across a corpus — contracts, reports, records — with sources attached.
Our process
From discovery to
a product you can run.
The same eight stages behind every build on this page — scaled up or down to fit the project, so you always know what comes next.
Step 01
Discovery & outcome mapping
Clarify the business goal, the people who will use it and how success gets measured — before any interface or stack decisions.
Step 02
Information architecture & UX
Map the journeys, content structure and enquiry paths so the product is organised around how customers actually decide.
Step 03
Technical architecture
Settle data models, access rules, integrations and hosting, along with the non-functional requirements that shape them.
Step 04
Design system & UI
Build a coherent visual system and high-fidelity screens, reviewed with you before a line of production code is written.
Step 05
Engineering sprints
Ship working slices of the product with code review, a demo at the end of each sprint and continuous integration behind it.
Step 06
Integrations & QA
Wire up third-party systems, harden access and security, and test the critical flows against real data before launch.
Step 07
Launch & SEO hardening
Deploy to production, watch load times, finalise metadata and sitemaps, submit through Search Console and train your operators.
Step 08
Support & iteration
Monitor the live product, fix what surfaces in real use, and plan the next round of work against what the data shows.
Engineering standards
Performance, SEO and
security, built in.
Part of how the work is done, not upsells after launch.
Content-aware chunking
Structure preserved, so a retrieved passage is intelligible on its own.
Embedding selection
The model chosen against your content rather than whichever is fashionable.
Hybrid search
Keyword and semantic retrieval together, because either alone misses a class of question.
Reranking
A second pass that puts the genuinely relevant passage first.
Citations in the answer
Every claim traceable to a document, so a user can verify rather than trust.
Measured accuracy
A number you can point at, tracked as the corpus and the prompts change.
Industries
Sectors we have
shipped into.
Healthcare networks
Membership associations
Scientific & research
Professional services
Education
Pharmacy & distribution
FAQ
Questions people
ask first.
A chatbot answers from what the model already knows. A RAG application retrieves from your documents first and answers from those, with citations — which is what makes it usable for policy, product or clinical material.
Related services
Explore the capabilities behind it.
Every build on this page draws on the same set of services, kept inside one small team rather than spread across vendors.
Have a project in mind?Let's build something
Let's build something
worth maintaining.
From idea to impact — we're here to turn your vision into long-term value.
- Direct consultation
- Tailored solutions
- Long-term partnership