Data stays in your environment
Nothing about your data leaves your environment to train someone else's model.
Custom RAG development
Retrieval-augmented systems that answer from your own documents, databases and wikis — with the source attached, so an answer can be checked rather than taken on trust.
Answers cite their sourceYour data stays in your environmentIndex updates with your files
Retrieval-augmented generation, built around your data
Ask a stock model about your product manual, your contracts or last quarter's support tickets and it will confidently guess. A custom RAG system replaces that guess with a lookup: before the model writes anything, it searches your own documents, databases and internal wikis for the passages that actually matter, then builds the response from what it found — with the source attached.
That is the core difference from an off-the-shelf AI chatbot. Nothing about your data leaves your environment to train someone else's model, the index updates the moment your source files do, and every claim in an answer can be traced back to where it came from.
Connecting and ingesting your sources, choosing the retrieval and vector database setup, fine-tuning the model where it earns its keep, and wiring the generation layer on top — that is exactly what our custom RAG development services cover, end to end.
Always up to date
The index updates the moment your source files do.
Every answer is traceable
Every claim in an answer can be traced back to where it came from.
Want to know how this would work for your data?
What we build
What a RAG engagement can produce
From a single retrieval-augmented assistant to a full knowledge platform — what our custom RAG development services cover in practice.
Custom RAG app development
An application built around your workflows and knowledge base, from architecture through to deployment — not a generic chatbot with your logo on it.
Multimodal RAG systems
Company knowledge rarely lives in plain text. We index and retrieve across documents, images, tables and slide decks, so answers can draw on whatever format the source happens to be.
Conversational agents & voice assistants
Chat and voice interfaces that answer from retrieved material rather than memory, aimed at cutting repetitive queries instead of imitating an FAQ page.
Automated insight & reporting pipelines
Reporting pulled from live sources into accurate, on-demand summaries, which removes the manual assembly work behind recurring reports.
Enterprise search & knowledge retrieval
Search across large, messy document archives in plain language — turning what used to be a manual lookup into a single question.
Domain fine-tuning for LLMs
A tuning layer on top of retrieval, matched to your terminology, tone and compliance requirements, so responses read as though your team wrote them.
Scope
What the engagement covers, end to end
Every engagement is scoped to your data, infrastructure and use case — from a proof of concept through to a pipeline running in production.
Sources and ingestion
PDFs, spreadsheets, SQL databases, wikis and cloud storage brought into one ingestion pipeline, with updates as the source data changes.
Vector database and embeddings
A chunking and embedding strategy tuned for retrieval accuracy, deployed on the vector store that fits your scale and latency budget.
Fine-tuning and model selection
Tuning of open or proprietary models where retrieval alone is not enough, plus prompt and context-window optimisation.
Permissions and security
Row- and document-level access control, so the system only ever surfaces what a given user is already entitled to see.
Integration with your systems
Deployed into your product, internal tools or support stack through an API rather than bolted on as a widget.
Monitoring and maintenance
Retrieval accuracy tracking, cost monitoring for hosting and tokens, and ongoing tuning after launch.
Benefits
Why grounding beats a stock model
Four ways a retrieval-grounded system holds up better than a model answering from memory alone.
Fewer fabrications
Answers are built from retrieved documents rather than recollection, which is where most of the confident-but-wrong output comes from.
Sources your team can check
Each answer can name the document behind it, so reviewers and compliance staff can verify instead of trusting.
Your data stays private
Proprietary material is used to answer questions, not to train a public model, and access stays scoped per user.
It stays current
The index follows your documentation, so the system reflects the current version rather than a training-time snapshot.
Outcomes
What teams tend to see
Typical early results after a rollout. [VERIFY these before publishing — they are indicative third-party figures carried over from the reference material, not audited client results.]
Faster research and document lookup for teams working across large archives.
Indicative rangeShorter audit and review preparation cycles.
Indicative rangeFewer repetitive support tickets reaching a human.
Indicative rangeUnsupported-answer rate once grounding and citation checks are in place.
Indicative rangeComparison
RAG, fine-tuning or a stock model
Three ways to make a language model work with your knowledge, and where each one holds up.
| Criteria | Custom RAG | Fine-tuning | Stock model |
|---|---|---|---|
| Grounded in your data | Retrieved live at query time | Baked in at training time | General knowledge only |
| Risk of fabrication | Low — answers cite retrieved sources | Reduced, but still generated from memory | Highest — nothing to ground against |
| Source citations | Yes, by default | No | No |
| Freshness | Updates as source documents change | Fixed until the next retraining run | Fixed at the training cutoff |
| Data privacy | Data stays in your infrastructure | Training data can surface in outputs | No control over training data |
| Setup cost | Moderate — no retraining needed | High — labelled data and retraining cycles | Low — works out of the box |
| Best for | Fast-changing, source-sensitive knowledge | Fixed tone, style or output format at scale | Simple, general-purpose questions |
Most production systems end up using retrieval, with a light tuning layer where tone or format matters.
Where it fits
Where retrieval makes the biggest difference
The same approach adapts to very different jobs, depending on what your data looks like and who is asking the questions.
Customer support and help desks
Agents and self-serve bots answer from help docs, past tickets and release notes, with a link back to the source article.
Internal knowledge search
Employees ask plain-language questions across wikis, chat history and internal docs instead of digging through folders.
Sales and RFP enablement
Reps get sourced answers pulled from product specs, pricing sheets and past proposals while responding to prospects.
Legal and compliance review
Teams query contracts, policies and filings and get answers that cite the exact clause or document.
Technical and developer docs
Engineers search API references, runbooks and architecture docs and get grounded answers rather than half-remembered ones.
Product and catalogue Q&A
Shoppers get accurate answers about specifications, stock and compatibility pulled from your live catalogue.
Process
From first data audit to production
How an engagement typically unfolds.
Discovery and data audit
We map your sources, formats and access requirements, and agree what "accurate" has to mean for your use case.
Architecture and proof of concept
We choose the retrieval and vector database architecture and validate it on a PoC before the full build.
Development and integration
We build the ingestion, retrieval and generation pipeline and integrate it into your product or internal tools, with QA on retrieval accuracy.
Launch and maintenance
We deploy to production and keep tuning retrieval quality, cost and freshness as the knowledge base grows.
Related
Other ways we can help
Start here
Tell us the problem. We'll tell you if it's worth solving.
Thirty minutes with an engineer, not a salesperson. You leave with a rough scope, a cost band and an honest read on feasibility.
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