RAG Development Services
Stop letting your LLM guess. Ground it in your own data.
Your chatbot doesn’t need to be smarter. It needs the right documents at the right time. PSSPL runs the full build as your RAG development services company: data ingestion, embeddings, vector search, orchestration, and the guardrails that stop the model from filling gaps with fiction. We build retrieval-augmented generation systems for enterprises, connecting LLMs to your internal docs, databases, and knowledge base so answers come from what your company actually knows, with a source attached to every response.
What is (Retrieval-Augmented Generation) RAG?
Retrieval augmented generation โ RAG involves the extraction of data that is pertinent to the questions asked through your system, and presenting the data to the LLM for answering. The model is not relying on whatever it learnt in the training process, but rather using your guides, tickets, and contracts as they exist in your system. Every answer can be traced to its origin.
GPT ยท Claude ยท LLaMA
Model-agnostic builds
6+
Vector databases supported
500+
Projects delivered by our team to date
Self-host
Your data stays private
What we build
Our RAG Development Services
We scope each build around the problem you actually have, not a fixed package.
We call ourselves a custom RAG AI services provider because that’s what this actually is โ every build gets shaped around your data and the systems you already run, not pulled off a shelf.
Under the hood
How Our RAG Pipeline Works?
Ingest
Upload and chunk your documents, data, and APIs.
Embedding & Indexing
Turn content into embeddings and index it in a vector database.
Retrieve
Retrieve the chunks that are relevant to the query.
Generate
The large language model generates the response based on the retrieval.
Cite & Safeguard
Cite the sources of the response before sending it to the end-user.
Where RAG Delivers Real Value - RAG Use Cases
Customer Support
Bot answering questions based on your documentation and policies, providing sources. Fewer duplicate support tickets.
Internal Knowledge Search
Employees can search all wikis, SOPs, and legacy projects in one place and get an actual answer back.
Documentation/Contract Analysis
Large volume of documents can be queried, clauses summarized and flagged as risk along with references which can be verified by legal team.
Compliance & Research
Answers are based on current regulations and internal policies. Comes handy when you need "trust me" answers.
Achievements and Industry Accolades
RAG vs Fine-Tuning
Choose the right approach
Itโs common for most production processes to utilize both approaches. We will let you know which combination is ideal without pushing you towards the combination that will offer us more engagement.
| RAG | Fine-Tuning | |
|---|---|---|
| Best for | Private, changing, citable knowledge | Changing style, tone, or task behavior |
| Updating knowledge | Instant โ just update the data | Requires retraining |
| Hallucination risk | Lower | Higher on facts |
| Upfront cost | Lower | Higher |
How we work - Our RAG Delivery Process
Every data environment is unique; therefore, every build must be, too. Below is how we deliver a custom RAG AI services engagement from initial call to going live.
Discovery and Data Audit
Understand where your knowledge comes from and what success will look like.
Pipeline Design
Architecture based on your data schema, size and privacy requirements - not some generic design.
Build & Integrate
Develop it and integrate it into your existing infrastructure.
Deployment and Monitoring
Deploy it and monitor it since retrieval quality is bound to degrade when left unmonitored.
Evaluate & Tune
Evaluate for accuracy, latency and validity. Tune it to do that.
Frequently Asked Questions
It fetches the relevant data from your own data and passes it to the LLM right away once a query is made. You receive the answer based on your content and the answer is updated according to any changes in your content, and the source is easily identifiable.
Your knowledge is changing often, and your knowledge needs to be kept confidential or cited โ RAG is the path ahead. Fine-tuning is used when you require adjustment of behavior or style of the model, not the knowledge base.
Absolutely. Since the model is fetching the answer from the content and not recalling them from its memory there is absolutely no room left for inventions and you can verify the source.
No, and we'd push back if a vendor tells you that's all you need. Pure vector search misses more than people expect โ SKUs, clause numbers, error codes, anything that needs an exact match rather than a semantic one. By default, we rely on hybrid retrieval where dense embedding search is used together with BM25 keyword search and then a reranking of their combination. It costs a bit more at query time, but it catches queries that pure vector search quietly gets wrong.