Knowledge Source Audit & RAG Scoping
We inventory your documents, wikis and databases, and decide what the model should retrieve, ignore or never expose.
- Source inventory
- RAG scope
AI, Machine Learning & Intelligent Automation
LLM Fine-Tuning & RAG Development
We build retrieval pipelines with vector search, chunking and re-ranking, and fine-tune models on domain data, so answers cite your sources and hallucinations are measured and reduced.
- Vector search
- Retrieval pipelines
- Domain fine-tuning
- Answer evaluation
- Years of delivery
- 8+
- Years of delivery
- Projects shipped
- 350+
- Projects shipped
- Clients across US, UK, AU & SG
- 200+
- Clients across US, UK, AU & SG
What’s included
Everything You Need to Ground LLMs in Your Own Knowledge
We build retrieval pipelines and fine-tuned models so language models answer from your data, with accuracy you can measure.
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Document Ingestion & Chunking Pipeline
We parse PDFs, web pages and records into clean, well-sized chunks with metadata, so retrieval finds the right passage.
- Ingestion pipeline
- Chunking strategy
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Vector Database & Embedding Setup
We configure Pinecone, pgvector or Weaviate with the right embedding model for fast, relevant semantic search at your scale.
- Vector index
- Embedding benchmark
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Hybrid Retrieval & Reranking Tuning
We combine keyword and semantic search with reranking so the model sees better evidence and gives fewer wrong answers.
- Retrieval pipeline
- Reranker config
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Domain Fine-Tuning With LoRA & PEFT
We fine-tune open models on your terminology, tone and task formats when prompting alone cannot reach the quality you need.
- Fine-tuned model
- Training dataset
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Hallucination & Answer Quality Evaluation
We score faithfulness and relevance on your test questions, so you can see how often answers are grounded and correct.
- Evaluation suite
- Quality scorecard
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Secure Deployment With Access Controls
We deploy on your cloud or private infrastructure with document-level permissions, so users only retrieve what they may see.
- Private deployment
- Permission model
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Index Refresh & Model Update Support
We keep knowledge current with scheduled re-indexing, then retune or retrain as your content and models evolve.
- Re-index jobs
- Version roadmap
Need something specific?
Why Kramate
Why Business Owners Choose Kramate for AI That Knows Your Business
We make the AI answer from your documents and show you the proof.
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Every Answer Points to Its Source
Instead of
answers you cannot double-check
With Kramate
Each reply shows the page it came from, so your staff can verify it quickly.
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A Report Card You Can Read
Instead of: no way to know if it is right
With Kramate: We run your own test questions and show you how often it answers correctly.
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People See Only What They May See
Instead of: private files showing up for the wrong person
With Kramate: The AI follows the same access rules your staff already have for each document.
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We Try the Simple Fix First
Instead of: costly retraining when it is not needed
With Kramate: We start with the cheaper way and only retrain the AI if that falls short.
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It Stays Up to Date
Instead of: answers based on last year's documents
With Kramate: When your documents change, we refresh what the AI knows about them.
Our approach
How We Build Your LLM Fine-Tuning and RAG Solution
We make a language model answer from your own knowledge, and measure how often it gets it right.
7 clear steps 7 deliverables you keep
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Knowledge Source Review
We list your documents, wikis and databases, and decide what the model may read, what to skip and what stays private.
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Document Preparation
We clean your files and split them into small passages, so the right piece can be found for each question.
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RAG Prototype
We build a search-and-answer prototype that looks up your content first, then writes a reply with its sources.
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Answer Quality Testing
We score each reply for accuracy and for whether it stuck to your documents, using your own test questions.
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Fine-Tuning Decision
If better searching is not enough, we train the model on your wording and formats, and compare the gain.
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Secure Launch
We set it up on your cloud or private systems, so each person only sees what they are allowed to.
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Content Refresh
We refresh the knowledge on a schedule and retest answers when your content or the model changes.
Tools & technologies
Technologies We Use for LLM Fine-Tuning and RAG
We ground models in your documents and fine-tune only when retrieval alone is not enough.
- Mistral AI: Efficient open and hosted language models for cost-aware builds.
- Hugging Face: Hub of open models and libraries for language and vision work.
- Claude: Anthropic language models for careful reasoning and long documents.
- Google Gemini: Google models that handle text, images and long context.
- PyTorch: Framework for building and training neural networks.
- Weights & Biases: Tracks training runs and compares model results.
- MLflow: Tracks experiments and manages model versions.
- NVIDIA: GPU computing for faster model training and inference.
- LlamaIndex: Indexes your documents so language models can answer from them.
- Qdrant: Vector database for fast similarity search.
- Weaviate: Open vector database for search over meaning, not keywords.
- pgvector: Adds vector search to PostgreSQL without a new database.
- Elasticsearch: Search engine for text, filters and hybrid retrieval.
- Python: Readable language for business logic, data and automation.
- Apache Airflow: Schedules and monitors data and model pipelines.
- Docker: Packages software so it runs the same everywhere.
- FastAPI: Python framework for fast, well-documented APIs.
Expert guide
LLM Fine-Tuning and RAG Development: A Complete Guide for Businesses
LLM fine-tuning and RAG development are the two main ways to make a general language model useful on your own knowledge. Retrieval-augmented generation, or RAG, looks up relevant passages from your documents at question time and asks the model to answer from them. Fine-tuning adjusts the model itself with examples so it adopts a style, format or expert behavior.
They solve different problems, and many teams reach for the wrong one first. Most business needs about current, citable company information are better served by retrieval, while fine-tuning is for consistent behavior.
This guide walks through how to choose, how a retrieval pipeline is built and tested, how to keep documents secure, regional privacy points, and how to choose a partner.
RAG or fine-tuning: a decision ladder
Work up the ladder only as far as the problem requires. Each step adds cost and maintenance.
| Approach | What it changes | Choose it when | Limits |
|---|---|---|---|
| Better prompts | The instructions given to the model | The model already knows enough and needs clearer direction | Cannot add private or recent knowledge |
| RAG | The information supplied at question time | Answers must come from your documents and be traceable and current | Quality depends on search and content quality |
| Fine-tuning | The model’s behavior, tone or format | You need consistent style, structured output or expert task skill | Poor at keeping facts fresh; needs good example data |
| Both | Knowledge and behavior | Cited answers in a fixed house style | Most to build, test and maintain |
Fine-tuning does not reliably teach a model new facts, and facts change. Updating a document in a retrieval index takes minutes, whereas retraining takes planning. Provider guides, such as the OpenAI documentation (opens in a new tab) and Hugging Face documentation (opens in a new tab), describe both approaches and their trade-offs.
What LLM fine-tuning and RAG development involves
A project typically begins with a use case and a set of real questions, then moves through content preparation, retrieval design, prompt design, an evaluation harness, a pilot with users and ongoing operation. In LLM fine-tuning and RAG development the language model is only one component. The content, search and checks around it decide whether answers are useful.
Typical uses of generative AI with company knowledge include internal knowledge search, support answer drafting, policy and contract question answering, and technical documentation assistants. These often sit behind a chat front end, see AI chatbot development services.
How a RAG pipeline works, stage by stage
- Ingest. Connect sources such as wikis, PDFs, tickets and drives, and keep them in sync. Remove duplicates and outdated versions, because retrieval will surface them.
- Chunk. Split documents into passages that keep their meaning, with headings and source details attached as metadata.
- Embed and index. Convert passages into numeric vectors that capture meaning and store them in a vector database or search engine, often alongside keyword search.
- Retrieve. For each question, find the best passages, and often re-rank them, applying filters such as the user’s permissions.
- Generate. Give the model the question plus the passages and instruct it to answer only from them and cite the sources.
- Check and respond. Verify citations, refuse when evidence is missing, and log the exchange.
Frameworks such as the one described in the LangChain documentation (opens in a new tab) provide common components for these stages, but the design choices on chunking, filters and fallbacks are what shape quality.
When and how fine-tuning is done
Fine-tuning trains a model further on examples of the inputs and outputs you want. It suits tasks like classifying support tickets into your categories (a staple of NLP development), producing a fixed JSON format, matching a brand voice, or improving a smaller, cheaper model on a narrow job.
- Example data. Hundreds to thousands of high-quality, reviewed input-and-output pairs, written or approved by subject experts.
- Method. Full tuning or lighter parameter-efficient methods, with open-weight models such as Meta’s Llama family (opens in a new tab) or hosted tuning services where the provider offers them.
- Baseline first. Compare against a well-prompted base model, which is often good enough.
- Version everything. Keep data, settings and results so a tuned model can be reproduced, see MLOps services.
Evaluating retrieval and generation separately
When an answer is wrong, you need to know whether the system found the wrong passage or misused the right one. Test the two halves apart.
- Retrieval. For each test question, mark the passages that contain the answer and check whether they are retrieved.
- Generation. Check that answers are faithful to the passages, complete and properly cited, and that the model declines when the passages do not answer the question.
- Test set. Use real questions written with subject experts, including ambiguous, out-of-scope and adversarial ones.
- Review. Use automated model-based grading to scale, but calibrate it against human judgments on a sample.
- Regression. Re-run the set after any change to content, chunking, prompts or models.
The NIST AI Risk Management Framework (opens in a new tab) frames this as measuring and managing risk across the lifecycle. Do not accept a vendor’s promised accuracy figure without a test on your own documents.
Security, permissions and data privacy
A retrieval system can expose in one chat window everything its index contains. Access control is the central design question.
- Respect existing permissions. Filter retrieval by the asking user’s rights, so a junior employee cannot read board papers through the assistant.
- Guard against injected instructions. Text inside a retrieved document can try to hijack the model. The OWASP Top 10 for LLM applications (opens in a new tab) covers prompt injection, sensitive information disclosure and related risks.
- Control data flow. Know which provider sees prompts and documents, and what the contract says about retention and training use.
- Handle personal data deliberately. Minimize it in the index and in tuning data, and plan how deletion requests reach the index.
- Keep a person in the loop for high-stakes answers in legal, medical or financial contexts.
The ICO guidance on AI and data protection (opens in a new tab) discusses lawful use of personal data in AI systems and is a sound reference beyond the UK.
Industry use cases
Illustrative scenarios, not Kramate results.
- Professional services. A firm searches past proposals and policies with cited answers, restricted by client confidentiality.
- Software and IT. Support engineers query product documentation and incident notes, with a tuned model producing tickets in a fixed format.
- Insurance and banking. Staff ask questions about internal procedures and product terms, with answers drawn only from the current approved versions.
- Manufacturing. Technicians search maintenance manuals by symptom, and the system links to the exact page.
Regional considerations: USA, UK, Australia and Singapore
Where documents and prompts are processed matters. Ask providers about hosting region and data retention, and consider private deployments for sensitive material.
- USA. Sector and state rules apply, including HIPAA for health data and state privacy laws. Contract terms on provider data use are important.
- UK. UK GDPR applies to personal data in documents and prompts, and international transfers need a valid mechanism.
- Australia. The Privacy Act 1988 and the Australian Privacy Principles cover cross-border disclosure of personal information.
- Singapore. The Personal Data Protection Act applies, alongside sector guidance such as for financial institutions.
The EU AI Act (opens in a new tab) may also matter for systems used in the EU. Confirm your obligations with legal counsel.
Cost factors for LLM fine-tuning and RAG development
- Quantity, quality and variety of source documents, and the cleanup they need.
- Number of connectors and how permissions are mapped.
- Hosting: managed model APIs, private cloud or self-hosted open models.
- Usage fees that grow with question volume and passage length.
- Evaluation work, including expert time to write test sets.
- Fine-tuning data creation, training runs and re-training later.
Budget in phases: a content audit and test set, a retrieval prototype, a pilot, then production hardening. Consider fine-tuning only after a retrieval baseline shows a gap. Fixed-price suits the prototype, a dedicated team suits scaling, and hourly work suits tuning experiments.
Choosing a partner for LLM fine-tuning and RAG development
- Starts from a test set of your questions before choosing tools.
- Can explain why retrieval, tuning or both fits your case.
- Designs for permissions from day one.
- Evaluates retrieval and generation separately and shares the results.
- Works across several models, avoiding lock-in.
- Delivers pipelines, prompts, tuning data and documentation to you.
How Kramate delivers RAG and fine-tuning projects
A dedicated project manager keeps the work moving, with daily updates on Slack or Teams and a progress review at the end of every two-week round, where you can try the system on your own questions. We sign an NDA before detailed discussions, apply least-privilege access to your content, and transfer all intellectual property to you. Read how our approach works.
Frequently asked questions
RAG vs fine-tuning: which is better?
Neither in general. RAG suits answers that must come from current, citable documents. Fine-tuning suits consistent style, format or a narrow task skill. Many projects start with retrieval and add tuning only if a measured gap remains.
When should you fine-tune an LLM?
When prompting and retrieval cannot deliver consistent behavior, such as a strict output format, a house tone or a expert classification, and you have good reviewed examples. Compare against a well-prompted base model first.
How do you evaluate a RAG system?
Test retrieval and generation separately. Check that the right passages are found, then that answers are faithful, complete and cited. Use a test set built with your experts, calibrate automated grading against human review, and re-run after every change.
How do you reduce hallucinations in RAG?
Instruct the model to answer only from retrieved passages, require citations, refuse when evidence is missing, improve content and chunking, and review samples. Risk falls but does not reach zero, so keep humans involved for high-stakes answers.
How do you keep private documents secure in RAG?
Filter retrieval by each user’s existing permissions, minimize sensitive data in the index, confirm provider retention terms, defend against prompt injection, and log queries for audit. Consider private deployment for the most sensitive material.
How much data is needed to fine-tune a model?
It depends on the task. Narrow formatting or classification jobs may need far fewer examples than broad behavior changes. Quality matters more than quantity, so start small, evaluate, and add examples where errors cluster.
Sources and further reading
- OpenAI API documentation (opens in a new tab)OpenAI
- Hugging Face Documentation (opens in a new tab)Hugging Face
- LangChain documentation (opens in a new tab)LangChain
- Llama (opens in a new tab)Meta
- AI Risk Management Framework (opens in a new tab)NIST
- OWASP Top 10 for Large Language Model Applications (opens in a new tab)OWASP
- Guidance on AI and data protection (opens in a new tab)ICO UK
- EU AI Act (opens in a new tab)Future of Life Institute
FAQ
Frequently asked questions
Factors include document volume and condition, number of data sources, embedding and vector store choices, whether fine-tuning is needed at all, GPU time, evaluation effort, access controls and hosting. RAG alone usually needs less training spend than fine-tuning. We estimate within 24 hours.
We typically ingest and clean documents, design chunking and retrieval, connect an LLM, build an evaluation set, then tune and release. Length depends on document quality, the number of formats, permission rules per user, and how much tuning the answers need.
You do. Kramate signs an NDA before detailed discussions and transfers the retrieval pipeline, indexing code, fine-tuning scripts, adapter weights and evaluation sets to you. Your documents are not used to train models for anyone else. Base model licences are noted in the handover.
We build connectors to sources such as file stores, wikis, ticket systems and databases, and keep the index in sync as content changes. Your existing permissions can carry over, so people only get answers from content they are allowed to see.
Use RAG when answers must come from current documents and cite sources. Use fine-tuning to change style, format or domain behaviour. Many teams start with RAG because updating a document is easier than retraining. Some projects combine them. We test both on your questions.
We build a test set of real questions with expected answers, then score retrieval relevance, answer correctness, source citation and refusal behaviour when information is missing. We rerun the set after every change so improvements and regressions are visible.
Yes, where needed. Options include deploying open models in your cloud account, using providers with strict data terms, masking sensitive fields and enforcing access controls. We map your compliance needs early and choose the setup that fits them.
Taking new projects
Let’s talk about your LLM Fine-Tuning & RAG Development needs
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- We reply within 4 business hours A real consultant reads your brief — no bot, no queue.
- 30-minute discovery call We dig into goals, constraints and budget. NDA signed on request.
- Proposal in 24 hours Scope, timeline, team and price — clear enough to decide on.
NDA on request You own the IP No obligation Free estimate