What RAG is and why it beats fine-tuning for most enterprises
Retrieval-Augmented Generation combines a large language model with a search index over your private knowledge. When a user asks a question, the system retrieves the most relevant passages and passes them to the model as context, so answers stay grounded in your data, cite their sources, and update the moment your documents change. For the vast majority of enterprise use cases — support agents, internal knowledge bots, policy Q&A, regulated-industry copilots — RAG is faster to build, cheaper to run, and easier to audit than fine-tuning.
Your data, your infrastructure, your control
We deploy RAG pipelines on your cloud, on-premise, or as a managed service — with strict controls over which model reads what. Sensitive documents can stay in your own VPC; open-source models (Llama, Mistral, Qwen) can run entirely within your perimeter. That means GDPR-safe deployment for EU teams, HIPAA-appropriate architecture for healthcare, and full sovereignty over vector stores and query logs.
Engineered for accuracy, evaluated in production
RAG that hallucinates is worse than no RAG. We build evaluation harnesses, retrieval-quality metrics, and safety filters into every pipeline; monitor answer quality post-launch; and iterate on chunking, embedding, and reranking until answers meet your accuracy bar. Every response cites the source passages a reviewer can click through and verify.
RAG development services we offer
📚Enterprise knowledge chatbots
Internal Q&A assistants over wikis, docs, and databases — grounded, cite-source answers with role-scoped access.
🧠Customer support RAG agents
AI agents for public support that read your knowledge base, product docs, and policies — deflect tickets while escalating cleanly.
🔍Semantic + hybrid search
Vector search combined with keyword and metadata filters — pgvector, Pinecone, Weaviate, Qdrant, Azure AI Search, or on-prem alternatives.
📄Document intelligence pipelines
PDF, DOCX, PPTX, and scanned-image ingestion with layout-aware parsing, table extraction, and OCR for regulated-industry sources.
🛡️GDPR / HIPAA-safe RAG
EU-hosted, on-prem, or open-source-model deployments with query logging, PII redaction, and audit trails built in.
📊RAG evaluation & monitoring
RAGAs / TruLens / custom eval harnesses, retrieval-quality metrics, and dashboards so accuracy is measured, not assumed.
What you get
- ✓Custom RAG pipelines & retrieval
- ✓LangChain / LlamaIndex expertise
- ✓pgvector, Pinecone, Weaviate, Qdrant
- ✓On-prem & open-source LLM deployment
- ✓GDPR / HIPAA-safe architectures
- ✓Evaluation, monitoring & guardrails
Technologies we build with
We are technology-agnostic and pick the right stack for your goals — here are the tools our engineers use most to ship fast, scalable rag development.
Frontend
ReactAngularVue.jsNext.jsTypeScriptTailwind
Backend
.NET / C#Node.jsPythonJavaGoGraphQL
AI / ML
LLMsRAGVector DBsPyTorchLangChainMCP
Cloud & DevOps
AWSAzureGCPDockerKubernetesCI/CD
Mobile
FlutterReact NativeSwiftKotlin
Data
PostgreSQLMongoDBRedisKafkaSnowflake
Industries we serve
We bring domain context to every engagement — mapping the compliance, workflows, and metrics that matter in your sector so the software we ship drives real outcomes.
Legal & complianceHealthcare & pharmaFinance & insuranceManufacturing & maritimeSaaS supportEnterprise IT & policy Q&A
RAG vs Fine-Tuning vs Prompt Engineering — which is right for you?
Most teams reach for fine-tuning too early. For grounding an LLM in your own knowledge, RAG is almost always the pragmatic choice — cheaper to run, easier to update, and auditable.
| Approach | Best for | Pros | Cons |
|---|
| RAG | Answering from private, changing knowledge with source citations | Updates instantly when docs change; cite sources; lowest ongoing cost; works with any base model | Requires a retrieval pipeline; quality depends on chunking and reranking |
| Fine-tuning | Teaching a model a specific tone, format, or narrow task | Stronger on style and structured output; smaller prompts | Expensive to retrain per data change; hard to audit; needs curated training data |
| Prompt engineering only | Simple, general tasks with small context | Zero infrastructure; instant iteration | Cannot ground on large private knowledge; hallucinates on domain-specific questions |
In practice we ship RAG in ~80% of engagements, sometimes combined with light fine-tuning for tone. See our blog for a full decision framework.
Flexible engagement & pricing models
Every business is different, so we offer several ways to work together. Start with a free consultation and we will recommend the model that best fits your scope, timeline, and budget.
📐Fixed-scope project
Best for well-defined builds. We agree scope, timeline, and a fixed price up front so you know exactly what you get and what it costs.
👥Dedicated team
A cross-functional squad — engineers, designers, QA, and a project lead — that works as an extension of your business, month to month.
🔧Staff augmentation
Add senior developers or AI specialists to your existing team to fill skill gaps and accelerate delivery without long hiring cycles.
🔁Support & retainer
Ongoing maintenance, optimisation, and new-feature development on a predictable monthly retainer after launch.
Our delivery process
1Discovery & strategy
We map your goals, users, and workflows, then define scope, architecture, and a clear roadmap with fixed milestones and transparent pricing — no surprises, no scope creep.
2UX design & prototype
Wireframes and interactive prototypes validate the experience and de-risk the build before a line of production code is written, saving you time and budget.
3Agile development & QA
We build in two-week sprints with continuous integration, automated testing, and live demos, so you see working software early and steer it as it grows.
4Launch, support & scale
We deploy to the cloud with monitoring, security hardening, and CI/CD, then iterate with analytics-driven improvements and ongoing support as you scale.
Frequently asked questions
What is Retrieval-Augmented Generation (RAG)?
RAG is an AI architecture that pairs a large language model with a search index over your private data. At query time, the system retrieves the most relevant passages and passes them to the model as context, so answers are grounded in your own documents and cite their sources instead of relying on the model's pre-trained memory alone.
When should I choose RAG instead of fine-tuning?
Choose RAG when you need accurate answers over changing private knowledge — docs, wikis, product data, policies — and want to update the answers by editing the source. Choose fine-tuning when you need the model to adopt a very specific tone or narrow task, and only after RAG has been ruled out; fine-tuning is far more expensive to iterate on.
Is RAG GDPR-compliant?
It can be. We deploy RAG in EU regions, on your own infrastructure, or entirely on-premise using open-source models so no customer data leaves your perimeter. PII redaction, query logging, and access controls are built in for GDPR, HIPAA, or SOC 2 requirements.
What vector databases do you work with?
pgvector (Postgres), Pinecone, Weaviate, Qdrant, Milvus, Azure AI Search, and Elasticsearch/OpenSearch. We recommend based on scale, latency, cost, and whether you need self-hosted vs managed.
Can RAG hallucinate?
Yes, if the retrieval step brings back irrelevant passages or the model ignores them. We reduce this with high-quality chunking, hybrid retrieval, reranking, prompt design that forces cite-source answers, and evaluation harnesses that catch regressions before deploy.
How much does a custom RAG project cost?
A production RAG pilot typically runs $15,000–$45,000 over 4–10 weeks, depending on the number of source systems, security requirements, and evaluation depth. Enterprise deployments with fine-grained access control, on-prem models, and evaluation dashboards cost more. We provide a fixed-scope quote after a free consultation.
Can you connect RAG to my existing knowledge base?
Yes. We ingest from Confluence, SharePoint, Notion, Google Drive, Zendesk, Salesforce, S3/Azure Blob, SQL databases, and custom APIs — with incremental sync so updates flow through in near real-time.