Document ingestion and parsing
Connectors for SharePoint, Confluence, Google Drive, databases and file stores, with layout-aware parsing for PDFs, tables and scans so structure survives the trip into the index.
Generative AI
RAG development services grounded in your data.
Our RAG development services give large language models a reliable memory: your policies, manuals, tickets and records, retrieved at query time and cited in every answer. The result is AI that knows your business and shows where its information came from.
- Answers with citations
- Permission-aware retrieval
- Measured retrieval quality
01 Overview
Retrieval augmented generation that actually finds the answer
Retrieval augmented generation sounds simple: search your content, hand the results to a model, get an answer. In practice, quality lives or dies in the details. How documents are parsed and chunked, which embedding model is used, whether keyword and semantic search are combined, and how results are reranked all decide whether the model sees the right passage or a near miss.
Our RAG development services cover that full pipeline, from ingestion connectors and document parsing to hybrid search, access control and answer generation. We measure retrieval and answer quality separately, so when something goes wrong you know whether to fix the search or the prompt. Enterprise RAG solutions also need to stay current, so we build incremental indexing that keeps the knowledge base in sync as source systems change.
02 Capabilities
RAG Development capabilities.
End-to-end RAG Development: strategy, engineering and the production operations that keep it working.
Chunking and embedding strategy
We test chunk sizes, overlap, semantic segmentation and embedding models against your content, choosing the combination that retrieves the right passages most consistently.
Vector database development
Design and tuning of vector stores such as Pinecone, Weaviate, Qdrant or pgvector, including metadata filtering, namespaces and index settings matched to your query volume.
Hybrid search and reranking
Combining BM25 keyword search with dense vectors, then reranking with cross-encoders, catches exact product codes and paraphrased questions alike, which pure semantic search often misses.
Permission-aware retrieval
Document-level access controls mirror your source systems, so the RAG pipeline only retrieves content each user is already allowed to see, enforced on the server rather than the prompt.
RAG evaluation and tuning
Golden question sets, recall and precision metrics, and faithfulness scoring with tools like Ragas make quality measurable, so every change to the pipeline is judged on evidence.
03 How we deliver
From first call to production.
Every RAG Development engagement follows the same five stages, so you see value early and risk stays low.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
04 Use cases
Where it pays off.
Practical, high-value applications we design and build for clients across industries.
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UC-01
Internal knowledge base chatbot
Let employees ask plain-language questions about HR policies, IT procedures or engineering docs and get cited answers instead of hunting through wikis and shared drives.
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UC-02
Customer support answer assistance
Surface relevant help articles, past ticket resolutions and product specs to agents or customers, drafting grounded replies that reference the exact source passage.
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UC-03
Contract and policy research
Search across large libraries of contracts, regulations or clinical guidelines, comparing clauses and summarizing obligations with links back to the original paragraphs.
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UC-04
Technical documentation assistant
Help field technicians and engineers query equipment manuals, schematics notes and maintenance logs on the job, with answers tied to the specific model and revision.
05 Technology
The stack we use.
Proven, well-supported tools chosen per project for accuracy, cost and long-term maintainability.
Vector and search
- Pinecone
- Weaviate
- Qdrant
- pgvector
- Elasticsearch
- OpenSearch
Orchestration
- LlamaIndex
- LangChain
- Haystack
- Unstructured
- Cohere Rerank
Models and evaluation
- OpenAI embeddings
- BGE embeddings
- Anthropic Claude
- Llama
- Ragas
06 Industries
Built for your sector.
We deliver RAG Development for regulated and fast-moving industries alike.
What is retrieval augmented generation and why use it?
Retrieval augmented generation pairs a search step with a language model. Before answering, the system retrieves relevant passages from your own content and gives them to the model as context. That lets the model answer questions about private, recent or specialized information it was never trained on, and it makes answers traceable to their sources.
Is RAG better than fine-tuning an LLM?
They solve different problems. RAG is best when answers depend on facts that change or must be cited, because you update the index rather than retrain a model. Fine-tuning is better for teaching a consistent style, format or specialized task behavior. Many production systems combine both: a fine-tuned model working over a well-built RAG pipeline.
Which vector database should we choose?
It depends on scale, hosting preferences and existing infrastructure. Managed services like Pinecone reduce operational work, Weaviate and Qdrant offer strong open-source options, and pgvector keeps vectors inside PostgreSQL you already run. We benchmark candidates on your data and query patterns before recommending one, since retrieval quality matters more than brand.
How do you reduce hallucinations in a RAG system?
Better retrieval is the first lever: hybrid search, reranking and good chunking put the right evidence in front of the model. We then instruct the model to answer only from provided context, require citations, and let it say it does not know. Faithfulness checks in evaluation flag answers that drift beyond the retrieved sources.
Can a RAG pipeline respect our existing document permissions?
Yes. We carry access control metadata from source systems into the index and filter results by the requesting user's permissions at query time. Because filtering happens in the retrieval layer, restricted content never reaches the model for users who should not see it. Permission changes in source systems propagate through incremental sync.