LLMs that know
your data.
Not just the internet.
We build retrieval-augmented generation systems that ground every answer in your private documents, databases, and knowledge bases - with citations, not hallucinations.
Your Docs
PDFs, DBs, wikis
Chunking
Size + overlap strategy
Embeddings
text-embedding-3-large
Vector Store
Pinecone / pgvector
LLM
GPT-4o / Claude
Answer
Grounded + cited
Architecture decision
RAG vs fine-tuning: when to use which
When to use RAG
- ✓Your data changes frequently (docs, tickets, policies)
- ✓You need citations and auditability for every answer
- ✓You want to add AI without retraining a model
- ✓You have large corpora that won't fit in a context window
- ✓You need to retrieve across multiple data sources
- ✓Compliance requires traceable sourcing of every output
When to fine-tune instead
- →You need the model to adopt a very specific style or format
- →Your task is narrow and highly repetitive
- →Latency is critical and retrieval adds too much overhead
- →Your training data is stable and versioned
- →You want to reduce prompt size for efficiency
- →Task requires deeply embedded domain knowledge
Products
What we build
Document Q&A systems
Ask questions across PDFs, contracts, reports, and policies. Get precise answers with exact source references - not summaries.
Enterprise knowledge bases
Make your internal wikis, runbooks, and Confluence pages instantly searchable and answerable by your team - with access controls per user.
Semantic search
Replace keyword search with meaning-aware retrieval that finds the right content even when the exact words don't match the query.
Citation-backed generation
Every generated answer includes the source passage and document reference. Designed for legal, compliance, and regulated industries.
Production pitfalls
The 5 things that make RAG fail in production
01
Bad chunking strategy
Fix: We test chunk size, overlap, and boundary strategies per document type before building the index.
02
No reranking
Fix: Top-k retrieval returns noisy results. We add a reranker (Cohere, cross-encoder) to surface what's actually relevant.
03
Missing metadata filters
Fix: Without metadata, retrieval ignores date, source, and access scope. We design a metadata schema at the ingestion stage.
04
No eval harness
Fix: Without ground-truth QA pairs and automated scoring, quality regressions are invisible. We build evals before launch.
05
Ignoring latency
Fix: Retrieval + reranking + generation chains can be slow. We profile each step and apply caching and parallelisation.
Architecture
Our RAG stack
Ingestion pipeline
Chunking strategy
Embedding model
Vector store
Retrieval strategy
Reranking
Generation
Evaluation
Retrieval methods
How we retrieve
Dense retrieval
Semantic similarity via embeddings
Pros
- +Understands meaning and paraphrase
- +Works well for conversational queries
- +Language-agnostic embeddings
Cons
- –Misses exact keyword matches
- –Requires index rebuild on new data
- –Embedding model is a dependency
Default for conversational Q&A and domain knowledge bases.
Sparse (BM25)
Term-frequency keyword matching
Pros
- +Excellent for exact product names / codes
- +No embedding overhead
- +Proven and fast
Cons
- –Misses semantic equivalents
- –Sensitive to vocabulary mismatch
- –No cross-lingual support
Best for product catalogues, error codes, and ID-based lookups.
Hybrid search
Dense + sparse with RRF fusion
Pros
- +Best of both methods
- +Higher recall than either alone
- +Robust to diverse query types
Cons
- –More components to manage
- –Slightly higher latency
- –Requires tuning fusion weights
Our default for production systems where recall and precision both matter.
Featured result
"RAG pipeline that answers 94% of support tickets without human escalation - with citations from the product knowledge base."
94%
Auto-resolved tickets
< 3s
Median response time
0 hallucinations
In 6-month audit