Your knowledge, ask-able.
Every SOP, contract, wiki page and PDF — searchable in plain English. Cited answers. Access-controlled. Not another Slack thread asking "where's that doc?"
Under the hood.
Ingest
Notion, Drive, Confluence, Sharepoint, PDFs, GitHub, Slack
Chunk
Semantic splitting with overlap for context preservation
Embed
OpenAI, Cohere or open-source models depending on cost
Store
pgvector, Pinecone or Qdrant with metadata filters
Retrieve
Hybrid search: keyword + vector with re-ranking
Answer
GPT-4 / Claude with citations and confidence score
Where teams put it to work.
Internal wiki AI
Ask any question about your ops, product, HR, finance.
Customer-facing search
Turn your docs site into a chat interface.
Compliance & policy
Instant answers grounded in your governance docs.
Enterprise-safe by default.
- ACL-aware retrieval
- No training on your data
- Source citations always
- Prompt injection guardrails
- Audit log of every query
- Self-hosted option available
RAG FAQs.
The AI looks things up in your own documents before answering, so it stays grounded in your data instead of guessing.
Chunking strategy — the choice that decides 60% of retrieval quality
Vector DB and model choice matter less than how you chunk. Here's what actually moves the needle.
| Strategy | Chunk size | Best for | Failure mode |
|---|---|---|---|
| Fixed-token | 300-500 tokens | Homogeneous prose | Splits mid-sentence, mid-table |
| Recursive character | 500-1200 with overlap | Mixed docs | Overlap inflates index cost |
| Semantic (embedding-based split) | Variable | High-quality prose | Slow to build |
| Structural (heading-aware) | One per H2/H3 | Docs, manuals | Fails on unstructured PDFs |
| Proposition-based | 1-3 sentences | QA-heavy corpora | Extraction cost at ingest |
| Parent-child (small-to-big) | Retrieve small, feed large | Long-context models | More complex index |
Retrieval evaluation — the metrics that matter
"It works in demos" is not evaluation. These are the metrics we track on every retrieval change.
- Context recall
Of the facts needed to answer, what % appear in retrieved chunks? Below 80% means retrieval is the ceiling, not the LLM.
- Context precision
Of retrieved chunks, what % are actually relevant? Low precision wastes context window and confuses the model.
- Answer faithfulness
Does the answer only use facts from context? Measured with an LLM-as-judge on a held-out set.
- Answer relevance
Does the answer actually address the question? Catches on-topic-but-off-question failures.
- Hit@k and MRR
Classic IR metrics on a labelled question set — the fastest way to compare embedding models.
- Latency p95
Retrieval + generation p95 under 3s or users abandon. Reranker adds 200-800ms; budget accordingly.
Turn docs into answers.
Start with a pilotServing businesses across Rayalaseema
Founder-led website design, development and automation — for businesses in Andhra Pradesh and beyond.
Automate what's costing your team hours
Describe the workflow — we'll reply with what's possible, what it costs, and where to start.
- Personal reply from Siddiq within 24 hours
- Honest advice — no pushy sales, no fake urgency
- Clear next steps, timeline, and estimate