AI Development
RAG System Development
Build Retrieval-Augmented Generation systems that answer from your documents—not guesses. We design ingestion, search, and guardrails so responses stay grounded, traceable, and useful in production.
Benefits
What you get
Clean ingestion pipelines (PDFs, docs, web, tickets, wikis)
Chunking + metadata strategy for high-quality retrieval
Vector database setup (Pinecone, Weaviate, Chroma, pgvector)
Hybrid search + reranking for better accuracy
Citations, excerpts, and “open source” links in answers
Monitoring, evals, and continuous tuning over time
Features
What we deliver
Ingestion & Normalization
Parse PDFs, Word, Markdown, web pages, and knowledge tools. Keep structure where it matters (headings, sections, tables) and add clean metadata.
Chunking & Metadata Design
Right-sized chunks, smart overlap, and reliable metadata (product, version, date, owner) so retrieval stays accurate and maintainable.
Embeddings & Indexing
Use OpenAI, Cohere, or open-source embeddings depending on cost, privacy, and language needs. Support scheduled re-indexing and incremental updates.
Hybrid Search + Reranking
Combine semantic + keyword search and rerank results for stronger precision—especially for product names, error codes, and exact phrases.
Grounded Answers + Citations
Answers include citations and highlighted excerpts. If sources are weak, the system can ask follow-ups or respond with a safe fallback.
Quality, Safety & Observability
Evaluation sets, failure tracking, prompt/version control, and metrics (retrieval hit rate, citation quality) so performance improves over time.
Process
How we work
Discovery
Requirements gathering and planning
Design
UI/UX design and prototyping
Development
Iterative sprints with demos
Launch
Deployment and support
Tech Stack
Technologies we use
Core
Tools
Services
Use Cases
Who this is for
Internal Knowledge Assistant
Search SOPs, policies, onboarding docs, and runbooks with citations and permission-aware access.
Support Deflection (Grounded)
Answer product questions from your docs and help-center content, with escalation paths when confidence is low.
Document & Research Workflows
Summaries, Q&A, and comparisons across large document collections (legal, technical, compliance) with traceability.
Search Upgrade
Turn keyword search into “answer + sources” experiences while still supporting classic search results when needed.
Decision Guides
Not sure which to choose?
FAQ
Frequently asked questions
No system can guarantee zero errors. RAG reduces hallucinations by grounding answers in retrieved sources, adding citations/excerpts, and using safe fallbacks when evidence is weak.
PDFs, Word/Google Docs, Markdown, websites, help centers, databases, and tools like Notion/Confluence/Drive. We choose connectors based on your stack and access controls.
We add evaluation queries, track bad answers, tune chunking/metadata, improve prompts, and introduce reranking or hybrid search where needed. It’s an iterative loop, not a one-time setup.
Yes. We can implement per-user access rules, tenant isolation, and source-level permissions. The final approach depends on your identity system and where documents live.
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