What's included in your build
Every project is delivered with complete transparency and direct technical accountability. No sales middlemen, no bloated code.
- Document ingestion pipeline handling PDFs, manuals, Notion, and databases
- Semantic chunking and vector embeddings using OpenAI / Cohere models
- Vector database setup and hybrid search indexing using pgvector or Pinecone
- Strict hallucination guardrails and automated inline source page citations
- Custom front-end chat interface with conversation history and feedback tools
- Role-based document access controls and enterprise privacy protections
Week-by-week sprint timeline
We work in a strict 28-day sprint calendar. You receive a clickable demo on day 14 and a tested, production-ready product on day 28.
| Sprint Phase | Milestone | Deliverables & Outcomes |
|---|---|---|
| Week 1 | Data Audit & Pipeline Architecture | Document parsing strategy, chunking logic, embedding model selection, and vector database schema design. |
| Week 2 | Working Retrieval PoC & Demo | Functional conversational interface querying your sample documents on a private staging URL by Day 14. |
| Week 3 | Guardrails, Tools & Citations | Prompt injection defenses, confidence score filtering, automated citations with page numbers, and API tool integration. |
| Week 4 | Load Testing, Security & Handover | Latency benchmarking (<800ms time-to-first-token), enterprise access control, and complete pipeline code handover. |
Sprint packages & scope tiers
Flexible sprint packages tailored to your product stage, feature roadmap, and business goals.
Document RAG Assistant
Ingestion of up to 10k documents/PDFs, semantic vector search, chat interface with citations, 4-week delivery.
- 10,000 document ingestion pipeline
- pgvector hybrid search index
- Chat interface with page citations
- Working demo in Week 2
- Zero data retention setup
Conversational AI Agent
Multi-turn dialogue, database tool calling, CRM integration, role-based document access, 4-week delivery.
- Database & API tool calling
- CRM / ERP webhook integration
- Role-based permission filtering
- Streaming response interface
- Production AWS container launch
Autonomous LLM Pipeline
Multi-agent orchestration, complex reasoning workflows, custom evaluation benchmarks, private model deployment.
- Multi-agent reasoning workflows
- Custom evaluation benchmarks
- Self-hosted open weights (Llama 3)
- Enterprise SLA & latency tuning
- Priority ongoing engineering
Technology stack & framework versions
We build with proven modern technologies that ensure blazing performance, effortless maintainability, and easy hiring for your future in-house engineers.
Enterprise AI Case Study
Corporate Legal & Advisory AI Knowledge Engine
Built a private, retrieval-augmented intelligence assistant grounded in over 10,000 internal contracts and compliance guidelines.
Frequently asked questions
What is RAG and why is it better than training a model?
Retrieval-Augmented Generation (RAG) retrieves relevant excerpts from your proprietary documents in real-time and passes them to an LLM to formulate an answer. It costs a fraction of fine-tuning, updates instantly when documents change, and provides verifiable citations.
What information is needed to build a custom AI chatbot?
We require access to your source documents (PDFs, knowledge base articles, or database schemas) and an understanding of the common queries your users ask. We then design custom chunking, embedding, and retrieval pipelines tailored to your content.
Will my private company data be used to train public AI models?
No. We utilize enterprise API agreements with zero-data-retention policies and encrypt all vector embeddings, ensuring your private IP is never used for external model training.
How do you prevent the AI chatbot from hallucinating?
We enforce strict system prompts that instruct the model to state when information is not present in the retrieved context, require inline citation references, and filter low-confidence vector matches.
Ready to build your AI Chatbot Development?
Schedule a direct technical scoping call. We'll tell you honestly whether 4 weeks is realistic and provide a full technical roadmap within 24 hours.