Chatbot Development

How it works.

The system, step by step — each stage is an agent or service with a clear job and a hand-off to the next.

01 · Knowledge Ingestion

We scrape your wikis, PDFs, Notion, and databases, chunking text into semantic vectors.

02 · Vector Embedding

Storing your knowledge in high-performance Vector Databases (Pinecone/Milvus) for semantic search.

03 · Context Windowing

Retrieving the exact relevant snippets to feed the LLM context window for hallucination-free answers.

04 · Action Execution

The bot doesn't just talk; it calls APIs to process refunds, book meetings, or reset passwords.

Built for production, owned by you.

Proven frameworks, not thin wrappers. Open any capability for the technique, the stack and an implementation sketch.

Hallucination Control

Strict system prompting and citation layers that force the AI to stick to provided facts.

Grounded answers with citations — Technique: Citation-Based Decoding

We engineer a 'Skeptical' AI. The model is instructed to answer ONLY using the retrieved context chunks. If the answer is not in the source material, it is programmed to say 'I don't know' rather than invent facts. We force the model to output footnote citations [Doc ID: 12] which we validate programmatically before showing the user.

Stack: LangChain Self-Query, Guardrails AI, NeMo Guardrails

Omnichannel Deployment

One brain, deployed everywhere: Web, WhatsApp, Slack, MS Teams, and SMS.

Headless Conversational Core — Technique: WebSocket Gateway & Adapter Pattern

We separate the 'Brain' from the 'Body'. The core logic (LLM + RAG) sits on a central server exposed via WebSocket API. We build lightweight adapters for every channel (Twilio for SMS, Slack Bolt for Teams). State is managed centrally in Redis, so a user can start on Web and finish on SMS without losing context.

Stack: Redis (Pub/Sub), Twilio API, Slack Bolt, React Native

Long-Term Memory

Persisting user preferences and conversation history across sessions for hyper-personalization.

Persistent memory — Technique: Vectorized Memory & Entity Extraction

Standard chatbots forget you after the session closes. Ours builds a persistent user profile. We use LLMs to summarize conversations into key facts ('User likes red shoes', 'Budget is $5k') and store these as 'semantic memories' in a vector DB. On the next visit, we preload these memories into the context window.

Stack: Zep Memory, LangChain Entity Memory, PostgreSQL

Sentiment Analysis

Real-time emotion detection that triggers human hand-off protocols during escalation.

Sentiment-based escalation — Technique: NLU Classification & WebSocket Routing

Every user message is scored for sentiment (-1.0 to +1.0) before the LLM generates a reply. If the score drops below a threshold (e.g. -0.6 'Anger'), the 'Human Handoff' circuit breaker trips. The bot apologizes, pauses generation, and instantly routes the chat to a live agent dashboard via WebSocket, passing the full conversation history.

Stack: HuggingFace Sentiment Pipeline, Socket.io, Zendesk API

Typical stack: OpenAI/Anthropic, Pinecone/Weaviate, LlamaIndex, FastAPI, React

From experiment to production.

We scope it on your stack, ship a measured first system, and run it against a baseline you can read.