AI Integration

Artificial Intelligence has captured global attention, but the vast majority of enterprise AI implementations fail to produce measurable business value. Off-the-shelf generative tools and generic chatbot wrappers frequently invent facts (hallucinations), leak confidential customer information, and lack connection to real operational systems.

At DotQ, we treat AI as an engineering capability, not a marketing gimmick. We build production-ready intelligent systems that connect state-of-the-art foundation models (OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini) directly into your private business databases, APIs, and customer workflows with mathematical precision.

Standard foundation models only know information up to their training cutoff date and know nothing about your private business documents, customer orders, or proprietary procedures. Fine-tuning models on private data is prohibitively expensive and difficult to update continuously.

We solve this by engineering private Retrieval-Augmented Generation (RAG) architectures. We ingest your company’s PDFs, databases, Notion wikis, and technical manuals, converting them into high-dimensional vector embeddings using pgvector or Pinecone. When a user asks a question, we execute hybrid semantic search to retrieve the exact relevant excerpts, constraining the LLM to generate responses solely based on verified facts with clickable citations.

Modern AI is at its most powerful when it transitions from passive reading to active execution. Using structured JSON function calling and LangChain/LlamaIndex orchestration, we build autonomous software agents capable of executing complex multi-step workflows.

An AI agent can parse an unstructured customer inquiry email, verify the customer’s identity in your PostgreSQL database, retrieve their tracking number from Shiprocket, check return eligibility rules, draft a courteous response, and queue a refund for human manager sign-off—all within 4 seconds.

Deploying AI into production requires rigorous risk management. We implement adversarial prompt injection defense layers (such as Llama Guard and custom regex shields) to prevent malicious users from jailbreaking your system or extracting private system instructions. Personally Identifiable Information (PII) is automatically scrubbed before reaching external LLM APIs.

To protect your bottom line from runaway API billing, we introduce semantic caching tiers using Redis. If a user asks a question identical or semantically equivalent to one answered recently, the verified response is served instantly from cache for zero API token cost.