Custom AI Solutions & LLM Integrations in Lahore, Pakistan
Suleman Zaheer designs and deploys production-ready AI systems — RAG knowledge assistants, autonomous multi-step agents, WhatsApp AI bots and Text-to-SQL business intelligence interfaces — using LangChain, LlamaIndex, OpenAI GPT-4o, Google Gemini and vector databases.
What Business Problems Does AI Solve?
AI adds measurable value when it replaces specific, repetitive knowledge-work processes.
❌ Your support team answers the same 50 questions daily
✅ A RAG chatbot trained on your product documentation and FAQs handles 70–80% of repetitive queries automatically, 24/7.
❌ Business managers cannot query the database without an analyst
✅ A Text-to-SQL interface lets managers ask "What were last month's top-selling SKUs?" in plain English and get instant results.
❌ You have thousands of documents but cannot search them intelligently
✅ A vector-search RAG system retrieves semantically relevant document sections — not just keyword matches — for precise answers.
❌ Repetitive multi-step workflows require hours of manual work
✅ An agentic workflow automates the full chain: data retrieval → processing → document generation → email dispatch, unsupervised.
Core AI Engineering Capabilities
The technical disciplines that go into every AI project.
RAG System Architecture
Complete Retrieval-Augmented Generation pipelines: document ingestion (PDF, DOCX, CSV, URLs), chunking strategy, embedding model selection, vector store indexing (Pinecone / pgvector / ChromaDB), retrieval chain construction and LLM response grounding with source citation.
LangChain & LlamaIndex Agent Workflows
Multi-step agentic workflows where the LLM reasons about which tools to call: web search, SQL query, calculator, email sender, or external API. ReAct and Plan-and-Execute agent patterns. Human-in-the-loop approval steps for high-stakes actions.
Foundation Model Integrations
Production API integrations with OpenAI (GPT-4o, o1), Google Gemini (1.5 Pro, Flash), Anthropic Claude (3.5 Sonnet), Mistral and open-source Llama models via Ollama. Function calling, structured JSON output and streaming responses.
WhatsApp & Omnichannel AI Bots
WhatsApp Business Cloud API webhook integration. AI-powered conversation management with memory, intent classification, escalation to human agents, and reply with text, images and interactive buttons. Same engine deployed to Telegram, website widget and Discord.
Vector Database Engineering
Selection and configuration of Pinecone (managed cloud), pgvector (PostgreSQL extension for self-hosted), ChromaDB (local or Docker) or Qdrant. Embedding model selection (OpenAI text-embedding-3, Cohere, BGE). Namespace organisation for multi-tenant RAG applications.
Text-to-SQL & BI Query Interfaces
Natural language to SQL interfaces that let non-technical business managers ask questions in plain English or Urdu and receive instant data tables and charts. Connects to your existing PostgreSQL, MySQL or SQLite databases. Deployed as a web panel or integrated into existing dashboards.
AI Engineering Stack
AI Project Case Studies
Representative projects showing real AI engineering problems and solutions.
AI Customer Support Bot – Document Knowledge Base
LangChain · pgvector · GPT-4o · FastAPI · WhatsApp Business API
RAG system chunking and indexing 350+ technical SOPs and manuals into pgvector embeddings. Achieved 94.6% retrieval accuracy on domain queries with strict hallucination guardrails. Resolves 68% of first-touch support inquiries without human intervention across English and Roman Urdu.
Business Intelligence – Natural Language SQL Interface
LangChain SQL Agent · PostgreSQL · Next.js · GPT-4o
Engineered a read-only SQL semantic agent allowing non-technical managers to query 52 PostgreSQL tables in plain English. Replaced 85% of ad-hoc manual SQL ticket requests and delivered real-time executive data summaries with an average query-to-chart latency under 2.2 seconds.
AI Project Delivery Process
Discovery & Data Audit
Identify use case, define success metrics, audit available documents and databases, select LLM and vector store based on cost, latency and privacy requirements.
Prototype & RAG Pipeline
Build a working prototype with document ingestion, embedding, retrieval and LLM response generation. Evaluate answer quality with test queries before production.
Integration & Channel Deployment
Connect to WhatsApp Business API, website widget or internal dashboard. Add conversation memory, escalation logic and usage analytics.
Evaluation, Monitoring & Handover
Measure retrieval precision and answer quality. Set up logging and cost monitoring. Handover with documentation and team training.
AI Solutions Pricing
Indicative project budgets. Final price depends on knowledge base size, number of channels, agent complexity and LLM provider costs.
Starter AI
- ✓Website knowledge-base chatbot
- ✓Up to 100 document pages ingested
- ✓Single LLM provider integration
- ✓Chat widget deployment
- ✓30-day post-delivery support
Professional
- ✓WhatsApp + website omnichannel bot
- ✓Custom RAG with vector database
- ✓Multi-language (English + Urdu)
- ✓Human escalation routing
- ✓60-day support
Enterprise
- ✓Full agentic workflow automation
- ✓Text-to-SQL BI interface
- ✓Multi-tenant RAG system
- ✓Self-hosted vector DB for data privacy
- ✓90-day SLA support
AI Solutions – Frequently Asked Questions
What is RAG and why does a business need it?
RAG (Retrieval-Augmented Generation) connects a large language model to your own company documents. Without RAG, an LLM answers from general training data and can hallucinate incorrect facts. With RAG, every answer is grounded in your verified documents retrieved in real time.
How does LangChain orchestrate an AI agent?
LangChain provides chains, tools and agents. An agent uses an LLM as a reasoning engine to decide which tools to call — web search, database query, or API call — and in what order to complete a multi-step task.
What is a vector database and when is one needed?
A vector database stores documents as numerical embedding vectors. When a user asks a question, the query is embedded and semantically similar document chunks are retrieved. It is needed when your knowledge base is too large for a context window, or when semantic similarity is more useful than keyword matching.
Which LLM should a business use — GPT-4o, Gemini or Claude?
The choice depends on the use case. GPT-4o is well-suited for complex reasoning and function calling. Gemini 1.5 Pro has the longest context window (1M tokens) for document analysis. Claude 3.5 Sonnet excels at nuanced writing and instruction following. Suleman Zaheer selects the appropriate model based on cost, latency and task requirements.
Can an AI chatbot be deployed to WhatsApp?
Yes. Through the official WhatsApp Business Cloud API, Suleman Zaheer connects custom AI engines to a WhatsApp Business number. The bot receives messages via webhook, processes them through a RAG pipeline, and replies with text, images or interactive buttons.
How is company data kept private in an AI system?
Enterprise-tier zero-data-retention API plans ensure prompts are not stored for model training. Vector databases can be self-hosted (ChromaDB, pgvector) so proprietary documents never leave your infrastructure. All API keys are server-side environment variables — never exposed in the frontend.
How much does custom AI development cost in Lahore, Pakistan?
AI projects start from PKR 60,000 for a simple knowledge-base chatbot, PKR 120,000–200,000 for a WhatsApp RAG bot with custom document ingestion, and PKR 300,000–400,000+ for enterprise agentic workflows with Text-to-SQL and multi-source retrieval. Contact samstacktechs@gmail.com for a free scoping call.
Related Services
Integrate AI Into Your Business Today
Consult directly with Suleman Zaheer to identify high-ROI AI opportunities and define a production-ready implementation plan.