AI Development Company for RAG Chatbots & Agentic AI
Bracket Mind is an AI development company in Gujranwala, Pakistan, building RAG chatbots, AI agents and LLM integrations for clients in 8+ countries. AI automation we built cut one client's manual data processing by 85%, saving about $120K a year. We work across Anthropic Claude, OpenAI, Azure OpenAI and AWS Bedrock in production.
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What AI systems has Bracket Mind built?
We build AI that answers from your data and acts inside your workflows. For one client, we work on a shared .NET AI backend that runs the AI features of four products and routes each task across eight provider types, failing over to the next ranked model on transient errors.
An autonomous agent we built checks a work list for new tasks every minute and completes the forms with nobody watching, and our own product, Aidfolk, answers customer questions from a business's own documents with a citation on every answer.
- RAG chatbots grounded in your documents, websites and databases, with citations
- AI agents with tools, skills and MCP integrations, with risky actions paused for a person's approval
- Agentic automation that reads tasks, fills forms and reports what it could not complete
- LLM integration and routing across Claude, OpenAI, Azure OpenAI, Gemini and Bedrock
- Guardrails: prompt-injection defenses, schema-checked output and per-call cost metering
What makes a RAG chatbot production-grade?
Three things separate a production RAG system from a weekend demo. Grounding: the bot answers from retrieved sources, cites them, and says so when it cannot answer. Isolation: in a multi-tenant system, one customer's data must never surface for another. Cost control: every model call is metered, so AI spend shows up per customer instead of as a surprise bill.
We build all three into the architecture, not into a prompt. In Aidfolk, retrieval is hybrid search: pgvector similarity and Postgres full-text search fused with reciprocal rank fusion in a single query, scoped to the tenant and guarded by forced row-level security, and every AI call is priced into an append-only ledger.
How do you keep AI agents safe when they act on their own?
On the client backend we work on, write actions and every MCP tool call pause the run until a person approves, and no answer within five minutes counts as no. Code the model writes runs in a throwaway Docker container with 1 CPU, 512 MB of memory, a 120-second default time limit and no credentials inside, and the container is force-removed after each run.
Our work-list agent treats the files attached to a task as data, never as instructions, and never submits an incomplete form: it saves what it can and leaves a note instead.
Proof: systems we built
Multi-Tenant AI BackendProduction
Shared AI Platform Backend
One production .NET backend running the AI features of four client products — a medical platform, a community platform and two storefronts — instead of four separate AI stacks.
- One AI backend, four products
- 8 provider types, routed per task
- Risky tool calls wait for a human
Team project · Read the case study
Autonomous AI AutomationProduction
Autonomous Work-List Agent
An AI agent that picks up assigned work-list tasks and completes the forms on its own — checking for new work every minute, with a person kept in the loop wherever it matters.
- One throwaway container per task
- No API keys inside the container
- Never submits an incomplete form
Founder-built · Read the case study
AI Customer Support SaaSLive product
Aidfolk
AI customer support that answers from a business's own docs. Add your files, website and help pages, and get a chat agent for your site that cites where every answer came from — set up in the browser, no code. Our own product, running in production.
- In production since October 2026
- Every answer cites its source
- 1,077 backend tests passing
Founder-built · Read the case study
Questions buyers ask
How do I choose an AI development company in Pakistan?
Ask to see AI running in production rather than a demo, and ask how it handles citations, tenant isolation and cost metering. Bracket Mind's own AI product, Aidfolk, has been live at aidfolk.com since October 2026, and we build agent and RAG systems for clients in 8+ countries. We will walk you through the code-level decisions behind any of our case studies.
How much does a RAG chatbot cost to build?
The build cost scales with the number of data sources, whether you need multi-tenancy, and how deeply the chatbot integrates with your systems, so we quote each one after a scoping call. Ongoing LLM usage is a running cost separate from the build. We design for cost visibility from the start, metering every model call so you can see what each customer or feature costs.
Can AI really automate our manual back-office work?
Often, yes: AI automation we built cut one client's manual data processing by 85%, saving about $120K a year. The caveat is that automation is only safe with defensive engineering. Our work-list agent fills only the fields it was asked about, drops hallucinated rows instead of writing them, and saves with a note rather than submitting an incomplete form.
Which LLM providers do you work with?
We integrate Anthropic Claude, OpenAI, Azure OpenAI, Google Gemini and AWS Bedrock, with vector search on Pinecone or pgvector. A client AI backend we work on routes across eight provider types and fails over to the next ranked model on transient errors. You are not locked to one vendor, and AI cost is reported per model.
Can your AI agents connect to MCP servers?
Yes. We build MCP integrations: servers are registered as data, their tools are discovered when each run starts, and every MCP tool call waits for a person's approval. On one client backend we cap MCP results at 24,000 characters, after a single oversized result cost 2.1M input tokens.