RAG
Custom RAG implementation
Retrieval-augmented generation over your knowledge base, policies, SOPs, or product docs — with versioning and quality control.
Acceleration
LLM assistants for customer-facing and internal use.
Custom LLM-powered assistants — RAG over company knowledge, intent recognition, banking-grade output guardrails, and live-agent escalation patterns proven in production at the Faysal Bank WhatsApp channel.
RAG
Guardrails
Production-proven
What we deliver
LLM assistants that actually ship to production.
Demos are easy. Production is hard. Output safety, hallucination handling, and cost control are where most projects get stuck.
Internal
Internal Q&A assistants
Staff-facing assistants for HR queries, policy lookups, technical documentation, and operational decision support.
Customer
Customer-facing chatbots
Conversational assistants on web, WhatsApp, or in-app — answering questions, resolving issues, escalating when needed.
Handoff
Live-agent handoff
Smart escalation when AI confidence drops — with full conversation context handed to the human agent.
Safety
Output guardrails & safety
Topic scoping, prompt-injection defense, output filtering, and audit logs — banking-grade safety on every response.
Ops
Cost & quality monitoring
Per-message cost tracking, latency monitoring, quality sampling, and model-version A/B testing.
Tech we use
Production tech stack.
OpenAI GPT
Production-grade LLM
Anthropic Claude
Sonnet, Opus tiers
Open-source LLMs
Llama, Mistral when needed
Pinecone
Vector store
LangChain
Orchestration framework
PostgreSQL pgvector
Vector + relational
FAQs
Questions buyers typically ask.
OpenAI, Claude, or open-source LLMs?
Depends on the use case. OpenAI and Claude are typically best for production-grade accuracy. Open-source LLMs (Llama, Mistral) make sense when data residency, cost, or on-prem deployment matters. We pick during discovery based on what the workload actually needs.
How do you handle data privacy?
For banking and regulated clients, we use enterprise OpenAI or Claude APIs (zero-retention modes) or self-hosted open-source models when residency requires. RAG retrieval and prompts are designed to minimize PII exposure.
What about cost — LLM operating costs can spiral?
Yes, naive implementations get expensive. We design with caching, retrieval-first patterns, smaller models for routing, and per-message cost monitoring. Production cost discipline is baked in from day one.
How do you prevent hallucinations and harmful outputs?
Topic scoping at the prompt level, retrieval-grounded responses (RAG), output filtering, escalation thresholds, and human review queues for ambiguous cases. For Faysal Bank’s WhatsApp channel, this discipline runs at ~2M users daily.
Multilingual?
Yes — English, Urdu (Roman and Nastaliq), Arabic, and most major languages supported by GPT-4 and Claude. We test for language-specific edge cases during pre-production.
Tell us what you need to build, integrate, or operate.
Whether it’s a new product, a stuck integration, or a system that needs a fresh team — we’d like to hear about it.
Read by the team within 24 hours. No drip sequences, no bots.
Or email info@braincrop.io