AI assistants that know your business better than your interns.
We build RAG-powered AI assistants trained on your company's knowledge base — customer support bots, internal search tools, and onboarding assistants. With hallucination guardrails, confidence scoring, and seamless human handoff.
73%+ Automated Resolution
Our RAG assistants resolve the majority of queries autonomously, with graceful human handoff for the rest. No more bottlenecked support queues.
Hallucination Guardrails
Confidence scoring, source attribution, and factual grounding checks. Your AI assistant won't make things up — especially critical for fintech and healthcare.
Plugs Into Your Stack
Integrates with your CRM, ticketing system, Slack, WhatsApp, and internal tools via n8n orchestration. Not a standalone silo.
Your Code, No Lock-In
We build on open-source infrastructure (Qdrant, LangChain, n8n) and hand over the codebase. Swap LLM providers anytime. No recurring AI-tax.
When Rule-Based Chatbots Aren't Enough
Rule-based chatbots work well for narrow, well-defined use cases — checking order status, routing to the right department, answering a fixed set of FAQs. If that's what you need, you don't need us. But when your knowledge base is large, constantly changing, or the question space is too broad to hard-code every path, a RAG (Retrieval-Augmented Generation) approach makes sense. Instead of decision trees, a RAG-powered assistant searches your actual knowledge base — docs, FAQs, policies, product data — and generates contextual responses grounded in your real information. For a fintech client with 8,000+ knowledge articles, rule-based resolution sat at 12%. RAG brought that to 73%. And when it doesn't know something, it says so and routes to a human with full context — instead of hallucinating an answer.
How We Build AI Assistants
Every AI assistant we build follows the same proven architecture: a vector database (typically Qdrant) indexes your knowledge base into semantic embeddings. When a user asks a question, the system retrieves the most relevant documents and feeds them as context to an LLM (Claude, GPT-4, or AWS Bedrock models) for response generation. But the architecture is only half the story. What separates our assistants from demo-grade chatbots is the guardrail layer: confidence scoring that routes low-certainty queries to humans, source attribution so users can verify answers, input sanitization to prevent prompt injection, and a continuous feedback loop that improves accuracy over time. We also build the n8n orchestration that connects the assistant to your CRM, ticketing system, and internal tools.
Use Cases We've Shipped
Customer support automation: a Series C fintech saw automated ticket resolution jump from 12% to 73%, cutting average resolution time from 4.2 hours to 8 minutes. Internal knowledge search: engineering teams find answers in internal docs instantly instead of pinging senior devs on Slack. Sales enablement: AI assistants that prep sales reps with relevant case studies, competitor analysis, and pricing guidance before calls. Onboarding bots: new hires get answers to policy questions, IT setup guides, and HR FAQs without waiting for anyone. Each of these is a real system we've built, running in production, handling real traffic.
Why Funded Companies Outsource This
Building a production-grade RAG system is straightforward if you've done it before. If you haven't, it's a minefield. Chunking strategies, embedding model selection, retrieval tuning, prompt engineering, hallucination prevention, latency optimization, cost management — each of these is a rabbit hole that eats weeks if you're learning on the job. You hired ML engineers to build your core product, not to become RAG infrastructure experts. We bring the specialized expertise, ship in 4–8 weeks, and hand off a system your team can maintain. Our code is yours — no vendor lock-in, no monthly AI-tax.
Technology Stack
Frequently Asked Questions
Which LLM do you use — GPT-4, Claude, or something else?
We're model-agnostic and recommend based on your use case. Claude excels at long-context reasoning and financial/regulatory content. GPT-4 is strong for general-purpose tasks. AWS Bedrock is ideal when data residency matters. We design the architecture so you can swap models without rebuilding the system.
How do you prevent the AI from hallucinating?
Three layers of protection: retrieval grounding (responses are based only on retrieved documents, not the LLM's general knowledge), confidence scoring (low-certainty responses get routed to humans), and factual verification (for domains like finance, we cross-reference generated responses against actual database records). The result is an assistant that says 'I don't know, let me connect you with a human' rather than making something up.
How long does it take to build a RAG assistant?
A production-ready RAG assistant typically takes 4–8 weeks from kickoff to deployment. This includes knowledge base ingestion, embedding pipeline setup, LLM integration, guardrails, testing, and deployment. Simple use cases (FAQ bot with static docs) can ship faster. Complex ones (multi-source, multi-language, with compliance requirements) take longer.
Can the assistant learn from conversations over time?
Yes. We build a feedback loop where human-resolved conversations (cases the AI couldn't handle) get flagged for knowledge base updates. An n8n workflow monitors these, and your team can approve new knowledge articles that feed back into the RAG pipeline. The system improves continuously without retraining the LLM.
Ready to get started?
Book a free 30-minute consultation. We'll discuss your specific needs and outline a clear path forward.
Book a Free Consultation →