Rule-Based Automation Handles the Predictable. AI Handles Everything Else.
We build AI agents, MCP servers, and intelligent automation systems that read context, make decisions, and take action — on tasks too variable, too complex, or too unstructured for traditional automation. Built for production, not proofs of concept.
The Real Problem
Most AI Projects Don't Fail Because of the Technology
They fail because they were built as demos. A ChatGPT wrapper that impresses in a boardroom presentation. A document summarisation tool that works perfectly on clean PDFs and falls apart on the ones that actually come from clients. An AI chatbot that handles 40% of queries well and confidently gives wrong answers on the other 60% — with no way to know which is which.
The gap between an AI proof of concept and an AI system you can trust with real business operations is wider than most people expect. It's not about the model — it's about what surrounds it. Retrieval architecture, grounding, guardrails, fallback logic, monitoring, and the human-in-the-loop design that determines when the AI acts autonomously and when it escalates.
That's the standard we build to.
We've been building production AI systems since before “AI agent” became a buzzword. The projects we're proudest of aren't the ones with the most impressive demos — they're the ones running quietly in the background, handling real work, making real decisions, with failure rates low enough that clients stop thinking about them.
What We Build
AI & Intelligent Automation Services
From single LLM integrations to full multi-agent systems — covering every layer of the AI stack, from data infrastructure through to production monitoring.
AI Agent Development
AI agents that plan, execute multi-step tasks, use tools, and adapt based on what they encounter. They browse the web, query databases, call APIs, write and run code — and hand off to humans when a decision falls outside their confidence threshold.
MCP Server Development
Custom Model Context Protocol servers that give your AI systems controlled, auditable access to internal tools, databases, and services — with proper authentication, rate limiting, and audit logging so you know exactly what your AI is touching.
RAG Systems & Knowledge Base AI
Retrieval-Augmented Generation that lets AI answer questions using your actual data — internal documentation, product knowledge, historical records — with proper chunking, embedding selection, and re-ranking so the system surfaces accurate information, not just confident-sounding information.
LLM Integration & Fine-Tuning
Integrating GPT-4o, Claude, Gemini, Llama, and Mistral into production products — with deliberate prompt architecture, context window management, output parsing, latency optimisation, and cost control. Fine-tuning only when genuinely needed.
AI-Powered Document Processing
Document intelligence pipelines that extract, classify, and validate data from contracts, invoices, medical records, and legal filings — using OCR, layout analysis, and LLM-based extraction for the cases that rule-based parsing can't handle.
Intelligent Workflow Automation
Workflows that use AI to handle variable inputs — classifying incoming requests, extracting information from unstructured messages, making routing decisions based on context, and generating responses that match the situation. The workflow runs itself; AI handles the judgement calls.
AI Chatbots & Conversational Interfaces
Actual conversational AI that understands context, maintains thread across a conversation, knows when to answer and when to escalate, and integrates with your backend systems to take action — not just provide information.
Why OrchiX
AI That Works in Production, Not Just Demos
Three things we've learned building production AI systems that most people figure out the hard way.
Hallucination Is an Architecture Problem
You don't fix a hallucinating AI by switching models. You fix it by grounding every response in retrieved facts, validating outputs against known constraints, and routing low-confidence responses to a human. We build the grounding infrastructure first.
Agents Need Failure Modes, Not Just Success Paths
Every AI agent we build has explicit logic for what happens when it's wrong, stuck, or uncertain. It logs what it did and why. It alerts the right person when it can't proceed. It doesn't silently fail or confidently complete a task incorrectly.
The User Interface Is Half the Product
The best AI system creates problems if people don't trust it or can't correct it when it's wrong. We design the human-AI interaction alongside the AI system — feedback mechanisms, confidence indicators, correction flows, and audit trails.
Production-First, Not Demo-First
The projects we're proudest of aren't the ones with impressive demos — they're the ones running quietly in the background, handling real work, with failure rates low enough that clients stop thinking about them. That's the standard we build to.
Our Process
How We Build AI Systems That Actually Work
AI Feasibility & Use Case Definition (Week 1)
We assess whether AI is actually the right tool — and if it is, which approach makes sense. We map the task, define what good performance looks like, identify data requirements, and flag risks. An honest assessment, not a pitch for the most complex solution.
Data Audit & Architecture Design (Weeks 1–2)
AI systems are only as good as the data they work with. We audit your existing data, identify gaps, and design the retrieval or training architecture before touching a model. This is where most projects either get set up to succeed or quietly doomed.
Prototype on Real Data (Weeks 2–3)
A working prototype using your actual data — not a curated demo dataset. This surfaces edge cases and failure modes early, when they're cheap to address. You see real performance before significant development investment is made.
Production Build with Safety Rails (Weeks 3–8+)
Full system build with monitoring, logging, guardrails, and fallback logic. Every AI decision is traceable. Every failure triggers the right alert. Human-in-the-loop handoff points are designed and tested, not added as an afterthought.
Evaluation, Tuning & Deployment
Systematic evaluation across representative test cases — measuring accuracy, latency, cost, and failure rate. We tune until the numbers justify production deployment, then monitor for 30 days and adjust based on real-world performance.
Technology
Models, Frameworks & Infrastructure We Work With
We don't lock into a single model or framework. We match the stack to your use case, your latency requirements, and your budget.
LLMs & Foundation Models
GPT-4o / o1, Claude 3.5 / 3.7, Gemini 1.5 Pro, Llama 3, Mistral, Cohere
AI Frameworks
LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Haystack
Vector Databases
Pinecone, Weaviate, Qdrant, ChromaDB, pgvector
Orchestration & Agents
MCP (Model Context Protocol), OpenAI Assistants API, custom agent frameworks
Document Processing
AWS Textract, Azure Document Intelligence, custom OCR pipelines, Unstructured.io
Embedding Models
OpenAI text-embedding-3, Cohere Embed, Sentence Transformers
Infrastructure
AWS Bedrock, Azure OpenAI Service, Google Vertex AI, self-hosted GPU instances
Monitoring & Evaluation
LangSmith, Arize, Helicone, custom evaluation pipelines
In Practice
AI & Intelligent Automation in Practice
Concrete examples of AI systems we've built — so you can see what production-grade AI actually looks like before we scope yours.
AI-Powered Sales Research Agent
Sales rep enters a company name → AI agent pulls data from web sources, LinkedIn, and CRM history → generates a personalised briefing with pain points, recent news, likely objections, and talking points → delivered before the call. 30 minutes of research now takes 90 seconds.
Contract Intelligence System
Legal team uploads a contract → AI extracts key terms, flags non-standard clauses, compares against internal playbook, highlights missing provisions, and generates a risk summary → lawyer reviews and makes final judgement calls. Review time cut from 4 hours to 45 minutes.
Intelligent Customer Support Triage
Incoming ticket arrives → AI classifies issue type and urgency, extracts relevant account info from CRM, checks knowledge base for known solutions → generates a draft response if confidence is high enough → routes to the right specialist with full context pre-populated.
Internal Knowledge Assistant
8 years of documentation across SharePoint, Notion, and Google Drive — none of it easily searchable. RAG-powered assistant indexes all of it, lets staff ask questions in plain language, and returns accurate answers with source citations. Onboarding time for new hires cut by 40%.
FAQ
Questions About AI & Intelligent Automation
Consumer AI tools are general-purpose. They don't know your data, your systems, or your processes — and they're not integrated into your workflows. What we build connects AI to your specific context: your documents, your databases, your tools, your business logic. The output isn't a conversation — it's an action taken inside your system, or a structured result that feeds into your operations.
Most isn't. A data audit is part of our process before we build anything. We'll tell you what state your data is in, what's usable as-is, and what needs preparation before an AI system can work with it reliably. "Our data is a mess" is a common starting point — not a blocker.
There's no guarantee of zero errors — any AI system will make mistakes, and anyone who tells you otherwise is selling something. What we do is design systems that catch and contain errors: grounding responses in retrieved facts, flagging low-confidence outputs for human review, building feedback loops so the system improves over time, and monitoring for failure patterns in production.
Rule-based automation is the right tool for tasks that follow consistent rules with structured inputs. AI is the right tool for tasks involving unstructured data, variable inputs, natural language, or judgement calls. Many workflows need both — AI for the intake and classification, traditional automation for the execution. We assess this properly before recommending anything.
A focused AI integration — one use case, one model, one system — typically takes 4 to 8 weeks from scoping to production. A multi-agent system or full intelligent automation platform takes 10 to 20 weeks. The timeline depends heavily on data readiness and how well-defined the success criteria are before we start.
A focused LLM integration or single AI agent runs $8,000–$20,000. A full RAG system with custom knowledge infrastructure runs $15,000–$40,000. Multi-agent workflows and end-to-end intelligent automation platforms start at $30,000. We scope properly before committing to numbers.