Production-Ready AI
Build digital products powered by AI.
We design, develop, and integrate AI into websites, apps, and workflows. LLM APIs, RAG knowledge bases, custom AI features, and full AI-native product builds — engineered for production, not just demos.
// what we build
Every layer of AI built properly.
From a single chatbot feature to a full AI-native product — we scope, architect, build, and maintain it.
AI Chatbots and Assistants
Custom AI assistants trained on your content and connected to your backend. Not a widget – a real product feature that knows your data, stays within your brand, and escalates to humans when confidence is low.
RAG Pipelines and Knowledge Bases
Upload your docs, embed them, retrieve on query. Your AI answers from what your business actually knows – not hallucinated training data. Built with Pinecone, Weaviate, or pgvector depending on your stack.
AI-Native Feature Development
Full-stack AI features built from scratch – not API calls bolted onto existing UI. We architect the prompt layer, data layer, fallback logic, and frontend together so the feature works as a coherent product.
Semantic Search
Vector search over your products, docs, or content. Users find what they mean, not just what they type. Dramatically outperforms keyword search for complex catalogues, support docs, or large content libraries.
Workflow and Process Automation
Connect AI to your CRM, email, Slack, or internal tools. Automate repetitive decision-making: classify support tickets, generate reply drafts, summarise meeting notes, route leads. No more copy-paste between tools.
Content Generation Pipelines
AI-generated product descriptions, review responses, blog drafts, and email copy – integrated into your existing CMS or workflow, not a separate tool to log into. Scales to thousands of items without an extra headcount.
AI App and Web Development
AI-first products built from scratch – scheduling assistants, diagnostic tools, valuation engines, recommendation systems. We own the whole stack: UI, API, data layer, AI layer, and deployment.
Fine-tuning and Custom Models
When a general model is 80% accurate and you need 98%, fine-tuning is the answer. We fine-tune GPT or open-source models on your domain data – support tickets, medical terminology, legal language, product specs.
// AI stack we build with
OpenAI GPT-4o
LLM
Anthropic Claude
LLM
Google Gemini
LLM
Mistral
Open model
LangChain
Orchestration
Pinecone
Vector DB
pgvector
Vector DB
Vercel AI SDK
Streaming
Hugging Face
Fine-tuning
Ollama
Self-hosted
// free offer · no obligation
Free AI scoping call
Share your tech stack and the problem you want to solve. We will outline which AI approach makes the most sense, estimate the build scope and ROI, and tell you honestly whether it is worth building — in 30 minutes.
Which model and architecture fits your use case
Realistic build timeline and cost estimate
Estimated hours saved per month
Data privacy architecture recommendation
ROI estimate before you commit a budget
AI Scoping Request
Written breakdown within 24 hours. No pitch call required.
// how it works
From idea to production in weeks.
Discovery
Map the exact workflow or problem to solve. Define inputs, outputs, acceptable accuracy, and what happens when the AI is wrong. No vague “add AI” briefs.
Architecture
Choose the right model and approach – pure LLM, RAG, fine-tuned, or agent. We prototype with 2-3 models before picking one. No vendor lock-in without good reason.
Prototype
Working proof of concept in 1-2 weeks. You interact with it before full build. If the output is not right, we course-correct before spending the full budget.
Build
Map the exact workflow or problem to solve. Define inputs, outputs, acceptable accuracy, and what happens when the AI is wrong. No vague “add AI” briefs.
Test
Accuracy benchmarking, edge cases, prompt engineering, hallucination guards. We set a target accuracy in Discovery and we do not ship until we hit it.
Monitor
Usage and cost dashboard. Real-time alerts for anomalies. Ongoing prompt tuning available. AI features can be covered under a Care Plan from month two.
Pricing
Transparent, fixed-scope pricing.
AI Feature
One production-ready AI feature integrated into your existing product.
Single AI feature (chatbot or automation)
LLM API integration + error handling
Prompt engineering and accuracy testing
Cost monitoring dashboard
30-day post-launch support
Get started
AI Platform
Full AI integration: RAG knowledge base + chatbot or assistant + workflow automation.
RAG pipeline (your data, your knowledge base)
AI chatbot or assistant feature
Workflow automation (1-2 processes)
Admin dashboard and monitoring
Prompt tuning sessions (3 included)
60-day support
Most popular
Most popular
AI-Native Build
Full AI-first product: multi-model architecture, custom data pipelines, and a complete user-facing application.
Multi-model system design
Custom fine-tuning where needed
Complex RAG and data pipelines
Complete AI-first product UI
Team training and documentation
90-day support
Start project
// FAQ
Questions we hear every week.
Which AI models do you work with?
OpenAI GPT-4o and o1, Anthropic Claude 3.5 Sonnet, Google Gemini 1.5 Pro, and open-source models via Ollama or Hugging Face. We prototype with multiple models before committing - the best model depends on your task, latency requirements, and budget.
What is a RAG pipeline and do I need one?
RAG (Retrieval-Augmented Generation) lets the AI answer questions using your own data - your docs, product catalogue, knowledge base, or CRM - instead of just its training data. You need one when accuracy and up-to-date answers matter. Most business AI features benefit from RAG.
Can you add AI to an existing WordPress site or web app?
Yes. We integrate AI as a custom WordPress plugin with a knowledge base you control, conversation logging, escalation to human support, and a simple admin UI. We have done this for booking platforms, WooCommerce stores, and service businesses.
How do you handle data privacy?
We design prompts and data flows to minimise sensitive data sent to third-party APIs. PII gets redacted or anonymised before hitting external models. For high-sensitivity sectors (healthcare, legal, finance), we can route through self-hosted models or providers with signed DPAs.
What is the ongoing cost of running AI features?
OpenAI API costs roughly $0.002-0.01 per 1,000 tokens at current pricing. We build cost monitoring into every integration so you see exact spend in real time with hard limits. Most clients find AI costs are a small fraction of the time saved - typically 50:1 ROI or better within 90 days.
What if the AI gives wrong answers?
We build fallback logic, confidence thresholds, and human-in-the-loop review for high-stakes outputs. Lower-confidence responses route to a human queue. High-stakes actions require confirmation before executing. We run accuracy testing and prompt engineering iterations before anything goes live.
Can you connect AI to my CRM or database?
Yes. We connect AI features to your existing data via APIs, webhooks, or direct DB queries. Your AI knows what your database knows. We have integrated AI into WordPress, WooCommerce, Laravel, Salesforce, HubSpot, and several proprietary CRMs.
How long does an AI integration take?
A single AI feature (chatbot or content automation) takes 2-4 weeks from spec to production. A full AI platform with RAG, automation, and monitoring takes 6-10 weeks. We always build a working prototype in weeks 1-2 so you see it before the full budget is committed.
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Let's build something extraordinary.
Whether you’re launching a startup, scaling a platform, redesigning a site or developing custom software — ConicPlex is ready to help.