AI that ships to production. Not just a demo.
We build AI agents, voice AI, RAG systems and custom ML models that run in your production environment under real workload — not proofs of concept that fall apart after the demo.
What's included
AI Agents
Autonomous agents that qualify leads, answer support, route tickets, process documents and run multi-step workflows — connected to your CRM, helpdesk and communication tools.
Voice AI
Phone agents that answer calls, book appointments, qualify callers and handle follow-ups in a natural human-sounding voice — 24/7, on your existing phone number.
LLM Applications
RAG systems on your private data, LLM-powered search, document summarisation, contract analysis and intelligent Q&A — built on GPT-4, Claude or open-source models.
Custom ML Models
Forecasting, classification, anomaly detection, computer vision and NLP models trained on your proprietary data and deployed to production with full monitoring.
Fine-Tuning & Prompting
Fine-tune foundation models on your domain vocabulary. Structured prompt engineering and evaluation frameworks for reliable, consistent outputs at scale.
MLOps & Monitoring
Model deployment, versioning, A/B testing, drift detection and automated retraining pipelines — so your AI keeps performing after launch.
Common questions
What AI models does TrueCodeAI use?+
We are model-agnostic and choose the best fit per project: OpenAI GPT-4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Whisper for voice transcription, and specialised open-source models where appropriate. We also fine-tune models on client data.
How long does an AI agent take to build?+
A focused AI agent — a sales qualification agent or a phone booking agent — typically takes 2–4 weeks from kickoff to production deployment, including integrations with your existing tools.
Can TrueCodeAI train a model on my private data?+
Yes. We handle the full ML pipeline: data cleaning and preparation, model selection, training, evaluation, deployment and ongoing monitoring. We have worked with structured databases, PDF document sets, call transcripts and image libraries.
What is the difference between an AI agent and traditional automation?+
Traditional automation follows fixed rules: if X then Y. AI agents understand natural language, handle ambiguity, reason over context and make decisions — they handle the edge cases that rule-based systems cannot.
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Scope your AI system
Tell us what you need — we scope it for free and reply within 24 hours with a plan and fixed price.
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