IAH Automation
What we build
Six capability areas, drawn on individually or combined, depending on what your workflow actually needs.
AI agents and process automation
Agents that plan, use tools, and act, not scripted flows dressed up as AI.
This is typically where issues show up: staff repeating the same manual steps between systems, leads going cold because no one follows up fast enough, or a process that only scales by hiring more people.
- AI agent development and orchestration
- Sales outreach and lead research agents
- Inbound reception and appointment booking
- Robotic process automation (RPA)
- Google Workspace, CRM, WhatsApp integrations
These are examples of what's possible in this area, not the full list.
Generative AI and RAG
Assistants and chatbots grounded in your own documents and data.
This is typically where issues show up: the same questions being answered from scratch every time, knowledge locked away in PDFs and wikis nobody searches, or a website with no way to actually have a conversation with a visitor.
- Retrieval-augmented generation (RAG) agents
- LLM-powered virtual assistants
- Model fine-tuning (LoRA, Q-LoRA)
- Prompt engineering
- Chatbot integration into existing sites and apps
These are examples of what's possible in this area, not the full list.
Computer vision and NLP
Understanding documents, images, and speech at scale.
This is typically where issues show up: piles of scanned documents or images needing manual reading, calls and messages nobody has time to review, or unstructured text that never gets sorted or tagged.
- Object detection and image classification
- OCR and document data extraction
- Sentiment analysis and text classification
- Named entity recognition
- Speech recognition and audio classification
These are examples of what's possible in this area, not the full list.
Data science and predictive analytics
Turning historical data into forecasts and early warnings.
This is typically where issues show up: decisions made on gut feel instead of data, customers churning without warning, or fraud and errors only caught after the damage is done.
- Sales and demand forecasting
- Fraud and anomaly detection
- Customer retention analytics
- Data pipeline engineering
- Dashboarding and visualization
These are examples of what's possible in this area, not the full list.
MLOps and cloud infrastructure
Making sure what works in a demo keeps working in production.
This is typically where issues show up: a model that worked in a notebook but breaks in production, no visibility into whether a live system is still accurate, or manual redeployment every time something changes.
- Model deployment and serving
- CI/CD for machine learning
- Monitoring and observability
- Cloud architecture and migration
- Containerization (Docker, Kubernetes)
These are examples of what's possible in this area, not the full list.
AI governance and security
Compliance and trust built into the system, not bolted on after.
This is typically where issues show up: a system making decisions nobody can explain, uncertainty about compliance with new AI regulation, or no clear picture of how secure an AI system actually is.
- Bias detection and fairness auditing
- Explainability and interpretability
- Regulatory compliance (EU AI Act, GDPR)
- Adversarial robustness testing
- Secure architecture and threat monitoring
These are examples of what's possible in this area, not the full list.
In practice, a single problem rarely fits inside one box above. An inbound support call, for instance, can involve speech recognition, NLP, agent orchestration, and CRM integration all at once. That's exactly why every engagement starts with a discovery call rather than a menu order — the goal is to map your actual issue across whichever of these areas it touches, not to sell you a category.
Start with a free discovery call
Tell me what's slow, manual, or error-prone in your business. I'll map it to the right combination of the above, or tell you honestly if AI isn't the right fix.
Get a free consultation