AI Solutions
AI that earns its keepin your operation.
AI works where a decision is too variable for a rule and too frequent for a person. We identify those moments in your operation, build the feature that handles them, and measure the result — no prototypes that never ship.
What we provide
AI integrated into the workflow — not as a prototype, but as a running part of the operation.
- Azure OpenAI (GPT-4o) integration for document processing and Q&A
- Retrieval-augmented generation (RAG) on private data
- AI agent design and multi-step orchestration
- Intelligent document classification and data extraction
- Semantic search with Azure AI Search
- Predictive analytics and anomaly detection
- AI-powered chatbots and conversational interfaces
- LLM output evaluation and hallucination mitigation
- AI pipeline monitoring, cost tracking and rate-limit handling
Why Theerrv
Integrated, not bolted on
We build AI into the workflow — the trigger, the action, the audit trail and the human escalation path — rather than delivering a model that lives in a separate tool.
Azure AI and OpenAI
Azure OpenAI Service, Azure AI Services and the broader Azure ML platform are where we work. Your data stays in your Azure subscription — not in a third-party AI company's training set.
Measured before shipped
Every AI feature ships with a baseline, an evaluation set and an agreed metric for what 'working' means. We do not ship models that have not been evaluated against real examples.
Our approach
Discovery → Architecture → Development → Testing → Deployment → Support
Use-case scoping
We identify which decisions in your operation are good AI candidates: high volume, variable inputs, measurable outputs, acceptable error rate. We also identify what AI will not help with.
Data assessment
We audit the data available — volume, quality, format, access constraints — and assess whether there is enough to evaluate a model against.
Architecture and retrieval design
Data pipeline, retrieval strategy (RAG or fine-tuning), model selection, prompt design and evaluation metrics agreed before development begins.
Build and evaluate
Feature developed against the evaluation set. Accuracy, edge-case behaviour and failure modes reviewed with you before any production exposure.
Integration
AI feature wired into the existing application or workflow — trigger, response handling, confidence thresholds and human escalation paths all implemented.
Monitor and improve
Logging, confidence tracking and a feedback loop built in so the model improves on real operational data over time.
Technologies
The stack we use to deliver ai solutions — chosen for longevity and maintainability, not trend.
- Azure OpenAI Service
- Azure AI Search
- Semantic Kernel
- LangChain (Python)
- Azure AI Document Intelligence
- Python
- Azure Machine Learning
- Azure Cosmos DB
- Azure Functions
Industries & use cases
Professional and Legal Services
- Contract review and clause extraction
- Document classification and routing
- Research Q&A over large document libraries
Financial Services
- Credit risk signal detection in unstructured data
- Fraud pattern analysis in transaction streams
- Regulatory document summarisation
Operations and Logistics
- Email and inquiry triage and routing
- Exception detection in fulfilment data
- Predictive maintenance signal extraction
Healthcare
- Clinical note summarisation and coding assistance
- Patient query handling with appropriate escalation
- Medical document extraction and structuring
Common questions
Is our data safe when we use Azure OpenAI?
Yes. Azure OpenAI processes requests within your Azure subscription — the data is not used to train OpenAI's foundation models and does not leave the Azure region you deploy to. We configure private endpoints so the model is not accessible over the public internet. You retain full control over what data is sent and can audit every request through Azure Monitor.
What is retrieval-augmented generation (RAG) and when do we need it?
RAG is an architecture where the AI model retrieves relevant information from your own documents before generating a response — rather than relying solely on its training data. You need it when the answer depends on information the model was not trained on: internal policies, product specifications, historical records, or any knowledge that changes over time. Without RAG, the model can only answer from general knowledge, which quickly leads to hallucinated or outdated answers.
How do you measure whether an AI feature is working?
Before building, we agree on a definition of 'working': an accuracy threshold, a precision/recall target, or a time-saved metric against the manual baseline. We construct an evaluation set from real examples — including difficult cases — and measure the model against it. If the model does not meet the threshold, we do not ship it. We also monitor production performance and alert when accuracy falls below the agreed level.
Can AI replace our customer support team?
Partially, in specific ways. AI handles well-defined, high-volume queries accurately — order status, policy questions, document retrieval, appointment scheduling. It handles poorly-defined, emotionally complex or novel situations badly. We recommend building AI as a first-response layer with clear escalation to a human agent, not as a replacement. The businesses that get this right save their staff time while improving response speed for customers.
Related services
Start here
Tell us what theoperation is costing you.
A first conversation is a conversation, not a pitch. Describe how the work runs today and we will tell you plainly whether software is the right answer — and what it would take.
Or reach us directly at info@theerrv.com