These are the AI capabilities we build into custom solutions. Each one has been delivered in production for a client — not a capability list we aspire to.
Looking for ready-to-deploy products instead? Explore Veehive.ai.
Agents and orchestration
Vertical AI agents
Agents built for one function and one industry — a booking agent that understands inventory and fare rules, a claims agent that knows your policy set, a procurement agent that respects your approval matrix. A narrow agent that knows the domain beats a general assistant that does not.
In production: Agentic WhatsApp booking assistant for Rayna Tours, handling 50,000+ conversations a month across 50+ languages.
Custom harness building
The company-controlled operating layer around Claude, GPT or Gemini: instructions, workflows, memory, model routing, evaluation and approval gates. Own the harness and the model underneath becomes a swappable component rather than a dependency.
In production: See our note on owning the harness for the architecture.
AI automation
End-to-end process automation where the decision points need judgement rather than a rule — triage, routing, exception handling and the escalations that keep a human in the loop where it matters.
Conversational AI and chatbots
Multilingual assistants across WhatsApp, web and email that complete transactions rather than deflecting to a form. Grounded in your own content so answers reflect your policies, not a generic model's guess.
In production: 50+ languages in production for travel and hospitality clients.
Computer vision
Face recognition
Identity verification and access control at venue scale, with the throughput, accuracy and privacy constraints designed in from the start rather than retrofitted.
In production: Deployed across 80+ venues for Phoenix Tourism.
Object detection and video AI
Detecting, counting and tracking objects in images and video streams — safety compliance on site, progress verification in construction, stock and shelf monitoring in retail, and anomaly detection where a human cannot watch every frame.
Knowledge and data
RAG pipeline development
Retrieval grounded in your own documents, policies and records, refreshed as the sources change — so the system answers from current company truth instead of what a model memorised during training.
Document processing and OCR
Turning contracts, invoices, forms and site paperwork into structured, queryable records, including the messy scanned and handwritten inputs that defeat template-based extraction.
In production: Covenant monitoring across a large loan portfolio for a major US bank.
Data lake and pipeline setup
The ingestion, storage and transformation layer AI depends on. Unglamorous, and the reason most AI programmes stall before they start.
AI data analysis and intelligence
Natural-language analysis over your own operational data, with citations back to the source so a number can be traced rather than trusted.
In production: AI reconciliation engine for ARKA, replacing manual matching with an auditable pipeline.
Models and integration
SLM configuration and fine-tuning
Smaller, specialised models tuned on your data — often cheaper, faster and more private than calling a frontier model for every task. Deployable on-premise or air-gapped where regulation requires it.
AI integration and API development
Connecting AI to the CRM, ERP, warehouse and workflow tools you already run — increasingly through MCP, so a capability is built once and reused across models.
Where these have shipped
Selected work delivered by Veehive Labs. This library is shared with Veehive.ai — one company, one set of case studies.
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