AI solutions
Different business challenges need different approaches to AI. BillianceAI develops intelligent systems around the needs of each problem — rather than forcing every challenge into the same solution.
Start with the problem
Before choosing a technology, we look at what kind of problem it is. This is the first question we work through together.
| If the problem is… | A likely approach |
|---|---|
| Repetitive, multi-step work that follows clear rules | Automation and AI agents, with human review for exceptionsAI Agents & Automation |
| High volumes of routine conversations | Conversational and voice AI with structured hand-offConversational & Voice AI |
| Information locked in documents, emails or knowledge bases | Generative AI grounded in your own informationGenerative AI |
| Decisions that depend on what is likely to happen next | Machine learning trained on reliable historical dataMachine Learning & Predictive Intelligence |
| Visual tasks: checking, reading or counting | Computer vision for well-defined image tasksComputer Vision |
| Data spread across systems nobody can see clearly | Analytics foundations and unified reportingBusiness Intelligence & Analytics |
| Systems that don’t talk to each other | Integrations, APIs and reliable data pipelinesCRM & Enterprise Integrations |
| A problem no existing tool fits | Custom AI engineering, starting with a scoped prototypeCustom AI Engineering |
Capability areas
Each area below describes what the capability is and where it can help. Use cases are illustrative — scope and feasibility are always agreed for the specific problem.
Many operational tasks follow a pattern: gather information, make a routine decision, update a system, notify someone. AI agents can carry out these steps across existing tools, escalating to a person when a case falls outside clear rules.
Where it can help
Relevant to
Conversations are often where customers first reach a business. Conversational AI can answer routine questions, capture structured details and hand off to a person with context — so conversations are handled consistently. This is the area our flagship product, Felina AI, is built in.
Where it can help
Relevant to
Some problems need software designed specifically for them: a particular workflow, data set or set of constraints. Custom AI engineering covers scoping, architecture and building the application, models and integrations needed to solve that problem.
Where it can help
Relevant to
Where reliable historical data exists, machine learning can estimate future demand, flag unusual patterns or score risk. The value depends on data quality and how predictions feed into real decisions, so both are assessed early.
Where it can help
Relevant to
Generative AI can read, summarise, extract from and draft text at scale. It is most useful when grounded in an organisation’s own information, with clear review steps for anything that leaves the building.
Where it can help
Relevant to
Computer vision can recognise objects, read text in images and detect visual changes. It is suited to well-defined visual tasks where consistent image capture is possible.
Where it can help
Relevant to
Before AI can help, data usually needs to be gathered, cleaned and made understandable. Analytics work turns operational data into clear reporting — and is often the foundation for later predictive or generative work.
Where it can help
Relevant to
AI delivers value when its output lands in the right place — a CRM record, a ticket, a calendar or an ERP. Integration work connects intelligent systems to existing software through APIs, webhooks and data pipelines.
Where it can help
Relevant to
That is the right place to start. Describe the problem in your own words and we will help work out whether AI is the right tool — and which kind.