In artificial intelligence, inference is the process where a trained AI model makes predictions, generates responses, or produces outputs based on new input it receives. Think of it like an employee applying their training and experience to handle actual work tasks. The learning phase is over, now they're doing the job.
When you type a question into ChatGPT and get an answer, that's inference. When an AI processes an invoice to extract the vendor name and total amount, that's inference. When a recommendation engine suggests products you might like, that's inference. It's the AI putting its training to practical use in real-time situations.
The term comes from the idea that the AI is inferring or figuring out the right response based on patterns it learned during training, similar to how you might infer the meaning of a new word from context clues. The AI isn't searching a database of pre-written answers. It's generating a response on the fly using the knowledge encoded in its model.
For businesses, inference is where AI creates actual value. Training an AI model is an expensive, one-time investment done by the AI company. Inference is what happens every single time you use the AI, and it's what you're typically paying for as a customer. When Zamp agents process your invoices, match transactions, or route items for approval, they're running inference constantly to understand your documents and make decisions based on your specific business rules.
Understanding inference helps explain AI costs, speed, and reliability. Inference is the work AI does for you.
Training is teaching the AI by showing it millions of examples so it learns patterns. Inference is the AI using what it learned to handle new situations. Think of it like the difference between going to medical school versus actually treating patients. Training happens once (or periodically) and is expensive and time-consuming. Inference happens constantly, every time someone uses the AI. For business users, you almost never think about training, that's done by companies like OpenAI or Anthropic. You only interact with the inference side, using an already-trained model to process your specific work.
Inference speed determines how fast your AI can actually work. If an AI takes 30 seconds to process one invoice, it becomes a bottleneck rather than an efficiency gain. Fast inference means your AI agent can handle high volumes without creating delays. For example, if you receive 500 invoices per day and inference takes 2 seconds per invoice, the AI finishes the batch in under 20 minutes. If inference took 30 seconds each, you'd be waiting over 4 hours. Speed also matters for user experience. When an employee asks an AI assistant for information, waiting 10 seconds feels sluggish, but 1-2 seconds feels responsive.
Inference costs depend on the model size and complexity. Larger, more capable AI models require more computing power to run, making each inference more expensive. Smaller, specialized models are cheaper per inference but might be less capable. Token usage also matters, processing a 50-page contract costs more than a one-page invoice because there's more text for the AI to analyze. This is why many AI tools charge by usage (per document processed, per API call, per message) rather than flat fees. The more inferences you run, the more it costs the provider in compute resources.
Yes, especially with creative or generative tasks. Because many AI models use some randomness in their inference process (controlled by a "temperature" setting), asking the same question twice can produce different responses. For structured tasks like extracting data from an invoice, this variation is usually minimal, the AI will consistently pull out the vendor name and amount. For open-ended tasks like writing an email or generating ideas, variation is higher and sometimes desirable. This is why business automation systems often use low temperature settings for predictable, consistent inference, while creative tools use higher settings for variety.
When an AI processes a business document, inference happens in stages. First, the AI reads the document and identifies what type it is (invoice, purchase order, receipt, contract). Then it infers where key information is located, even if documents have different layouts. For example, one vendor might put the invoice number in the top right corner, another in the center. The AI infers the meaning from context, labels, formatting, and position rather than looking in a fixed location. Finally, it extracts the data and infers how it should be structured, turning messy, unstructured document text into clean, structured data your systems can use.
In a well-designed business system, incorrect inferences get caught through verification steps before they cause problems. For example, if an AI incorrectly infers a vendor name from an invoice, a matching step against your approved vendor list would flag the discrepancy. If an AI misreads an invoice amount, an approval workflow requiring human sign-off above certain thresholds provides a safety net. The AI might also produce a confidence score with each inference, flagging low-confidence extractions for human review. The key is treating AI inference as a powerful first pass that benefits from verification checkpoints, not as infallible final decisions.
Zamp addresses this through structured processes with built-in verification. When agents run inference to extract data from documents or match transactions, they flag low-confidence items with a "Needs Attention" status for human review instead of proceeding with uncertain information. Activity logs record exactly what the agent inferred from each document, making it easy to audit decisions and catch errors. You can configure approval checkpoints at any step, ensuring humans verify critical inferences before they become final actions. The dashboard shows you items flagged for review, so you maintain oversight of where the AI is less certain.
Chatbots primarily use inference to generate conversational responses. You ask a question, the AI infers the best answer based on your question and conversation history. AI agents use inference to analyze documents, extract data, make decisions, and take actions. An accounts payable agent might run dozens of inferences per invoice, inferring the document type, extracting vendor details, matching to purchase orders, determining approval routing, and checking for anomalies. Where a chatbot's inference output is text for you to read, an agent's inference output is structured data and decisions that drive automated workflows. Both use the same underlying technology, but agents apply inference to operational tasks with measurable business outcomes.
Not automatically, no. Once a model is trained and deployed, its inference capability stays relatively fixed unless the model is retrained or updated. If you use the same AI model for a year, it will perform roughly the same in year one as in year twelve (assuming consistent inputs). However, some AI systems implement learning loops where human feedback on inference results feeds into model improvements over time. For example, if you consistently correct certain types of inference errors, that feedback might be used to fine-tune or retrain the model, improving future inference. But this requires intentional setup, it doesn't happen passively just from running inference.
For business automation, you typically control inference through configuration rather than changing the model itself. You can't modify how the underlying AI model works (that's managed by the AI provider), but you can control what inputs it receives, what instructions guide its inference, what thresholds trigger different actions, and what happens with its outputs. For example, you might configure an agent to only process invoices in specific formats, to flag items with certain characteristics for review, or to route approvals based on inference results. You define the business rules and decision logic that wraps around the AI's inference capabilities, shaping how it fits into your specific processes.
Zamp gives you control through the Knowledge Base, where you define agent instructions, approval rules, and decision logic in plain language. You're not changing how the AI model runs inference, but you're defining what the agent should do with those inference results. For example, you might specify that invoices over $5,000 require manager approval, or that mismatches between purchase orders and invoices should be flagged for review. You can adjust these rules anytime as your business needs change, and the agent applies them consistently to every inference it runs.