- Services
SERVICES
- Platform
PLATFORM
- Solutions
AI AGENTS
LIFE SCIENCES AI
TECHNOLOGY
- Industries
INDUSTRIES
- Work
- About Us
CORPORATE
RESOURCES
- Contact us
Inference is the process of running a trained model to produce an output. It is what happens every time an enterprise AI system answers a question, classifies a document, or decides an agent’s next action.
The distinction from training is fundamental to how enterprise AI economics work. Training creates the model, is enormously computationally expensive, and happens once, performed by the model provider. Inference uses the model, is comparatively cheap per request, and happens continuously for as long as the system runs.
For an enterprise consuming foundation models, training cost is somebody else’s capital expenditure. Inference is the operating cost, and it is the entirety of what the organisation pays for ongoing AI capability.
Five factors, and enterprises control four of them.
Model size and type. Larger models cost more per token and respond more slowly. Reasoning models cost more still, because they allocate additional computation to deliberation.
Input length. Everything sent is processed, so retrieved context, conversation history, system instructions, and tool definitions all contribute on every request.
Output length. Generation is sequential, so response time scales with tokens produced. Instructing concise output is a genuine latency lever.
Number of calls per outcome. This is where agentic systems differ most from conversational ones. A single completed case may involve many inference calls across a reasoning loop, plus retries, so cost per case can be an order of magnitude above cost per call.
Caching. Repeated content, including stable system instructions and recurring queries, need not be recomputed where the platform supports caching. Support and service workloads are highly repetitive, which makes this unusually effective there.
The measure that matters is cost and latency per completed outcome, not per call. Optimising call level metrics while the number of calls per case grows is a common way to reduce the visible number while increasing the actual spend.
The architectural decision that most affects running cost is whether models can be matched to tasks, which is why the model agnostic position matters operationally rather than only strategically. Wizr AI describes selecting the stack per engagement across proprietary and open source models on performance, cost, and data sovereignty grounds, which allows cheaper models to be assigned to high volume steps without rebuilding the system around them.
Retrieval quality has a direct cost effect as well, since precise retrieval reduces the context sent on every request. Data engineering to integrate, optimise, and classify data for AI models sits within Enterprise AI Services, which is why data preparation should be assessed for its cost impact and not only its accuracy impact.
Ongoing optimisation falls to AI Ops in the same service line, described as continuous management and optimisation of data and models for ongoing results. Model reselection and configuration tuning belong there, once production traffic patterns are observed rather than estimated.
Inference cost management is the practice of controlling what an AI system costs to run in production, covering model selection, context size, caching, routing, and the architectural choices that determine how much computation each request consumes.
It matters because enterprise AI economics invert the usual software pattern. Traditional software has high build cost and near zero marginal cost per transaction. AI systems have moderate build cost and a real, recurring cost per request. A system that is inexpensive in pilot at a few hundred cases a week can become materially expensive at production volume, and the cost scales with success.
Agentic systems amplify this. A single agent case may involve several model calls across a reasoning loop, plus retrieval, plus retries, so cost per completed case can be many times the cost of a single response.
Five levers, ordered by how much they typically return.
Match model to task. Most enterprise steps are classification, extraction, and routing, which smaller and cheaper models handle reliably. Reserving the strongest model for genuinely reasoning heavy steps usually yields the largest single reduction.
Control context size. Cost scales with tokens processed. Retrieving five well chosen passages rather than fifty is cheaper and frequently more accurate, since precision improves when irrelevant material is excluded.
Cache aggressively. Repeated questions, stable system instructions, and common lookups do not need recomputation. Support and ITSM workloads are highly repetitive, which makes caching unusually effective there.
Bound the agent loop. Step limits, timeouts, and progress detection prevent the most expensive failure mode, which is an agent that keeps reasoning without converging.
Measure cost per resolved case, not per token. Token price is the wrong unit. A more expensive model that resolves a case in one pass can be cheaper than a weaker one that retries three times and escalates.
Cost control begins with the architectural decision. Because Wizr AI describes a model agnostic approach spanning proprietary and open source models, with the stack selected per engagement on performance, cost, and data sovereignty grounds, the cheaper model can be assigned to the high volume steps without rebuilding the surrounding system.
Ongoing cost management falls within the AI Ops capability of Enterprise AI Services, described as continuous management and optimisation of data and models for ongoing results, which is where model reselection and configuration tuning happen after go-live rather than only at design time.
Retrieval quality also has a direct cost effect, since precise retrieval reduces the context sent to the model. Data engineering to integrate, optimize, and classify data for AI models sits in the same service line, and is one reason data preparation should be assessed for its cost impact and not only its accuracy impact.
Intelligent document processing is the use of AI to extract, classify, validate, and structure information from documents, converting unstructured and semi-structured content into data that business systems can act on.
It differs from optical character recognition and from template based extraction in what it can handle. OCR converts an image to text without understanding it. Template extraction reads known fields from known layouts and fails on anything unconfigured. Modern document processing built on language models interprets a document it has not seen before, determining what type it is, locating the relevant information wherever it appears, and recognising when something expected is missing.
That last capability is what makes it usable in production. A system that extracts confidently from documents it misread creates downstream errors that are expensive to find. A system that flags low confidence extraction for review does not.
The value concentrates where documents are the input to a process rather than the output of one.
Accounts payable, where invoices in supplier specific layouts feed matching and payment.
Receivables, where remittance advices explain payments that arrive separately.
Regulatory and quality documentation, where content must be extracted, cross referenced, and validated against structural requirements, and where a missed element carries consequence beyond rework.
Technical and product data, where specifications, drawings, and supplier documents feed catalogues and master data.
Contracts and agreements, where obligations, dates, and terms must be located for downstream tracking.
The common pattern is that a person currently reads a document and types its contents into a system. That is the work being addressed, and the measure is the proportion of documents processed without human touch alongside the error rate on those.
Document processing appears across Wizr AI’s portfolio as a capability inside process automation rather than as a standalone product, which reflects how it delivers value.
In finance and accounting, the invoice matching agent extracts invoice data from an inbox and validates it against purchase orders in the ERP, and the reconciliation and cash application agents extract remittance and exchange rate information from mixed sources.
In automotive operations, multi-format document ingestion and part data extraction feeds parts catalogue creation, with automated part number normalisation and usage mapping producing catalogue output in standardised structures.
In regulated work, Wizr eCTD Studio drafts submission narrative sections from clinical study reports, preclinical summaries, and CMC data, and verifies every table, figure, and citation across all five submission modules, with human oversight retained throughout.
Invoice matching automation is the AI driven comparison of supplier invoices against purchase orders and, where applicable, goods receipts, validating that quantities, prices, and line items agree before the invoice is approved for payment.
The process is conceptually simple and operationally difficult. Invoices arrive as email attachments, PDFs, scans, and portal submissions, in layouts that differ by supplier and change without notice. Line item descriptions rarely match purchase order wording. Units differ. Partial deliveries, consolidated invoices, freight charges, and tax treatment all create legitimate discrepancies that are not errors.
This combination is precisely what defeats rules based automation and template based extraction. Both work on the invoices they were configured for and fail on the rest, which is why accounts payable teams in most enterprises still process a substantial exception queue manually.
The value is not in the clean cases. Two way and three way matching on well structured invoices was largely solved by existing AP automation.
The value is in three places those systems leave behind.
Format independent extraction. Reading an unfamiliar invoice layout correctly without a template configured for that supplier, which removes the onboarding cost that limits how many suppliers are automated.
Semantic line matching. Recognising that a supplier’s product description refers to the same item as the purchase order line, despite different wording, ordering, or granularity.
Reasoned exception handling. Distinguishing a genuine mismatch from a legitimate variance such as partial delivery or agreed price change, and routing only the former.
The metric that matters is touchless invoice rate, meaning the proportion processed without any human intervention, together with the error rate on those. Improvements in extraction accuracy that do not raise touchless rate have not changed the workload.
Wizr AI’s invoice matching agent runs as a defined workflow within the finance and accounting portfolio. It monitors a designated inbox, extracts invoice data, cross references it against purchase orders in the ERP, and validates line items, quantities, and pricing.
Where the comparison agrees, the agent updates the ERP and archives the validated invoice for audit. Where it does not, mismatches are flagged into exception handling and stakeholders are notified rather than the case being resolved on an assumption.
Two design points are worth noting. The workflow completes in the ERP rather than producing a recommendation, which is what converts effort saved into process closed. And the archival step exists because an automated approval that cannot be evidenced later is a control weakness regardless of accuracy, which is consistent with the platform’s audited access and secure data processing.
