AI and Machine Learning Solutions.

What is applied AI and machine learning?

Applied AI is the use of machine learning and language models inside real business workflows: reading documents, routing requests, forecasting demand, flagging anomalies, answering staff questions from internal knowledge. Neovantra focuses on the category where results are measurable — systems connected to your data, owned by a named team, with evaluation and monitoring that prove whether they still work six months later.

We deliberately start from the decision the business wants to improve, not from a model choice. If a rules engine or a process change solves the problem more reliably, we will say so.

Where AI earns its place

Typical candidates are tasks that are high-volume, judgement-light and currently expensive because a person reads, classifies or summarises: invoice and claims intake, contract review support, support-ticket triage, catalogue enrichment, demand forecasting, document extraction into a system of record, and internal search over policy and product documentation. Each has a measurable baseline — handling time, error rate, deflection rate — that a pilot must improve against.

We decline use cases where error cost is high and review is unrealistic, or where the training data is too thin to evaluate. Saying no early is cheaper than explaining drift later.

How a first engagement runs

Weeks one and two are a use-case assessment: baseline metrics, data availability, integration surface, risk classification and a written recommendation including the option not to proceed. If it proceeds, we build a narrow but real slice — one workflow, one document type, one team — with an evaluation set built from your historical examples before any model is chosen.

The pilot runs alongside the existing process so results are comparable. Success criteria are agreed in writing beforehand: what accuracy, what latency, what cost per document, and how many humans review the output.

Model choices and architecture

For language tasks we usually build on hosted large language model APIs — from providers such as Anthropic, OpenAI or Azure OpenAI — or open-weight models in your own environment when data residency forbids external calls. Retrieval pipelines sit on your existing stores with access controls inherited from the source system, so a user only ever retrieves what they are already permitted to see. Classical machine learning covers forecasting, scoring and anomaly detection where interpretability and cost matter more than language fluency.

Every deployment includes prompt and version management, structured output validation, guardrails on tool use, and a fallback path when the model is unavailable or below threshold.

Evaluation, monitoring and responsible use

Evaluation is built with the client: labelled examples, edge cases, and regression suites that run whenever a prompt, model version or retrieval index changes. In production we track quality signals — acceptance rate, correction rate, override reasons — alongside cost and latency, and retrain or re-tune on a schedule rather than by accident. Data handling is agreed up front: what may be sent to an external model, retention settings, redaction of personal data and audit logging.

What you receive

A working slice in your own environment, an evaluation report against the agreed baseline, the pipeline and prompt code in your repositories, data handling documentation, a monitoring dashboard, cost-per-run measurements at expected volume, and a scale-up plan with the next increments estimated.

Timelines and commercials

Use-case assessment takes one to two weeks; a production-quality pilot for a single workflow typically runs eight to twelve weeks including evaluation. Pilots are usually fixed-price against written acceptance criteria, with scale-up on a dedicated team or time and materials as the workflow expands. Inference cost is billed to your own cloud or model accounts so spend stays visible.