Production AI with evidence behind each change.
An AI result has to fit the work around it: the inputs it receives, the decision it supports and what happens when it is wrong. We build model orchestration, evaluation and controls around a defined task, then measure the result against how that task runs today.
Where it fits
This work fits a recurring task where a model may help with judgement, classification or extraction and the result can be evaluated. There needs to be a clear owner, representative inputs and an agreed way to decide whether an output is acceptable. Those foundations come before choosing a model.
Example workflow
Consider a workflow that uses a model to classify an incoming item before another service handles it. The workflow supplies the permitted context, asks for the agreed output format and checks the response. Unsupported or uncertain results go to review. Evaluation measures incorrect routing and the amount of manual work that remains, alongside latency and inference cost.
Build the evaluation first
We define representative cases and acceptance criteria with the people who know the task. The evaluation should include ordinary inputs and the cases where an error matters most. It provides a repeatable way to compare the current process, candidate approaches and later changes to prompts, models or context.
Set the operating boundaries
Model output is one step in a controlled workflow. We agree what it may influence, which checks run before the next action, and when a person must decide. Logging, cost ceilings and failure handling make the behaviour visible. We widen the scope only where the measured quality and cost support it.
What the delivery includes
We agree the scope and acceptance criteria around the selected workflow. The delivery covers the working service and what your team needs to take it over.
- A defined task, baseline and acceptance criteria, with an evaluation harness that can be rerun after changes.
- Model orchestration and integration code for the agreed inputs, context and output format.
- Output validation, review rules and failure handling matched to the actions the workflow can take.
- Measurements of quality, latency and inference cost, with the agreed cost controls and audit trail.
- Code, infrastructure as code and a handover covering configuration, evaluation and the limits found during the work.
What we need from you
- A specific task and examples of acceptable, incorrect and ambiguous outcomes.
- Inputs that may be used for evaluation, plus a domain owner who can review results and resolve disputed cases.
- The existing baseline, expected volume and acceptable latency, cost and review workload.
- Decisions on allowed data use, model access, residency and who operates the workflow after delivery.
Constraints to settle before rollout
- A model can produce a plausible answer that is wrong. Validation and review must match the consequence of the next action.
- Changes in models, prompts or input data require renewed evaluation against the agreed cases.
- Data access, model availability and usage costs affect what can run in the chosen account and region.
Ownership and handover
The system runs in your accounts, with the code and infrastructure as code handed over to your team. We build the service; your team owns daily operation. Read our delivery methodology and approach to accounts, access and data residency for the wider delivery boundaries.
Related work
- Document processing — Turn documents into validated data with extraction, classification and review of uncertain fields. Landvex builds traceable processing workflows.
- System integration — Connect existing platforms with reliable handoffs, validation, retries and audit trails. Landvex builds integration services in your accounts.
Bring one concrete workflow
Describe the task, how you judge a correct result and what happens when the result is wrong. Include the current workload and any data or cost constraints.
Discuss your workflow