AI engineering, from prototype to production.

Define the task and the standard it must meet

An AI system needs a defined task, appropriate information and a way to assess its output. Those requirements guide the choice of model, integration and interface.

We help translate an AI idea into an engineering brief: who will use it, what it should do, which data it may access and what needs to remain under human control.

Where AI may fit

• Knowledge search: help staff find answers in approved documents, with references they can inspect.
• Assisted document workflows: classify, extract or summarize information for a person to review.
• Product features: bring AI capabilities into an existing application with appropriate access controls.
• Decision support: organize information and surface options while keeping accountable people in charge of consequential decisions.

We evaluate each use case against your data, quality requirements and operating costs.

Plan for evaluation and operation

The project brief should define a representative set of examples, expected behaviour and unacceptable failures. We discuss quality, latency and usage cost together, to determine whether the system meets both user needs and operating requirements.

For a production release, agree on monitoring, changes to prompts and models, incident handling and a fallback path. The required controls depend on the sensitivity and consequences of the use case.

Connect the feature to the rest of your system

AI rarely stands alone. It may need a usable interface, source-system permissions, an integration or a review queue. Our software development and automation work provides the surrounding application and workflow.

Start with one evaluated workflow

A pilot tests a defined workflow with a limited audience and approved or synthetic inputs. For a reporting workflow, that might mean drafting one report type for staff review, with no automated sending.

The pilot proposal defines the sample inputs, reviewer, expected output, unacceptable failures and the systems it may access. The agreed deliverables include the demonstration, evaluation record, observed usage costs and a recommendation on the next stage.

We agree scope, schedule and pricing after the data and integration review. Production operation, additional integrations and broader rollout are separate decisions. Bring a workflow owner and examples you have permission to use; start with our evaluation worksheet.

AI in our product work

CareOps combines project evidence with AI report drafting, a retained source record and staff approval before publication. Networkbase prepares organizer outreach proposals that become campaign drafts after review, with sending handled separately.

These products bring source data, model output and human decisions into a complete application workflow. Each case study explains the product’s current stage and engineering approach.

Working with your technical team

Bring the existing architecture, integration boundaries and evaluation requirements to the scoping discussion. We agree responsibilities for code review, environments, access and release approval with the people who already operate your product.

Our engineering approach explains how we address delivery, testing, ownership and handover. Our discovery guide shows how a brief, option comparison, acceptance checks and handover plan fit together. Share procurement or security requirements before the proposal so that any specialist work can be scoped explicitly.