Software built
around your business.

Working software

The interface, logic and system connections needed for the agreed task.

A quality record

Data preparation decisions, repeatable behavior checks and the results used to assess release readiness.

An operating handover

Code, configuration and documentation, with access, hosting, maintenance and support responsibilities agreed.

At Anthropic, agents worked six times as many hours as their human handlers by August 2026. An agent is an AI assistant allowed to use connected tools to carry out tasks. That much output only helps a business when the software around the model fits the work: your information, the connections to your systems, the access rules and a clear point where a person checks the result.

The best way to get real efficiency gains is you have to actually reengineer the process to take advantage of what agents are actually good at and can do differently and faster than people.

So we agree the process first, then build for it. We define acceptable behavior with your team and check it throughout development: the information the system relies on, the actions it may take and what it does when it cannot finish a task reliably.

1. Define what good means

   Your team brings the requirements, source systems and records of actual work. We agree the expected results, permitted actions and acceptance criteria before building.

2. Build and review

   We develop working versions and review them with your team. You try the task, inspect the output and help resolve the business decisions that emerge during development.

3. Release and maintain

   We review the checks before release and prepare the operating handover. Ongoing maintenance has an agreed scope, including who responds to failures and approves changes.

The checks become part of the system, so quality can be reassessed when its data, instructions or software change.

Data preparation and access

We identify the authoritative sources, organize the agreed data and check for missing, conflicting or outdated records. We define how information is retrieved and refreshed, while preserving who is allowed to see it. Data owners help resolve ambiguities before those ambiguities reach users.

Evaluations

An evaluation is a repeatable check of whether the system behaves as intended. We assemble a test set from actual tasks, with expected results or review criteria agreed by your team. It covers output accuracy, supporting sources, permissions and incomplete inputs. Disagreements help identify what needs changing.

Quality after release

For systems we maintain, we rerun the checks when models, instructions, data or code change. This catches updates that weaken earlier results. We monitor failures, response times and usage costs. When your team operates the system, we hand over these checks with the recovery procedures.