ACW Soft Solutions

AI product development and engineering

Build AI into a product flow your users can complete and your team can operate. ACW develops the interface, model connection, context handling, and application logic for the feature you need to release.

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Address the work around the model

  • Repeated integration work

    Define which connections and components can be reused across the intended features.

  • Unclear product behaviour

    Specify what users can edit, approve, retry, or complete without AI.

  • Growing operating costs

    Assess context size, response time, and request volume alongside output quality.

Engineer the complete user flow

  • Decision logic

    Separate model suggestions from application rules. Define which outputs can affect product state and which require confirmation.

  • Context and memory

    Specify the information needed for each request, access permissions, and what persists between sessions. Agree retention and deletion requirements before selecting storage and providers.

  • AI workflows

    Connect input, generation, validation, and the next user action. Handle missing context, unsuitable output, and interrupted requests in the interface.

  • Control and fallback

    Define limits, error messages, and alternative paths. Check permissions and action arguments in the application where changes are executed.

What the product team receives

  • A user flow and architecture for the selected feature.

  • Implemented interface and system connections within scope.

  • Context, access, and output-handling rules.

  • Evaluation examples, findings, and known limitations.

  • Release and handover documentation for the included environment.

Build one feature before extending the architecture

  1. 01.

    Define the experience

    Agree the user’s task, acceptable output, and behaviour when AI is unavailable.

  2. 02.

    Compare approaches

    Review model options against representative inputs, latency, cost, and data requirements. Identify reusable components without assuming every future feature needs the same architecture.

  3. 03.

    Implement the flow

    Connect the interface, backend, and model. Add the validation and review steps the task requires.

  4. 04.

    Evaluate the release

    Test ordinary requests, missing data, unsuitable outputs, and provider failures. Record what is ready and what remains outside the release.

Make the release criteria explicit

Define quality examples, response-time expectations, request limits, and a cost measure such as cost per completed task. Check whether users can recover from failure and whether an unauthorised request can reach restricted data or actions. Version changes need a repeatable way to check these behaviours again.

This example describes a possible scope, not an ACW client result.

Illustrative workflow: a document review feature

A user submits an authorised document and receives a structured draft summary. The interface shows the source passages used and lets the user edit the draft. Unsupported sections are flagged for review. The saved result remains a deliberate user action, with retention defined for the document and its generated output.

Questions before building

Do we need to rebuild the product?

That depends on existing interfaces, APIs, and access controls. Assess those constraints before deciding whether to extend or replace a component.

Does AI-native mean a custom model?

No. This service concerns the product around AI. Model training is a separate requirement when the task justifies it.

Can we change model providers later?

The architecture can account for that requirement. Switching still needs evaluation because output, capabilities, and costs differ.

Explore related services

Bring the user flow you want to build

Share the task, expected output, existing product, and constraints on data or operating cost.

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