How we work · wGrow Since 2008

Understand the business.
Build the capability.

AI-native operations connect people, information and software. We shape the engagement around how your business works, then develop the systems and skills that make the change usable.

Start with the operating context

A business view.
A practical place to begin.

We look across the functions involved: where work arrives, who makes decisions, which systems hold the information and how one team's output reaches another. The scope may include application development, integration, Agentic Crews, staff learning and ongoing operation.

That wider view helps us choose a useful starting point. An initial workflow gives the team something concrete to test, while the design considers shared information, permissions and responsibilities that adjacent work may also need.

Some needs are best served by an existing product, a conventional integration or a clearer process. We choose the engineering around the task.

From investigation to everyday use

Five connected parts of delivery.

The delivery map

One connected engagement.
Learning travels both ways.

Each stage informs the next. What people discover in use can change the design.

  1. 01

    Understand

    Learn the business

    Priorities, existing work and constraints.

  2. 02

    Design

    Define the working model

    Roles, information, permissions and acceptance.

  3. 03

    Engineer

    Build and evaluate

    Connected software, workflows and test cases.

  4. 04

    Adopt

    Prepare people to use it

    Practice, release review and operating instructions.

  5. 05

    Continue

    Operate and improve

    Monitoring, ownership and the next useful change.

Feedback loopReview failures, user questions and operating evidence. Revisit priorities and design.

People own the release decision

Agree representative tests, permitted actions and operational responsibility before everyday use.

Illustrative delivery approach. Outputs, review points and responsibilities are agreed for the engagement.

01 · Understand

Map the work and its constraints.

Work with sponsors, process owners and users to understand priorities, exceptions, existing software and data access. Establish a baseline for the work you want to improve.

Outputs to agree: a problem map, priorities, access findings and an implementation recommendation.

02 · Design

Define the working model.

Describe how people, information and software will interact. Decide what AI may do, where review is required, how systems connect and what a good result looks like.

Outputs to agree: target workflows, integration scope, responsibilities, evaluation cases and acceptance criteria.

03 · Engineer

Build and review working increments.

Develop the agreed applications, integrations and AI steps using our daily Codex and Claude desktop/CLI practice. Use Agentic Crews when specialist roles and handoffs help; define orchestration, review responsibility and failure handling. Review progress with domain users, including difficult cases and failures.

Outputs to agree: working software, review interfaces, tested integrations and documented limitations.

04 · Adopt

Prepare people and release deliberately.

Connect practical learning to the workflow. Test representative work, check permissions and practise exceptions. Agree release authority, recovery and the owner of everyday operation.

Outputs to agree: an accepted release, operating instructions and role-specific demonstrations.

05 · Continue

Operate, improve and extend.

Review quality, cost, usage and staff effort. Test changes to models, instructions or integrations. Use evidence to choose further workflows and reuse foundations where they fit.

Outputs to agree: an operations plan, improvement priorities and a support or handover arrangement.

Engineering the working environment

Give the agents context.
Give the system boundaries.

Working with Codex, Claude and coordinated agents involves more than writing a prompt. We design the instructions, information and permissions around the task, then evaluate the complete workflow.

  • Orchestration and handoffs. Give each role a defined task, expected output and review point. Specify how failed or incomplete work returns to a person.
  • Scoped memory. Separate working notes from reusable project knowledge. Agree who can change persistent instructions and how incorrect or outdated information is corrected.
  • Retrieval, context and token budgets. Select relevant information, control how much is loaded and test the effects on task quality, latency and cost.
  • Least-privilege access. Scope file and system access, tool permissions, secrets handling and permitted outbound connections. Review what information each service receives.
  • Evaluation before expansion. Check representative work, exception handling and regression cases as instructions, tools or models change.

Where data handling or deployment requirements favour a local LLM, we assess an appropriate self-hosted model and hardware, including NVIDIA DGX Spark. The local model, connected tools and any cloud services have separate data flows to agree; hardware alone does not establish a private environment.

Explore the tools and private AI practice

Clear responsibilities

Your expertise stays central.

The client provides an accountable sponsor, process owners, representative users, approved information and access. Your team defines business rules, reviews domain judgments and makes the decisions reserved for it.

wGrow is responsible for the engineering and other deliverables agreed in the scope. Together, we define acceptance, change handling and who maintains each component after release. A company without developers still needs a named technical owner or provider.

Working arrangements, fees, third-party charges and ongoing support are scoped explicitly. Ownership of custom code and documentation is distinguished from platform components and third-party licences.

Bring the business context.

Tell us what your teams do, which systems they depend on and where work gets stuck. We can discuss the priorities and define a useful next step.

Discuss your business

Explore the company cases · Training and adoption

Read the practical workflow roadmap · AI FDE overview

wGrow · Since 2008

Bring the business.
Let's shape what comes next.

Tell us about your people, systems and operating priorities. We can discuss the right starting point and a scoped path towards AI-native working.

Discuss your business

Scope, fees and responsibilities agreed
before diagnostic or implementation work.