Use-case definition
Choose a task with representative inputs and reviewable outputs. Compare ordinary rules or search with a model-based approach.
Generative AI developmentBuilt around your workflow.
A useful generative AI feature has a bounded job and a way to evaluate its output. We help distinguish drafting, retrieval and action-taking workflows, then define the source and review boundaries.
Prepare a project briefThe work, connected
- 1Approved input
- 2Model & evidence
- 3Reviewed output
Define what needs
to work together.
Use these areas to define the first release, its interfaces and the responsibilities that continue after launch. Generative AI applications grounded in approved sources, evaluated against real tasks and scoped around privacy, review and operating cost.
Source and model integration
Define source access, model-provider requirements and data policies. Keep prompts, retrieved evidence and action permissions within explicit boundaries.
Evaluation and oversight
Prepare expected answers, unsupported cases and human-review rules. Compare quality, cost and latency before expanding automation.
A workflow.
You can inspect.
This reference flow identifies the responsibilities to agree during discovery. It is an illustrative planning model.
- 1
Approved input
What should the system decline rather than guess?
- 2
Model & evidence
Which information may be sent to a model provider?
- 3
Reviewed output
How will model changes be evaluated before release?
Review each handoff: what enters, who can act, what can fail and what evidence shows completion.
Good questions.
Better foundations.
These answers shape the architecture, estimate and acceptance checks. Record assumptions where an answer is still unknown.
What should the system decline rather than guess?
Which information may be sent to a model provider?
How will model changes be evaluated before release?
Start where
your project is.
The first step depends on what is already known. These starting points help define an engagement; scope, price and timing are agreed separately.
Explore an uncertain idea
The problem is clear, but the approach is not.
- 1Question
- 2Prototype
- 3Decision
What you take forward
A bounded experiment, findings and a recommendation on what to validate next.
A prototype is not a production-ready service.
Deliver a defined scope
You know the workflow and can agree acceptance checks.
- 1Scope
- 2Build & review
- 3Handover
What you take forward
Agreed deliverables, verification evidence and operating instructions for the scoped release.
Changes to scope need an explicit decision on cost and timing.
Improve an existing system
A live system needs focused changes or ongoing care.
- 1Review
- 2Prioritize
- 3Release & observe
What you take forward
A prioritized backlog, reviewed changes and an agreed maintenance or support boundary.
Coverage, response targets and access are agreed per engagement.
Model choice follows the evidence and the task
Technology is a scoping decision. These are relevant tools and building blocks to discuss against your existing systems, operating constraints and maintenance needs.
Sources
Document parsingRetrievalPermissions
Choose source formats and retrieval behavior around the approved material and each user’s access.
Application
Model APIsNode.jsTypeScript
Compare candidate model behavior on representative tasks before committing to a provider or deployment arrangement.
Operations
Evaluation setsLogsReview gates
Define an evaluation process and monitor answer quality, latency and operating cost within the agreed privacy boundaries.
The ecosystem.
Behind the experience.
Interfaces, application logic, data and operations. The building blocks work together around your project.
Frontend Development
- React
- Angular
- JavaScript
- HTML5
- CSS3
Backend Development
- Node.js
- Express.js
- Laravel
- PHP
Database
- MySQL
- PostgreSQL
- MongoDB
- Firebase
Cloud & DevOps
- Azure
- AWS
- Google Cloud
- Docker
- Jenkins
What should be checked
before handover.
Turn quality expectations into reviewable evidence. The exact checks and responsibilities belong in the project scope.
Grounded output
Evaluate answers against expected results and check that cited passages actually support the answer.
Safe boundaries
Test restricted documents, missing evidence and unsupported tasks; keep consequential actions behind agreed approvals.
Repeatable evaluation
Record evaluation cases and limitations so changes to models, prompts or source data can be reviewed before release.
Before
we begin.
Turn the idea into a scope that can be reviewed and maintained.
What does generative ai development include?
Generative AI applications grounded in approved sources, evaluated against real tasks and scoped around privacy, review and operating cost. The scope can include use-case definition, source and model integration, evaluation and oversight. Deliverables depend on the agreed workflow and existing systems.
Who is generative ai development for?
Teams adding document, drafting or knowledge features to a product or internal workflow.
What should we prepare before starting?
Bring the workflow, user roles, existing systems and anonymized examples. What should the system decline rather than guess? Start with a local project brief and share it when you are ready.
How are scope, timing and price agreed?
The estimate depends on the workflows, integrations, data, review requirements and operating responsibilities. These are agreed for the individual project. A planning brief does not book work or establish a delivery commitment.
Connected capabilities.
Still have questions?
Start with the brief.
Bring the problem you want solved. We’ll shape it into a project brief you can keep — or let AI draft a first approach.