Start a project ↗AI / SOFTWARE
Custom AI software development and LLM integration for a specific user task. We build a new application or add AI to an existing product, connecting approved context, model output, application rules and a usable interface.
OUTCOMEAI becomes one controlled component of a working product, not a promise attached to an empty interface.
AIDELIVERABLES
Each engagement is scoped around the smallest complete system that can deliver the intended result.
For a new product, define the interface, user state and complete journey. For an existing product, define the API boundary and the one task AI should add. We agree on example inputs, expected outputs and acceptance checks first.
Approved facts, files, instructions and task context are separated so output can be inspected and corrected.
Connect the selected provider, prepare task-specific requests and validate returned structures before they affect product state. Timeouts, invalid responses and unsupported claims have explicit stop or recovery paths.
The interface shows what the model proposed and what the application actually did. Storage, permissions, approval where needed and operating documentation turn the model connection into usable software.
USE CASES
Good software starts with an operating need. These are common patterns, not fixed packages.
Content Operator illustrates the complete product boundary: local source knowledge, six draft formats, review and supported publishing connections. Generation formats do not imply automatic publishing to every network.
Combine deterministic scoring with contextual analysis without presenting model output as certainty.
Add interpretation, classification or draft generation to an existing flow. Keep account permissions, record changes and consequential actions in application code rather than letting a model execute arbitrary instructions.
Local-first applications for workflows involving private files, credentials or operator-controlled actions.
PROCESS
One continuous path from product definition to build, validation and launch.
Separate the business outcome from the parts where AI is genuinely useful.
Establish approved inputs, output constraints, review steps and data handling.
Connect the model to product state, storage, tools and a clear user interface.
Evaluate unsupported claims, stale approvals, malformed output and provider failures before expanding automation.
RELATED WORK
Verified knowledge becomes six platform-specific drafts, visuals and an approval-controlled publishing queue.
View case ↗02Self-hosted AI automation + courseMathematical opportunity scoring, contextual AI analysis and controlled publishing — with a course and included program.
View case ↗03AI Telegram productCanonical rules protect the experience while AI creates context-specific interpretation.
View case ↗FAQ
We can design and build the complete product: interface, application logic, storage, AI integration and deployment path.
Yes. A bring-your-own-credentials model can be used when it fits the product and the provider’s access rules.
Yes. Local-first storage is possible for desktop workflows. Any selected context sent for generation still reaches the configured AI provider.
Yes. Approval-first operation is often the right starting point for external publishing, customer communication and other consequential actions.
Connecting a model to an existing product involves more than an API request: context selection, authentication, output validation, user states, error handling and an interface for the result. We confirm what the current application exposes before promising a connection.
AI software may be a complete user-facing application. Workflow automation primarily coordinates recurring steps between inputs and actions. They can share components, but the first-release scope and acceptance checks follow the actual user task.
Use representative examples, edge cases and tasks where the correct outcome is to stop or ask for review. Check output validity separately from whether the answer is useful, and validate recovery when the provider fails.
The estimate depends on the complete user task, existing API access, data preparation, interface and review states, evaluation, deployment and handoff. WeMAIde’s minimum project budget is USD 1,500; this is an engagement threshold, not a fixed price for a complete AI application. Model usage, infrastructure, external services and ongoing maintenance are budgeted separately. We define assumptions and acceptance checks before estimating the agreed scope.
RELATED INSIGHTS
Plan an AI feature for your existing software: define the task, connect approved data, validate outputs and test the complete workflow before release.
Read insight ↗
Estimate an AI application's build and running costs: scope, integrations, testing, model usage, maintenance and a transparent worked token-cost example.
Read insight ↗
A practical build-versus-buy framework for teams considering a custom AI application, workflow or internal system.
Read insight ↗Plan an AI document-processing workflow with source evidence, field validation, human review and controlled export. Includes a synthetic invoice example and review checklist.
Read insight ↗Compare JSON mode, schema-constrained output and tool calling. Run an offline invoice example that separates parsing, shape checks and business validation.
Read insight ↗START A PROJECT
Tell us what needs to work. We will come back with the clearest next step.
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