SaaS & MVP Development
Build new software products from concept to launch.
AI integrations & automation
Practical AI Integrations for Software and Business Workflows
Integrate AI capabilities into your applications, connect business systems, and automate workflows using established AI APIs, language models, and sound application engineering.
Common requirements
Where AI Fits in Real Software
Typical AI requirements, and how each is handled as an engineering problem rather than a demo.
- Adding AI functionality to an existing product
- Integrate AI features into current workflows, data, and permissions instead of bolting on a separate tool.
- Integrating LLM-based features
- Connect to established model APIs with prompts, context, and outputs designed for the specific feature.
- Processing documents and structured information
- Extract, classify, or summarize content into structured outputs the application can validate and store.
- Automating repetitive workflows
- Automate defined steps between systems, with review points where human judgment is still needed.
- Connecting disconnected business systems
- Link CRMs, document stores, and internal tools through APIs so AI features work with current data.
- Building AI-enabled product features
- Design features around what models do reliably, with clear fallbacks when they don't.
- Handling AI responses within application workflows
- Validate, parse, and route model output before it reaches users or other systems.
- Establishing appropriate access controls
- Apply the application's existing permissions to what AI features can read and do.
Scope of work
What an AI Engagement Covers
The integration and engineering work we offer, what it can produce, and how it can be arranged. We integrate established models; we do not train foundation models.
What we offer
Engineering capabilities
- AI API integration
- LLM integrations
- AI-enabled product features
- AI assistants
- Structured AI outputs
- Document processing workflows
- Third-party service integration
- Workflow automation
- Application and backend orchestration
- Validation and error handling
- Data flow integration
What an engagement can produce
Possible deliverables
- AI-enabled application feature
- AI integration
- AI assistant interface
- Document processing workflow
- Automated business workflow
- Third-party integration
- Structured-output API
- AI integration documentation
Deliverables depend on the agreed scope of each engagement.
How the work is arranged
Engagement options
- AI feature developmentA defined AI capability added to a new or existing application.
- AI integration projectConnecting models and business systems for a specific workflow.
- Ongoing AI-enabled engineeringContinued development of AI features as part of a product roadmap.
- Workflow automationAutomating agreed process steps across existing tools and services.
Engineering considerations
Responsible Engineering for AI Features
AI features behave differently from conventional code. These considerations are addressed during design, not after release.
Privacy and data handling
Decide what data is sent to model providers, what is stored, and what must never leave your systems.
Cost awareness
Choose models, context sizes, and caching with usage costs in mind.
Reliability and validation
Constrain and validate model output before the application relies on it.
Failure states
Define what happens when a model is slow, unavailable, or returns something unusable.
Human review
Keep people in the loop where decisions carry real consequences.
Access and permissions
AI features act within the same permissions as the user they serve.
Please note: Output quality depends on the model, the data, and the use case. Behavior is evaluated against agreed examples rather than guaranteed.
How we approach the work
From Use Case to Working Integration
AI work adds evaluation and reliability steps to the usual delivery stages.
Step 1: Discover
Identify the use case, the data involved, and what a useful result looks like.
Step 2: Design the integration
Choose models and APIs, define data flows, permissions, and failure handling.
Step 3: Prototype
Test the approach on representative examples before full implementation.
Step 4: Evaluate
Review output against agreed examples, and adjust prompts, structure, or scope.
Step 5: Build and integrate
Implement the feature in the application with validation and error handling.
Step 6: Release and refine
Deploy, observe real usage, and refine within the agreed engagement.
FAQ
Questions About This Service
Answers to common questions. Anything specific to your project can be discussed directly.
Can AI be added to an existing application?
Yes. Most AI work involves existing applications: adding features that use the application's data, workflows, and permissions.
Do you build custom AI models?
No. We integrate established AI models and platform APIs into software and workflows. We don't train proprietary foundation models.
Can AI integrations connect to existing business systems?
Yes, where those systems offer APIs or other supported integration methods. Connecting to current data is often what makes an AI feature useful.
How are sensitive information and access controls considered?
They're part of the integration design: what data is shared with model providers, what is stored, and which permissions AI features operate under. Specific requirements are agreed per project.
Can AI features be developed as part of a larger SaaS project?
Yes. AI features can be scoped within a broader SaaS or application build, so they're designed into the product from the start.
Exploring AI for Your Product or Workflows?
Describe the workflow or feature you have in mind, and we'll discuss whether AI fits and how it could be integrated.