The right fit
SaaS products that want to add AI-powered features without rebuilding their architecture
Operations teams spending hours on tasks that language models can handle in seconds
Healthcare, legal, or finance businesses that need AI output to be reliable and auditable
Founders who want to ship AI features that users actually keep using, not abandon after two sessions
Scope of work
Our process
Use Case Definition
We start by identifying the specific job the AI feature needs to do and defining what a good output looks like versus a bad one. Vague AI features ship broken. Specific ones ship working.
Model and Stack Selection
Not every task needs GPT-4. We match model capability to task complexity and budget — using smaller, faster models where appropriate and reserving expensive inference for tasks that justify it.
Prompt Engineering and Testing
Systematic prompt development with a test set of representative inputs, edge cases, and adversarial examples. We do not go to production with a prompt we have only tested on best-case inputs.
Integration and Validation Layer
The AI output is one part of the system. The validation layer that checks that output before rendering it to users or passing it to downstream logic is equally important and often more complex.
Monitoring and Iteration
Post-launch tracking of model performance, cost per request, error rates, and user engagement with the AI feature. AI features require ongoing tuning in a way that regular features do not.
Technology stack
Outcomes
AI features that users engage with repeatedly, not once out of curiosity
Structured, validated output that downstream systems can depend on
Cost visibility per feature before you discover an unexpected API bill
A validation layer that catches model failures before users see them
Documentation of every prompt, model choice, and design decision so the next engineer can maintain what we built
Frequently asked
Do you work with models other than OpenAI?
Yes. We work with Anthropic Claude, open-source models via HuggingFace or Ollama, and custom fine-tuned models. Model selection depends on your use case, latency requirements, data privacy needs, and budget.
How do you handle hallucinations in business-critical applications?
Through validation layers, structured output formats, retrieval augmentation with authoritative sources, and user interface design that signals confidence level. We do not treat hallucinations as an acceptable product failure.
Can you add AI to an existing product we've already built?
Yes. We assess the existing architecture, identify integration points, and scope the work cleanly so AI features are added without destabilizing what already works.
What does AI integration cost?
Project cost depends on complexity. A single well-scoped AI feature typically takes 2 to 4 weeks. Ongoing API costs depend on usage — we will give you an estimate of expected inference costs before you commit.