AI SaaS · Copilots
AI Product Development
Most AI prototypes work in a demo and fall apart in production. We build the version that holds up.
The problem
A model call in a notebook is not a product. The hard part is everything around it: grounding responses in your data, handling the cases where the model is wrong, keeping latency and cost sane, and making the behaviour observable enough to improve. Teams often ship an impressive demo, then stall for months turning it into something they can actually put in front of customers.
What we do
We design and engineer AI-native applications end to end - retrieval and grounding, evaluation and guardrails, the surrounding SaaS surface, and the infrastructure that keeps it reliable. AI is treated as one component of a real product, not the whole story. That means the same team owns the data pipeline, the API, the UI, and the deployment.
The outcome
An AI feature you can defend in front of customers: grounded in your own content, measurable, and built so accuracy and cost can be tuned over time rather than guessed at once.
Key capabilities
What's included
- Retrieval-augmented generation over your documents, policies, and knowledge
- Copilots and assistants embedded directly in product workflows
- Structured extraction, classification, and summarization pipelines
- Evaluation harnesses, guardrails, and human-in-the-loop review
- Prompt, model, and cost strategy with observability built in
- Streaming interfaces and responsive UX around model latency
What we'll think through
- How responses are grounded and how hallucination is contained
- Evaluation: how you'll know quality is improving, not just changing
- Latency, streaming, and perceived responsiveness in the UI
- Token cost modelling and where caching or smaller models fit
- Data privacy, retention, and where inference is allowed to run
Who it's for
- Founders validating an AI-native product idea
- Product teams adding intelligent features to an existing app
- Companies sitting on documents or data they can't search well
How we engage
We usually start with a tightly scoped slice - one workflow, grounded in real data, with evaluation from day one - so quality is measurable before scope expands. From there we harden it into production: guardrails, observability, cost controls, and the surrounding product.
Related work
Where this shows up
Questions
Common questions
Do you train custom models?
Rarely, and only when it's clearly justified. For most products, retrieval, good prompting, evaluation, and the right model choice outperform a custom-trained model at a fraction of the cost and risk. We'll tell you honestly which situation you're in.
How do you keep the AI from making things up?
Grounding responses in your own content, constraining outputs where structure matters, adding evaluation and guardrails, and designing the UI to show sources and invite correction. Reliability is an engineering problem, and we treat it like one.
Can you work with our existing model provider or infrastructure?
Yes. We're provider-agnostic and design so the model layer can be swapped as pricing and capability change, rather than hard-wiring you to one vendor.
Related services
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