Most teams do not need "an AI". They need a specific task to get faster, cheaper, or more consistent: answering customer questions, finding information across documents, processing requests, or drafting routine work. We build AI products around those tasks, so they are useful from the first release and dependable as they grow.
For small businesses, founders, and product teams, a useful first step is to choose one recurring task and test it with real examples. We can help assess the data, define a focused pilot, and agree on what success should look like before expanding.
Where a focused pilot can help
- Customer support: answer a limited set of questions from approved help content, with an option to reach a person.
- Internal knowledge: help staff locate answers in permitted documents and check the original sources.
- Routine workflows: prepare summaries or route requests for human review before an action is taken.
These are examples, not guarantees of a particular outcome. The right scope depends on your data, systems, and risk requirements.
Who this is for
- Companies with repetitive, text-heavy work such as support, operations, sales, or admin
- Teams sitting on valuable knowledge in documents, wikis, and past tickets that is hard to find
- Product teams that want to add AI features to an existing product
- Founders who want to test an AI product idea with real users
What we build
- Customer support assistants that answer from your help content and order data, and hand over to people when needed. How AI customer service works
- Internal knowledge assistants that answer questions from your own documents, with links to the source. What you need before building RAG
- AI agents that complete multi-step tasks across your tools, such as triaging requests, looking up records, and preparing actions for approval
- Document processing that extracts information from invoices, contracts, and forms
- AI features inside your product, such as summaries, smart search, drafting, and classification
- Workflow automation that connects AI to the systems your team already uses
What you get
- A working AI feature or product, tested with your real data and real examples
- Integration with the systems it needs, such as your CRM, help desk, database, or website
- Guardrails: clear limits, human review where it matters, and safe handling of data. Our guardrails checklist
- A test set of real questions or tasks, so quality can be measured, not guessed
- Monitoring, documentation, and a handover your team can work with
How we work
- Discover. We look at the task, the people doing it today, and the data available, and agree on what success looks like.
- Test feasibility. Before building screens, we try the AI on real examples to confirm it can do the job well enough.
- Build the first version. One workflow, end to end, with the guardrails in place.
- Pilot and measure. A small group uses it on real work. We measure quality and adoption, then improve.
- Expand. Once it proves its value, we extend it to more tasks, teams, or channels.
Common questions
Which AI models do you use?
We choose the model for each task based on quality, cost, speed, and your data requirements, and design systems so the model can be changed later if something better or cheaper becomes available.
Is our data safe?
We map exactly which data goes where before building, send only what each task needs, respect your existing access permissions, and choose setups where your data is not used to train models. Where you have specific compliance requirements, we design around them from the start.
What if the AI gets something wrong?
Every AI system makes mistakes, so we design for them: answers grounded in your sources, an honest "I don't know" when information is missing, human review for high-impact actions, and easy ways for users to flag problems.
How much does it cost?
It depends mainly on scope, data readiness, and integrations. We usually recommend starting with a focused first version, which keeps the investment contained while proving value. What drives the cost of an AI project
