Most of the teams we sit with already have a B2B product in customers’ hands.
The person on the call is usually the founder, the CTO, or the head of product.
Leadership wants something on the AI roadmap, a competitor just shipped a copilot, support is underwater, or an early proof of concept never quite became part of the product.
Customers ask questions of their own documents, and the answer shows where it came from.
An assistant sits in the screens your users already use, and it can call the APIs those screens use.
The repetitive steps in a workflow get a first pass. A person still approves anything painful to undo.
A contract, invoice, or form comes back as fields your app can store.
Search follows the way a customer phrases the question, across that customer’s data only.
When the product has a reason to be on the phone, the caller reaches the product, not a generic front desk.
Drafts and queues come first. A post goes out when someone on the team says so.
Each tenant has a cap, so a busy week does not turn into a surprise invoice. Sometimes the feature is meant to be sold onward, inside your product, under your name.
Search results and actions follow whoever is signed in.
Some products already separate customers with row-level security, some with a schema, some with a store of their own. We stay with the pattern your app already uses, and before we call it done we check that one customer cannot see another.
Before launch we agree on a small set of real questions the feature should keep getting right. When a prompt changes, we run them again, and we can roll that prompt back.
Personal details stay out of casual logs. If a model is slow, there is a fallback. Each tenant has a cap, so a busy week does not become a surprise invoice.
We start with a week or two beside the product owner: a sketch, a picture of cost, and a written scope.
Then one feature in your repo, usually over 4–8 weeks.
If that feature needs admin controls and tighter isolation, it grows into a larger module. If you want the next slices done the same way, we stay on.
Day to day that is Laravel or Node, React or Next, and Postgres. Retrieval sits in pgvector, or in Pinecone, Qdrant, or Weaviate when that fits better. The model is OpenAI, Anthropic, or Azure OpenAI, and it runs in your cloud account. If the product needs a phone channel, we add Twilio and a realtime voice layer.
If most of the data is health information, tell us that up front. We will want to understand how you already handle it before we sketch anything.
Yes. It can live in your product, with each of your customers’ keys and history kept apart.
Most first features take 4–8 weeks after a short discovery. Larger ones take longer, and we write that down before anyone starts.
If this is the feature that has been sitting on the roadmap, tell us about the product.
