Your clients have started asking for AI, and the honest problem is not building it — a demo takes an afternoon. It is everything the demo skips: what happens when the model is confidently wrong, who is allowed to see which documents, what it costs once a thousand people use it, and how anyone can tell whether a change made it better or worse. Panze builds the feature and all of that around it, under your brand, and hands it over whole.
Our Process
Four steps. The first one talks a fair number of people out of the feature they asked for, which is cheaper than building it.
Services
Panze builds assistants grounded in your client's own documents and records, with the source shown next to the answer. An assistant that cannot say where something came from is one nobody trusts twice.
Services
Panze builds assistants grounded in your client's own documents and records, with the source shown next to the answer. An assistant that cannot say where something came from is one nobody trusts twice.
Panze builds the ingestion, indexing and permission handling behind a useful assistant, so the index stays current and one customer's data can never answer another customer's question.
Panze builds the repetitive work out of the day: triaging what comes in, drafting the reply, filling the record — and, most importantly, the point where it stops and asks a person.
Panze builds a set of real questions with known answers, and the screens your client's team uses to read what was asked and correct it. Without both, an AI feature can only be improved by opinion.
What We Build
The model is the part you rent. These are the parts that are yours, and they are what makes the difference between a demo and something people keep using.
Chat and search grounded in their documents, products or records rather than in general knowledge, with the source shown next to the answer. An assistant that cannot say where something came from is one nobody trusts twice.
The repetitive work done without a person: triaging what comes in, drafting the reply, filling the record, flagging what needs a human. Including the part that matters most, which is where it stops and asks.
What the product shows when the model is slow, down, over budget or plainly wrong. Every one of those happens, and a feature with no answer for them fails in front of your client's customers rather than quietly.
A set of real questions with known good answers, run against every change. It is the only way to tell an improvement from a regression, and it is the thing most AI work skips.
Limits per user, caching for what gets asked repeatedly, and a smaller model where a large one is waste. An AI feature with no ceiling is a bill that arrives after launch.
Screens for your client's own team to read what was asked, see what was answered, and correct it. Without this nobody inside the business can tell whether the thing is working.
Benefits
Four things that matter when a client has asked for AI and the answer cannot be no.
Some briefs are better served by search, a rule or a saved view, and we say so before the quote rather than building something expensive that underwhelms. A feature that disappoints is a problem you inherit, not us.
Every build leaves with an evaluation set, so the next person can change a prompt and know what it did. Without one, an AI feature can only be improved by opinion.
Code, prompts, evaluation set and the provider accounts transfer at handover. No licence, no retainer required to keep it running, nothing that stops working if you never call us again.
We work in your tools and under your brand. White label builds stay off our portfolio permanently, in writing, before we start.
Industry Expertise
Panze builds AI features for agencies whose clients differ mostly in what a wrong answer costs. That is what decides how much review the product needs around the model, and it is settled before the build rather than after the first complaint.
AI inside an existing platform: assistants over the client's own data, with the permissions the product already enforces carried through rather than worked around.
AI features where a wrong answer is expensive, so sources, limits and a human checkpoint are part of the build rather than a later addition.
AI features built around what the model is allowed to read, what leaves the client's systems at all, and where a clinician has to confirm before anything happens.
AI features on a phone, where response time, cost per request and what happens with no connection decide whether anyone keeps using them.
AI inside internal tools, with single sign-on, per-team permissions and a record of what was asked and answered that their own compliance people can read.
A first version small enough to test with real users, and an honest answer about whether the idea needs AI at all before anyone spends runway on it.
Search that understands a question, descriptions written at catalogue scale, and support answered from the merchant's own policies rather than from guesswork.
AI work your agency sells under its own name, with Panze invisible and the handover written down before the project starts.
Frequently Asked Questions
Scope, data, cost, ownership, and what happens when the model gets it wrong.
Contact
No pitch decks. No jargon. Just a conversation about what you're building, and how we can help.
Mahmudul Hasan Manik
Founder & CEO
