Not a tool for your team. A feature your customers use, inside your own product, under your own name. We build it into your codebase, keep each customer's data separate, make sure the cost per customer is a number you know, and hand it over with the tests.
This is for you if
Where this is used
These are the ones that survive contact with customers. The pattern they share is that each removes a specific task the user used to do by hand, rather than adding a chat box beside the work.
The step that loses deals. Reading whatever spreadsheet or export the customer arrives with, mapping it to your fields, and flagging what it could not place. Often worth more than any feature. Removes: weeks of manual set-up.
The reply, the report, the description, the summary, prefilled from what your product already knows, for the user to edit rather than write. Removes: the blank page.
Answers from that customer's own data, with their permissions applied and a link to the record it came from. Replaces the filter panel most users never learn. Removes: knowing where to look.
Noticing the duplicate, the wrong category or the number that does not fit the pattern, at the moment of entry rather than in a report next quarter. Removes: cleaning up afterwards.
Invoices, contracts, forms and certificates turned into fields in your product, with the low-confidence ones sent for a person to confirm. Removes: retyping.
The user asks for an outcome and your product carries out the steps, within that user's own permissions, showing what it did. Removes: the ten click sequence.
Services
Half of this list is not about AI. It is about shipping something into a product other people pay for, which has requirements a demonstration never has.
We look at your support tickets and your own usage data for the task users complain about or abandon. A feature that removes a named task gets used. One that adds a chat box beside the same work does not.
In your interface, in your patterns, with your components. If it looks like something bought and attached, users treat it as optional and it dies quietly in the analytics.
The requirement that separates a product from a project. Every retrieval, every log and every stored result carries the customer it belongs to, enforced at the data layer rather than trusted to the code path. One leak here ends the feature.
If a user cannot open a record in your interface, the AI must not read it either. We use your existing permission model rather than building a second one that will drift away from it.
Measured per action and per account, before pricing is decided. Otherwise your heaviest customer becomes your least profitable one and nobody notices for two quarters.
Limits per account, fair queuing, and a cheaper path once a plan's allowance is used. Standard product engineering, routinely forgotten on AI features because the demonstration had one user.
A visible confidence signal, an easy way to edit or reject, and a product that still works when the model is slow or down. Users forgive a wrong suggestion they can correct. They do not forgive a page that will not load.
Some of your buyers will have a policy against AI processing of their data, and some will need it off for one department only. A switch per account, honoured everywhere, keeps those deals alive.
Where data goes, whether it trains anything, what is retained and for how long, which sub-processors are involved. Written once, properly, so it goes into questionnaires instead of stopping deals.
A scored set of cases that runs in your pipeline on every change, so an improvement for one customer cannot silently break another. This is where our QA practice does most of the work.
Your engineers get the traces and the runbook. Your support team gets a way to see what a user was shown and why. Without that, every complaint escalates to whoever built it, which after handover is nobody.
Behind a flag, to a few accounts first, measuring whether people accept, edit or ignore the output. The edit rate tells you more about the feature than any survey will.
Tell us what your users do by hand today, and we will tell you what is worth building.
Our stack
Most of this is ordinary product engineering. That is the point, and it is the half that demonstrations skip.
Example
A company selling scheduling software to maintenance teams asked for an assistant. A chat box in the corner, ask it anything about your jobs. Their board had asked what their AI story was.
Before building it we looked at where their users actually struggled. The chat idea was interesting but nothing in the data suggested people wanted it. What the data did show was that new customers took about six weeks to go live, and most of that was one person retyping asset lists out of whatever spreadsheet the customer had.
So we built that instead. Upload the customer's own file, whatever shape it is in, and the product maps the columns to its fields, flags what it cannot place, and asks about the rest. It is not the impressive feature. It is the one that shortened their onboarding, which is the number their sales team is measured on.
The chat box came later, once there was a retrieval layer to answer from. In that order it was two weeks of work rather than a project.
An example, invented to show how the feature gets chosen. What your usage data says will be different.
FAQ
Yes, that is the normal arrangement. Your repository, your branch and review process, your release train. We are not building a separate service and handing you an API unless you specifically want that.
Your team owns it. That means their code review from the first week, documentation as we go, the evaluation set in your pipeline, and a handover session with whoever will support it. If nobody on your side has touched it, we have done this badly.
We give you the cost per action and per account so you can decide. What we would advise against is giving it away on every plan before you know what your heaviest customer costs you, which is the mistake we see most.
Then it has to be switchable per account, and that is designed in rather than added later. For some buyers a private model in their own region is what unlocks the deal, and that is a related piece of work we also do.
Six to ten weeks for something in front of real customers, if the feature is chosen well. Onboarding and document reading tend to be quicker than anything conversational, because success is easier to define.
Yes, and it is the better arrangement. Usually one or two of ours embedded with your team, so the knowledge stays with you rather than leaving with us.
Next step
The step they complain about, the one where they abandon your onboarding, or the export they always ask for. That is where a feature is worth building, and it is rarely the one the board asked for.
Ahmedabad, India. We work with teams in the US, UK, Europe, Singapore and the Gulf, and we are used to the time difference.