Give AI a useful job.
Search, document handling, voice, or actions inside a product you already run. We start by asking what the AI must do and what information it can rely on, then build it into the workflow with the checks it needs.
A convincing answer isn't enough.
If someone acts on what the AI says, by approving a claim, calling a lead or quoting a figure to a client, the answer has to come from the right source and be checkable. That shapes everything we build: what data the feature can see, who can trigger it, which actions wait for a person, and how you tell whether it is working.
We also say when AI is the wrong tool. Plenty of tasks are better handled by clear rules or a plain workflow automation, which costs less to run and is easier to trust.
We agree the cost of a wrong answer before deciding how much the feature is allowed to do.
Where we have built this already.
LessRounds
Applicants take a spoken first interview from their browser whenever it suits them. The hiring team gets the recording, the transcript and the reasons behind each score, and people decide who moves forward.
- Scores arrive with their reasoning
- Recordings and transcripts kept for review
SmartBDM
Our CRM keeps every call, note and WhatsApp exchange with the lead it belongs to. From there, AI helps keep the record current, proposes what to do next and points out leads that need attention, while salespeople stay in charge of the relationship.
- AI suggests, the salesperson confirms
- Leads that need a nudge get flagged
Four things every AI feature needs.
Grounding
The feature draws on the information it should use and nothing else. In DataStandard, a client platform we built, financial figures for Indian listed companies reach Claude and ChatGPT with their source, period and reporting basis attached, so a researcher can check every number.
Permissions
The AI sees only what the person using it is allowed to see. A sales assistant should never read HR files simply because they sit on the same server.
Review and control
We decide which outputs go straight through, which are drafts for a person to approve, and which actions the AI may never take by itself.
Evaluation
We test against examples from the real task, including hard cases and ones that should go to a person, and keep testing after launch as prompts, models and data change.
Bring these to the first conversation.
- Real examples of the task, especially the ones your team finds difficult.
- The expected answer or outcome for each, or the notes a reviewer would write.
- The data sources the feature would draw on, and who controls access to them.
- The point where a person takes over, and how they would be told.
- What a wrong answer costs, in money, time or trust, for each kind of output.
- Who will own the feature on your side once it is live and people start relying on it.
Can you add AI to software we already have?
Yes. We review the workflow, how the data can be reached and what the product needs to change. Sometimes the AI runs beside the existing system; sometimes it needs new screens for review. Product AI shows the kinds of features this covers.
Which AI models do you use?
It depends on the task, the cost and where your data may be processed. We choose per feature and keep the design open to switching providers. Model usage is billed by the provider, in accounts your business owns.
How do you stop it making things up?
By narrowing what it can draw on and showing where each answer came from. When the source doesn't contain an answer, the feature should say so and hand over to a person. Our evaluation set includes questions like that on purpose, so we can see whether it guesses.
Does every task need AI?
No. If a rule or a lookup does the job reliably, we will build that instead.
Got a task you think AI could handle?
Send us a few real examples and we will tell you what a dependable version of it would take.