AI practice
We put assistants and automations next to the systems you already run. Written SOW, the same engineers through delivery, and the boring controls a demo skips.
You have seen the chat window that looks finished. Then someone has to wire it to your files, decide what it is allowed to see, and catch it when it guesses. That second part is the job.
Myrafy is a software company in Noida. We take one assistant or one automation into production, keep the people who scoped it on the work, and write down what happens when the model is wrong.
Pick the ones that match how you actually work.
The assistant searches a corpus you name, cites what it used, and only sees what that person is allowed to see. If search comes back empty, it says so.
Ticket routing, document intake, copying between tools - work that is already written down somewhere. We give it retries, logs, and a person to land on when it cannot finish.
Once it is live, someone has to watch cost, quality, and what to do when it is wrong. That can be us, or a retainer with a runbook if you would rather run it.
We plug into what you run. We are not here to rip out a working ERP and install a bot.
This usually dies in the same three places as any other software: people, handoffs, and messy data. We start there.
The answers live in a few heads.
The same three people get pinged on chat. When they leave, the answers leave with them.
Work dies in the handoff.
Approvals sit in inboxes. Someone re-types the same fields from CRM into ERP. Automating that without an owner just moves the pile.
Search everything and you leak. Search nothing and it makes things up.
Production AI needs one place that is true, and it needs to respect who is asking. Logs you cannot audit do not count.
Most teams pick one. Both sit on the stack you already have. Neither needs a strategy offsite first.
A repetitive, well-defined path - invoices, tickets, onboarding, document routing, sync between tools. Someone owns the job. It has logs. When it cannot finish, a person gets it.
An assistant that answers from your corpus and cites the source, or a feature that changes how a team finds things. Built against the identity and data stores you already have.
The same four steps we use on this work. We will not promise a company-wide uplift from a kickoff.
Sit with the team. Watch the real path, not the one in the process doc. Name where context drops and who is allowed to see what.
One automation or one assistant. Something you already measure. Then we write inputs, owners, acceptance, and what happens when it fails.
Against your APIs and login. Search respects permissions. We test answers before you invite the whole company.
You get a runbook: quality, cost, logs, and who to call. If the work continues, that can be a retainer.
We name the files or system, how they stay current, and what the product does when search returns nothing. Models come after that.
The assistant sees what that user is allowed to see. A disclaimer in the UI is not access control.
Quality checks, a cost cap, and a person or a deterministic path when it cannot finish. If nobody can run it, we do not ship it.
The commercial bit, without invented win rates. We will publish customer stories when we have permission.
Inputs, owners, acceptance, and the fail path before we start. Fixed-scope when the slice is clear. Retainer when it will keep growing.
The people who scoped it stay on delivery. 15+ years collective experience. You will not get a junior bench behind a slide.
Code, prompts, tests, and runbooks transfer to you. We do not lock this in a product only we can run.
Typical reply from Noida the next working day. If the workflow is still fuzzy, discovery can be a paid slice.
Logs, retries, an owner, and a person to catch it. Not a notebook.
Retrieval over your corpus, citations, and a clear answer when nothing matches.
Quality checks, a cost cap, and logging written into the SOW.
Source, prompts, tests, and who to call when the model is wrong.
Shapes, not named clients yet. If yours is close, we will write a proposal around your constraints.
Representative patternHealthcare / AI
Staff ask questions of internal docs. Answers come with sources, respect who is logged in, and leave a trail someone in compliance can read.
Representative patternOperations / automation
Ticket routing, document intake, or sync between two systems. If the job cannot finish, it goes back to a person instead of vanishing.
If you want the long version: Shipping AI Features Past the Demo · All work patterns →
We use what is already in your stack when we can. This is not a vendor lock-in.
Send the process and the systems it touches. We usually reply with an estimate the next business day.
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