AI engineering services
In short
Mirai Minds offers five AI engineering services: AI agents and automation, voice AI agents, WhatsApp AI agents, custom LLM and document AI, and computer vision. Each is delivered end to end — scoping, evaluation, build, launch with human review and running it afterwards — and most builds ship in two to eight weeks.
AI agents and automation
Agents that read, decide and act inside your tools, with every tool call logged and a person approving the risky ones.
- Operations copilot over internal APIs
- Browser or desktop task agent
- Research and enrichment pipeline with human approval
Voice AI agents
Phone agents that answer, qualify and follow up in English or Hindi, and bring in a person when the caller needs one.
- Inbound support line with live handoff
- Outbound qualification campaign
- Abandoned-cart recovery calls
WhatsApp AI agents
WhatsApp agents that remember each customer, answer from your documents and let your team step in from an inbox.
- Lead qualification and follow-up on WhatsApp
- Customer support with answers from your documents
- Onboarding that turns a chat into a profile
Custom LLM and document AI
Language models grounded in your documents and data, measured on your own test set, with people reviewing what matters.
- Document extraction into ERP or SAP
- Question answering over policies, manuals or catalogs
- Call and meeting analysis
Computer vision
Vision models that find faces and products, cut out backgrounds and edit photos, fast enough for a queue of real customers.
- Face search for event and venue photos
- Background removal and compositing
- Generative photo effects with identity checks
Approach
How we pick the approach
Start from the job, not the model
Every engagement starts with the work the system has to do: the documents it reads, the calls it takes, the actions it may and may not take, and what happens when it is unsure. The model comes later, and it is usually the easiest part to change.
Build the test set before the system
In the first week we collect real examples — documents, recordings, chats, photos — and agree what a correct answer looks like. That set tells us which model is good enough, catches regressions when prompts change, and keeps running after launch. Numbers we quote come from it.
Keep a person in the loop until the numbers say otherwise
Extraction gets a reviewer, voice agents get a live handoff, and agents that act in your software get limits on what they may do alone. We relax those checks only when the error rate on real traffic says it is safe.
Choose the simplest stack that holds up
A hosted platform is often the fastest way to a pilot; a custom build pays off at volume, with strict data rules or with Indian languages. We model running costs on your expected volume before you commit, and we say when a smaller or cheaper model is enough.
Process
How an engagement runs
Week 01
Discovery call
Thirty minutes with an engineer. You describe the job; we say whether AI is the right tool and what it would take.
Week 12
Scope and evals
We agree what done looks like and build a test set from your real data before writing the system.
Weeks 2–63
Build
Working software every week, measured against the test set, with your team trying it early.
Launch4
With human review
The system goes live with a person checking the risky steps and a clear way to reach a human.
Ongoing5
Run and improve
We watch real traffic, fix what breaks and tune on real cases, or hand over with documentation.
FAQ
Asked on the first call
Can one system combine voice, WhatsApp and an agent?
Yes, and it often should. On iKoMatch, founders onboard over WhatsApp and an AI phone call fills in whatever the chat missed; both feed the same founder profile and the same matching engine. We design the core once and add channels on top.
How do you decide what to build first?
We start from the job that costs the most time or money today and the data that already exists for it. The first release does one job end to end, with a person reviewing the output, so you see value in weeks rather than after a long platform project.
Do you only build new systems, or improve existing ones too?
Both. Some of our work adds AI stages to software a client already runs; the Photo Experience platform, for example, predates us. We read the existing code and data first, then decide what to keep, fix or replace.
What if AI is the wrong tool for our problem?
Then we say so on the first call. Some problems are better solved with rules, a better form or a spreadsheet, and a model would only add cost and failure modes.
Have a system in mind? Let's scope it.
A 30-minute call with an engineer who has shipped this before. You leave with a plan, a rough timeline and what it would take — whether or not we build it.