Generative AI Development
Generative AI Development That Ships to Production
The demo works. The last 80% — grounding, integration, trust — is where most projects stall.
We close that gap before it reaches you, using the same engineering discipline that runs our own 300-person operation.
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02 · What We Deliver
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Proof before promises
We don't ask you to believe a demo. We run every system below inside our own operation first, at the scale and messiness of a real business, before it reaches a client.
~300
person operation running on our own AI — InterviewGod for hiring, Vikaas for outreach, Vidya for upskilling.
30+
enterprise clients in production, including Swiggy, Myntra, PhonePe, Indian Oil and FootLocker.
2016
the year we started building AI systems — this is not our first cycle.
Customer-facing assistants
Answers grounded in your data, with citations and a handoff to a human when confidence drops.
Unstructured text into decisions
Contracts, tickets, transcripts and filings parsed into decisions a system or person can act on.
Anomaly and fraud detection
Models trained on your own transaction history, flagging what deviates before it costs you.
Demand forecasting
Forward demand signals built from your own historical and real-time data.
Visual and voice search
Customers find products by photo or by speaking, matched against your catalog.
Production content systems
The pipeline that generates, checks and ships on-brand content at real volume.
On-brand visual generation
Brand-locked design systems that generate visual output without manual rework.
LLM integration and orchestration
The wiring between models, your systems of record and your existing stack.
03 · How We Deliver
Where it breaks, in order
Six stages, three failure points named in advance — data, integration, trust — so the gap gets closed before it reaches you.
01
Discovery & use case definition
We scope the exact production job the model has to do — not a demo. If there's a prior attempt, we diagnose where it stalled before proposing anything.
02
Failure point — data
Data collection & preparation
The system is grounded in your own data, with citations back to source — not the model's guesses.
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Model selection & development
Built with the same models and patterns running our own 300-person operation — InterviewGod, Vikaas, Vidya.
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Failure point — integration
Integration & system design
Wired into what you already run. You own the system that comes out the other end — nothing here is locked to us.
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Failure point — trust
Testing & optimization
Tested against production failure modes, with a confidence threshold and a fallback to a human built in before go-live — not discovered after a bad answer reaches a customer.
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Deploy & continuous support
Shipped in weeks. The team that built it stays on it after go-live.
04 · Recent Work
Studylab AI
Majra
What we shipped, not what we pitched
Held to the same standard we hold our own 300-person operation to.
A UAE deployment
05 · CLIENT REVIEWS
What our clients say
"We came to Banao with a screening process that lived in spreadsheets and someone's inbox. They rebuilt it as an actual platform — clinical assessments, automated reporting, counselor case management, role-based access for our admin team — and stayed on through five engagements as our needs kept changing. It's the system our care team runs on every day now."
RaviKant
CEO & Co-founder, Happimynd
"Our platform went through real phases — from a therapist booking marketplace to anonymous peer-support chat rooms to a matching system with escalation to a paid therapist. Banao rebuilt the product at each turn without us losing momentum. That kind of continuity across five engagements is hard to find."
Jabez Zinabu
CEO, LeapifyTalk
"We needed one system to unify online ordering with a retail network that was already running across multiple stores — inventory, pricing, shipping, invoicing, loyalty, all of it. Banao built the admin, seller and retailer panels as one connected platform. Seven engagements in, they still know our operations better than most new hires would."
Parth Sethia
Product Manager, O-line-O
06 · FAQ
Still weighing it? Book a 45-minute call
Answers, not a pitch.
The objections that usually surface before a scoping call — hallucination, ownership, cost — answered the way we'd answer them on one.
Q1The demo worked. Why did it fail in production?
Because demos don't carry the last 80% — grounding, integration, trust. Production means the model answers from your data, sits inside systems that already run, and someone can sign off before it acts. We name which of the three broke your last attempt before we propose anything.
Q2How do you stop the model making things up?
Every answer is retrieval-grounded — pulled from your data, not the model's memory — and carries a citation back to source. Below a confidence threshold, it hands off to a human instead of guessing.
Q3Will our data train someone else's model?
No. You own the system — the code, the data, the deployment. That's the OWNED pillar of how we build: no lock-in, and nothing about the engagement routes your data into anyone else's product.
Q4Do we need to replace our existing systems?
No. Integration is one of the three places production stalls, so we build to sit inside what's already running — your stack, your systems — not to rip them out.
Q5We tried AI before and it didn't deliver. Why different?
We run our own ~300-person operation on the same production AI — hiring on InterviewGod, outreach on Vikaas, upskilling on Vidya. We don't sell software we hope works. We sell the software we depend on.
Q6Can you work with our existing cloud and tech stack?
Yes — production AI has to sit inside what you already run, not next to it. That's the integration half of the last 80%.
Q7Should we just build this in-house?
You can. What usually stalls an in-house build isn't the model — it's grounding, integration, trust. We've already closed that gap at Swiggy, Myntra, PhonePe, Indian Oil and HCL scale, and on our own operation, before it reaches yours.
Q8What does it cost, and how long does it take?
Weeks, not quarters — the number depends on which of the three problems you're solving. That's scoped on a call, not priced here.
07 · Proof, Then a Call
Grounded in production, not a lab.
Every system we ship runs first inside our own operation — InterviewGod for hiring, Vikaas for outreach, Vidya for upskilling — before it reaches a client. We do not sell software we hope works. We sell the software we depend on.
- 30+Enterprise clients, including Swiggy, Myntra, PhonePe and FootLocker
- ~300People running their day-to-day on our own AI, not a client's
- 2016Building production AI since — Bengaluru, Chandigarh, Dubai, Cambridge, California