Artificial Intelligence & Machine Learning
AI & ML Development That Ships to Production, Not Just Demos
Most AI projects die between the demo and production — the model works, the integration doesn't, and the team never trusts it. Banao engineers the last 80% that vendors skip: LLM systems, predictive models, and computer vision deployed into your live operations. We run the same stack on our own 300-person business first.
The first call is free · 45 minutes · no obligation
What we deliver
The Gap Isn't AI Capability. It's Getting It Into Production.
You don't have an AI awareness problem — you have a deployment problem. The proofs-of-concept work in a notebook and stall before they reach a single customer. Most vendors hand you a model and walk away from the integration, monitoring, and change management that decide whether anyone actually uses it. Banao is an AI-native engineering partner: we've shipped production AI for Swiggy, PhonePe, Indian Oil, CP Plus, and RAK Ceramics, and we operationalize it the same way we run InterviewGod and Vikaas inside our own 300-person team. You're not a beta tester.
AI Opportunity Assessment
Before any code, we map where AI returns the most value against the cost to build it, then sequence the roadmap by ROI. It's the first phase of every Banao engagement and the reason our builds reach production instead of stalling as science experiments.
LLM Assistants & Conversational AI
We build retrieval-augmented assistants grounded in your own data, so answers come from your business, not a generic model. Deployed with guardrails, evaluation harnesses, and human-handoff paths — governed the same way as the AI support stack we run for our own team.
Natural Language Processing
Sentiment analysis, entity extraction, intelligent search, and document processing across multiple languages. We turn unstructured text — tickets, contracts, claims — into structured signals your systems can act on, with accuracy measured before anything goes live.
Computer Vision & Video Analytics
Object detection, OCR, quality inspection, and anomaly detection on images and video streams. We built the AI behind CP Plus's enterprise surveillance systems — vision models that run reliably in production, not just on a benchmark dataset.
Recommendation & Personalization
Behavioral recommendation engines that lift conversion and retention, integrated into your live product and your CRM — not parked in a dashboard. The same personalization patterns we've shipped for retail and e-commerce clients serving millions of users.
Predictive Analytics & Forecasting
Demand forecasting, churn prediction, risk scoring, and anomaly detection on your operational data. We build the model and the pipeline that feeds it, with monitoring that flags drift before it costs you — so predictions stay accurate as your data shifts.
Speech & Audio Intelligence
Speech-to-text and audio analysis tuned to your domain vocabulary and accents, integrated into the workflows where transcripts actually get used. Accuracy validated on your data, not a vendor demo set.
MLOps & AI Infrastructure
The operational backbone for AI at scale — data pipelines, feature stores, model registries, and monitoring. This is the layer most vendors skip and the reason their models quietly degrade. We build it so your AI keeps working six months after launch.
How we deliver
Our Proven AI Development Process
- 01
AI Strategy & Consultation
We pressure-test your business goals against where AI actually returns value, and kill the use cases that won't. Most projects fail here by building the wrong thing well — we'd rather spend week one saying no than ship an expensive distraction.
- 02
Data Collection & Preparation
We audit, clean, and structure your data and tell you honestly if it can't yet support the model you want. Skipping this is the single most common reason 'the AI doesn't work' — the model was fine; the data wasn't.
- 03
Development & Training
We build and train models against measurable acceptance criteria agreed up front, with evaluation harnesses that prove performance on your data. You see numbers, not adjectives, before anything ships.
- 04
Deployment & Integration
We wire the model into your existing systems, workflows, and access controls — the 80% of the work that decides adoption. A model behind an API nobody calls is a cost, not a capability.
- 05
Monitoring & Maintenance
We track accuracy, latency, and drift in production and retrain before performance decays. AI is not a one-time build; the systems that keep working are the ones somebody is watching.
Recent work
Recent Work
Manentia AI needed diagnostic teletracking physicians could trust enough to act on remotely. Banao built the imaging and reporting pipeline with accuracy validation at every step, so reports held up under clinical scrutiny. The result is a system physicians use for dedicated patient care anywhere — not a pilot that stayed in the lab.
Client reviews
What clients say about working with us
“Banao has helped shape up Happimynd into a creative design and exceptional development. The technical capabilities in web development at Banao are commendable.”
“Banao did a fantastic job in every way. They helped revamp the UI of our website as per our expectations. They were always on schedule when it came to delivering. We're not sure how they accomplish it, but the results are stunning. Their responsiveness and customer service are exceptional, and they are really appreciated.”
“Banao has great expertise at designing and building e-commerce apps and are trustworthy. We look forward to completing our 4 months current engagement and moving forward to a long term partnership.”
FAQ
Frequently asked questions
We tried AI before and it failed. Why would this be different?
Usually the failure was the data, the integration, or change management — not the model. We diagnose which one it was before proposing anything, then design specifically for that failure mode. We've broken and fixed our own AI systems running our 300-person operation; that scar tissue is part of what you're hiring.
Should we just build this in-house?
Many of our best clients started in-house and came to us six months in. AI/ML talent is hard to hire, the learning curve is real, and the project competes with your team's day jobs — in-house builds typically take 12–18 months. Because this is our day job, we compress that to 8–12 weeks, and we're happy to show you a side-by-side.
How do you keep our data secure and our models compliant?
Mutual NDA before detailed discussions, and DPAs for regulated industries like healthcare, finance, and GCC government work. You own 100% of the code, models, and training data. For BFSI and clinical use cases we build audit trails and explainability in from day one.
Will the AI keep working after launch?
That's the part most vendors skip. We instrument every model with monitoring for accuracy, latency, and drift, and retrain before performance decays. You get 30 days of post-launch support included, then an AMC or retainer for ongoing model and feature work.
Can you work with our existing stack and data infrastructure?
Yes — we're stack-agnostic by design. We've integrated AI into systems on MERN, Django, Java, .NET, and cloud data stacks across AWS, GCP, and Azure. Tell us what you're on and we'll tell you exactly what the integration path looks like.
What does an AI engagement cost and how long does it take?
We scope to bands: focused builds start around $50K, and most production AI engagements run $80K–$250K+ depending on scope, integrations, and data readiness. A first production system typically takes 8–12 weeks. The fastest path to a real number is a 2-week AI Discovery Sprint — fixed price, prioritized roadmap, ROI projection on your top three opportunities, no obligation to continue.