Most generative AI projects stall between a demo that impresses and a system that ships. The model works in a notebook; the integration, the guardrails, and the team's trust don't. Banao engineers the last 80% — RAG pipelines, fine-tuned LLMs, and AI agents that run in production, not just in pitch decks.
You've seen the demos. The hard part is everything after — grounding the model in your own data so it stops hallucinating, wiring it into the tools your team already uses, and proving it's safe enough to sit in front of a customer. That last 80% is where most vendors hand you a notebook and walk away. Banao ships generative AI into production: e-commerce personalization for FootLocker, an AI knowledge platform for the UAE's Majra, an adaptive learning system for Studylab AI. And we run the same stack across our own 300-person operation before it reaches you.
Each capability below is something we've put into production for a client — not a slide. Here's what we build, and how we make it hold up under real traffic.
Talk to a Banao AI engineer
Customer-facing AI assistants that don't go off-script
We build retrieval-grounded assistants that answer from your own product data, policies, and tickets — not the open internet — so replies stay accurate and on-brand. Every deployment ships with guardrails, fallback-to-human routing, and conversation telemetry, so you can see exactly what it's telling your customers.
Turn unstructured text into decisions
Customer reviews, support tickets, survey responses, call transcripts — we build LLM pipelines that extract themes, sentiment, and entities at scale and push them into the dashboards your team already reads. The output is a decision, not a data dump.
Catch anomalies before they cost you
We build models that watch transaction and event streams in real time and flag fraud and anomaly patterns the moment they appear. Banao's delivery history includes security and surveillance AI for enterprise clients like CP Plus — putting this work in production is in our track record, not on our roadmap.
Forecast demand you can actually plan against
We build demand-forecasting and inventory-optimization models that read your sales history, seasonality, and external signals to cut both stockouts and overstock. In retail and e-commerce, engagements like these typically return 300–800% over 18 months — we scope the target with you before we build.
Let customers search the way they think
We add visual and voice search to your storefront so customers can find products by photo or by asking — widening the funnel for everyone who never types the right keyword. Built on your catalog, tuned to your taxonomy.
Production content, not generic filler
We build content systems on fine-tuned, retrieval-grounded LLMs that draft blogs, product descriptions, and campaign copy in your voice and on your facts. Vikaas, our own demand-gen engine, runs on this stack across Banao's marketing — you're not the test case.
On-brand visuals at the speed of a prompt
We deploy custom-trained text-to-image and text-to-video models that generate product imagery, ad creative, and short video at scale — trained on your brand assets so the output is usable, not just impressive.
GPT wired into the tools you already run
We integrate OpenAI, Claude, and open-weight models into your apps, websites, and internal workflows — with the orchestration, prompt management, and cost controls that keep an LLM feature from becoming an unbounded bill. Model-agnostic by design, so you're never locked to one vendor.
What changes in your operation within six months
Audit and analysis at machine scale
Generative AI reviews every record, not a sample — surfacing unusual patterns, anomalies, and risk areas across data volumes no human team could cover. What took an analyst a week runs continuously in the background.
Hours returned to your team
We automate the repetitive document, data-entry, and triage work that eats your team's day, so their hours go to judgment work instead of copy-paste. Most clients measure this in hours reclaimed per employee, per week.
Marketing personalized to each customer
AI-generated content and recommendations tuned to individual behavior, not broad segments — the same approach that lifts conversion across retail and e-commerce. We run this stack on Banao's own demand generation first.
Forecasts you can stock and staff against
Models that read terabytes of sales and behavioral data to project demand with an accuracy spreadsheets can't reach — cutting stockouts, overstock, and the working capital trapped in either.
Recent Work
FootLocker
FootLocker's catalog was deep, but shoppers struggled to surface the right product and generic recommendations weren't converting. Banao integrated a generative AI recommendation and personalization layer directly into their commerce platform, grounding suggestions in real browsing and purchase behavior rather than static rules. The result is discovery that feels personal at scale — the kind of personalization engine that, in retail, typically pays back several times over within 18 months.
Studylab AI
Studylab needed to teach thousands of students at once without losing the personalization a one-on-one tutor provides. Banao built an AI-powered adaptive learning platform that profiles each student's strengths, gaps, and pace, then reshapes the content path in real time. Every learner gets a curriculum that adjusts to them — turning a static course library into measurable learning outcomes.
Majra
Majra, the UAE's national CSR and sustainability authority, was managing knowledge, communication, and training across a growing organization with tools that didn't talk to each other. Banao built an AI-driven enterprise platform that unifies knowledge management and learning behind a single intelligent interface staff can actually search. For a national authority, that keeps institutional knowledge consistent and findable as the team scales.
Delivering production-ready generative AI solutions through a structured, scalable, and outcome-driven approach.
We understand your business goals, challenges, and identify high-impact generative AI use cases. Most vendors jump straight to a model; we start by ruling out the use cases where AI won't earn its keep — so you don't pay to automate the wrong thing.
We gather, clean, and structure relevant data to ensure accurate and reliable AI model performance. This is where most generative AI projects quietly fail. We treat your data quality as the project, not a prerequisite.
We select, customize, and fine-tune LLMs to align with your domain and business requirements. We benchmark open, hosted, and fine-tuned options against your actual task — not the model that's trending — so you don't overpay for capability you'll never use.
We integrate AI into your existing systems, workflows, and applications with scalable architecture. A model that isn't wired into the tools your team already uses never gets adopted, so we design for the workflow, not the demo.
We rigorously test, refine prompts, and optimize performance for accuracy, speed, and efficiency. We test against adversarial and edge-case prompts before launch, because the failure you don't find, your customer will.
We deploy AI solutions and continuously monitor, improve, and scale them based on real-world usage. LLM behavior drifts and costs creep, so we instrument every deployment with telemetry to catch regressions and runaway spend early.
What our clients say
RaviKant
CEO and Co-founder, Happimynd
Jabez Zinabu
CEO, LeapifyTalk
Parth Sethia
Product Manager, O-line-O
Spot on delivery!
Banao has helped shape up Happimynd into a creative design and exceptional development. The technical capabilities in web development at Banao are commendable.
Here is what you will get for submitting your contact details.
45 minutes of free consultationA strict non-disclosure agreementFree market & competitive analysisSuggestions on revenue models & planningDetailed feature list documentNo obligation proposalAction plan to kick start your projectWe built a generative AI demo that worked — why did it fail in production?
Almost always one of three things: the model hallucinates because it isn't grounded in your data, it isn't integrated into the tools your team actually uses, or no one trusts it because there's no telemetry showing what it does. Banao's practice is built around that last 80% — RAG grounding, system integration, guardrails, and monitoring — which is exactly the work demos skip.
How do you stop the model from making things up?
We ground responses in your own data with retrieval-augmented generation (RAG), so the model answers from your documents, policies, and records instead of its training data — and we add citations, confidence thresholds, and fallback-to-human routing for anything it can't answer reliably.
Will our data be used to train someone else's model?
No. We architect deployments so your data stays in your environment, use enterprise model endpoints that don't train on your inputs, and back it with end-to-end encryption, audit trails, and regular security reviews. You own the system, the data, and the model weights where applicable.
Do we need to replace our existing systems to add generative AI?
No. We integrate via APIs and connectors into your current stack — CRM, support desk, data warehouse, or app — so the AI layer sits on top of what you've already built.
We tried AI before and it didn't deliver. Why would this be different?
Fair — most failed projects we inherit broke on data quality, integration, or trust, not the model itself. We diagnose which of those sank the last attempt before proposing anything. We've also broken and fixed our own AI systems internally — that scar tissue is part of what you're hiring.
Can you work with our existing cloud and tech stack?
Yes. We're stack- and cloud-agnostic — AWS, GCP, or Azure; OpenAI, Claude, or open-weight models. We choose based on your constraints and cost profile, not a vendor partnership.
Should we just build this in-house?
If you have a senior ML team with spare capacity, sometimes yes. In practice, in-house GenAI builds run 12–18 months because the talent is hard to hire and the work competes with day jobs. We compress that to weeks because it's our day job — and many clients come to us about six months into an in-house attempt.
What does a generative AI engagement cost, and how long does it take?
Most production builds land in our Growth tier ($80K–$250K); focused integrations start lower, enterprise platforms above. A production pilot typically ships in 8–12 weeks. We lock the exact number after a short scoping call — before that, any quote is fiction. Book a 45-minute call and we'll size yours.
