Most enterprise analytics stalls in a dashboard nobody trusts — the forecast misses, the numbers don't reconcile, and decisions go back to gut feel. The hard part was never the model; it's grounding it in messy operational data and keeping it accurate after launch. Banao builds AI analytics and BI systems that survive contact with real data — validated forecasts, real-time KPI dashboards, and decision-intelligence tooling — engineered by a 300-engineer team across India, UAE, UK, and US, and run on our own operation first.
Most teams already have dashboards. What they don't have is a forecast the planning team will commit to, or an insight that reaches a decision before the quarter closes. Banao bridges that gap — we engineer the data pipelines, models, and dashboards as one production system, with forecast accuracy tracked every cycle. It's the same decision-intelligence discipline we run across Banao's own 300-person operation, and that we've shipped for clients from PhonePe in payments to RAK Ceramics on the factory floor.
From data strategy to deployed dashboards and forecasting models, we build the full analytics stack and the engineering around it — not a notebook your team won't open. Banao runs the same telemetry and decision systems across its own 300-person operation before they reach a client.
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Real-Time Visibility Into the Metrics That Move Revenue
We build live KPI dashboards wired directly to your warehouse and operational systems, so leadership sees revenue, margin, and pipeline shift as it happens — not in a deck a week later. Built on the same telemetry discipline Banao runs across its own 300-person operation.
Forecasts Your Planning Team Can Actually Commit To
We build demand, sales, and inventory forecasting models validated against your historical actuals, with forecast accuracy tracked every cycle so planners can trust the number. We ship a working model in 6–8 weeks, then retrain it as new data lands.
Scenario Modeling That Replaces Gut-Feel Decisions
We build decision-support systems that let your team test pricing, capacity, and investment scenarios against modeled outcomes before committing budget. Every recommendation ships with an explainability layer, so finance and ops can see why the model points where it does.
Know Which Customers Drive Margin — and Which Churn
We build segmentation and behavior models on your first-party data to surface who's about to churn, who to upsell, and where revenue concentrates. Built on customer-analytics patterns we've shipped for retail and marketplace operations at the scale of Myntra and Swiggy.
Find the Bottlenecks Costing You Throughput
We instrument your operational data to expose process inefficiencies, idle capacity, and failure points that spreadsheets hide. For RAK Ceramics, Banao turned raw factory-floor production data into defect and throughput insight.
Analytics Built Around Your Data, Not a Template
When off-the-shelf BI can't model your business, we build custom analytics pipelines tuned to your data, workflows, and governance requirements. Stack-agnostic by design — we integrate with Power BI, Tableau, Snowflake, and BigQuery instead of forcing a rip-and-replace.
Industries Benefiting from AI Analytics & BI
Retail & E-commerce
Predictive demand and inventory models that cut stockouts and surface what each customer buys next — the analytics patterns behind retail operations at the scale of Myntra and Swiggy.
EdTech & Learning
Engagement and outcome models that flag at-risk learners early and personalize pathways before completion rates slip.
Healthcare & Life Sciences
Forecasting for patient demand and resource allocation that turns clinical and operational data into decisions — built to the data-governance standards regulated providers require.
Banking & Finance
Risk, credit-scoring, and real-time analytics models with an explainability layer, shipped to the compliance bar Banao meets for payments clients like PhonePe.
Manufacturing & Supply Chain
Production, demand, and bottleneck analytics that turn factory-floor data into throughput — the work behind Banao's industrial builds with RAK Ceramics.
Telecom & Media
Churn prediction and usage analytics that show which subscribers leave and which content holds them — before it shows up in the quarterly.
Recent Work
NLP Solutions | Custom AI/ML Models
Ai Contract Review
Legal teams were spending days manually reviewing contracts, and slow turnaround was holding up deals. Banao built an AI contract-review system with clause extraction and risk flagging grounded in the client's own playbook, so the model surfaced real issues instead of inventing them. Review cycles dropped from days to minutes while catching the high-risk clauses reviewers were missing.
AI Consulting | Responsible AI
Justicia Lab AI
Immigrant-justice nonprofits wanted to adopt AI responsibly but had no safe way to evaluate it. Banao designed and built the first non-profit AI lab for the sector, with governed access so legal-aid teams could test tools without risking sensitive case data. It gave advocacy organizations a path to AI adoption that met their ethical and data-handling bar.
Predictive Analytics | Analytics & BI
Supply chain
A logistics and manufacturing enterprise was flying blind across a fragmented supply chain, with demand and inventory decisions made on lagging spreadsheets. Banao built an end-to-end Supply Chain Intelligence Platform unifying the data and layering predictive demand and bottleneck analytics on top. Planners moved from reactive firefighting to forecasting disruptions before they hit.
Data Tagging System
A data-driven enterprise was drowning in manual content categorization across millions of records, slowing every downstream search and report. Banao built an AI and NLP tagging pipeline that classified content automatically, with a human-review loop to keep accuracy high. Document processing sped up and search relevance improved across the full corpus.
Flow Legal AI
Law firms and in-house teams were bottlenecked on manual document review, drafting, and summarization. Banao built an end-to-end AI solution that automates review and contract drafting, grounded in legal source documents to keep outputs defensible. It compressed routine legal work from hours to minutes without sacrificing accuracy.
Understand business objectives, KPIs, and user requirements. Map features and identify AI analytics opportunities to enable predictive insights and intelligent dashboards for better decision-making. Why this matters: most analytics projects fail here — we map the actual KPIs and audit data quality before modeling, so we don't build a beautiful model on numbers that don't reconcile.
Select and integrate AI and machine learning models for predictive analytics, KPI forecasting, recommendation engines, and intelligent reporting. We pick the simplest model that meets the accuracy target and wire it into your BI systems and app backends from day one. Why this matters: a standalone notebook your team can't deploy is where most analytics work dies — we engineer for production from the start.
Design AI-driven dashboards and BI interfaces with real-time analytics, predictive insights, and intuitive visualizations to maximize data clarity, usability, and business impact. Why this matters: a dashboard only drives decisions if people open it, so we prototype with your actual users and reconcile every figure to source before build.
Build scalable web and mobile analytics applications, embedding AI models for predictive insights, KPI tracking, and intelligent data visualization for actionable business intelligence. Why this matters: we engineer the pipelines and apps to production standards — the failure mode we avoid is the demo that breaks the moment it meets live data volume.
Validate AI predictions, data accuracy, KPI calculations, and dashboard usability. Optimize analytics models, performance, and data-driven recommendations for maximum business value. Why this matters: we validate forecasts against your historical actuals and set an accuracy gate, so the model ships only when the numbers hold up — not on faith.
Deploy AI analytics applications, monitor performance, retrain models with new data, and continuously improve predictive insights, KPI dashboards, and actionable business intelligence for sustained growth. Why this matters: models drift as data changes, so we monitor accuracy every cycle and retrain — the system stays trustworthy long after launch instead of quietly decaying.
What Enterprise Teams Say About Banao Analytics
Ritika Malhotra
COO, SmartSupply
Thomas Lee
CTO, FinEdge
Forecasts our planners actually commit to
Banao's demand-forecasting models replaced our spreadsheet guesswork. Accuracy is tracked every cycle, and our inventory and supply-chain decisions now run on numbers we trust.
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 tried AI analytics before and it never made it past a pilot. Why would this be different?
Usually the pilot worked and production didn't — the model wasn't grounded in messy operational data, and accuracy drifted after launch. We engineer for that failure mode: every model is validated against your historical actuals and monitored each cycle. Banao has broken and fixed its own analytics systems internally since 2017 — that operational scar tissue is what you're hiring.
How do you stop the models from producing misleading forecasts or wrong dashboards?
We ground models in your own data, validate forecasts against actuals before anything ships, and attach an explainability layer so finance and ops can see why a number moves. Dashboards reconcile to source systems, and accuracy is tracked continuously — if a model drifts, we catch it, not your board.
Who owns the data, the models, and the dashboards you build?
You do — 100%. Custom code, trained models, pipelines, and dashboards are all yours. For regulated data we work inside your VPC or a secure clean room, sign DPAs, and can train models without our team ever touching raw customer records.
Should we just build this in-house?
For some pieces, yes. But hiring senior analytics and ML engineers takes 6-9 months, and the project competes with their day jobs. Banao ships a working model in 8-12 weeks because this is our day job — your team learns from our engineers and owns the codebase from day one.
Can this integrate with our existing BI tools, warehouse, and ERP/CRM?
Yes — we're stack-agnostic by design. We integrate with Power BI, Tableau, Looker, Snowflake, BigQuery, and your existing ERP/CRM rather than forcing a rip-and-replace. An integration audit is the first week of any engagement.
How accurate will the forecasts be, and how do we measure success?
We don't promise a magic accuracy number before seeing your data. We benchmark against your current process, set a target as part of scope, and track forecast accuracy every cycle. Success is measured in your metrics — stockouts, forecast error, decision latency — not features shipped.
What does an AI analytics engagement cost and how long does it take?
Pilots typically run $20K-$80K and ship in 6-8 weeks; full enterprise analytics platforms run $80K-$250K+ over 2-3 months depending on data complexity and integrations. We scope to a fixed range after a short discovery — book a 45-min scoping call and we'll map it to your data and KPIs.
