Predictive analytics · AI business intelligence
Banao builds an AI intelligence layer over your data: one that flags the metric that moved, traces the cause, surfaces the next question worth asking, and puts the recommended action in front of the person who can act on it — without waiting for an analyst to run the query.
We wire the intelligence into the system your team already uses, so insight becomes a decision in the same morning it becomes available, not a chart someone reads on Thursday for a meeting that happened Tuesday.
Banao— Vikaas monitors Banao's own pipeline health and flags anomalies before the weekly ops review.
A standard BI tool answers the question you already knew to ask. An AI intelligence layer answers the question you didn't know was urgent.
Models that separate a real signal from noise across every KPI and notify the right person before the weekly report catches it — no analyst required to notice something is wrong.
Business users ask a data question in plain text and get an answer, not a request to raise a ticket. The query is validated, the SQL is generated and reviewed, and the result is returned with the logic visible.
Each morning's data is summarized in plain language — what changed, by how much, against what baseline, and what to watch — delivered to the people who decide, not the people who build dashboards.
When revenue drops or a KPI spikes, the system traces the change back through the contributing factors — region, product, channel, cohort — and surfaces the most likely cause rather than leaving the analyst to guess.
We model where a metric is heading given current trajectory, flag KPIs likely to breach a threshold before the month closes, and give planners time to act rather than time to explain.
Analysts and operational teams explore data without a data-engineering dependency — with semantic guardrails that keep the definitions consistent and access controls enforced so a wrong query can't produce a misleading number.
A curated view of the three or four decisions a leadership team makes each week, pre-loaded with the numbers, context, and recommended action — rather than a 40-tab dashboard that takes 20 minutes to read.
Where existing data infrastructure is too slow or inconsistent to support intelligence, we build the clean semantic layer, the pipelines, and the metrics store that let every downstream tool agree on the same number.
A BI dashboard is a question you already knew to ask, built by an analyst, and delivered a day or a week after the data arrived. By the time someone reads it, the decision window has often closed. That is not a BI tool problem — it is a coverage problem. A team of analysts can answer a finite number of questions; the data is raising an infinite number.
An AI intelligence layer changes the economics. Instead of waiting for someone to notice a metric moved, the system scans every signal, ranks by business impact, and surfaces the three things worth a human's attention today. Analysts stop fielding status queries and start working on the problems the system can't resolve alone.
Standard BI answers the question you asked. AI BI asks the question you should have asked — automatically, at the cadence your data updates, across every metric at once.
A metric moving is not intelligence; knowing why it moved is. Root cause tracing turns a number into a signal a manager can act on, rather than a starting point for a three-hour investigation.
We build toward the decision the intelligence is supposed to drive. If the alert doesn't reach the person who can act on it, and doesn't include what to do, it is a notification, not intelligence.
Most organisations have already committed to a BI platform — Power BI, Tableau, Looker, Metabase, or a custom stack. We do not replace what is working. We add the AI layer on top: the anomaly detection, the natural language interface, the insight summarisation, and the decision routing that the platform was not designed to do alone.
The integration surface is the semantic layer — the agreed definitions of metrics and dimensions that let AI and the existing platform agree on the same number. Where that layer is missing or inconsistent, we build it as the foundation of everything else. Where it already exists, we sit on top of it and extend it rather than building a parallel truth.
We connect to the data you already have — Snowflake, BigQuery, Redshift, or on-premise — rather than requiring a migration before intelligence can start.
Revenue cannot mean two things. We build or extend the semantic layer so every downstream consumer — the AI layer, the existing dashboards, the NLQ interface — agrees on what each metric means and how it is calculated.
Insights land in the tool your team already reads — Slack, email, the existing BI platform, or a purpose-built executive digest — not in a separate application your team has to remember to open.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified against live outcomes. The deployments are real.
Banao — Vikaas
Vikaas monitors Banao's own pipeline health, flags stage-level anomalies, and surfaces the root cause before our ops lead opens the review. The questions that used to consume the first half of the meeting are answered before anyone sits down.
National e-commerce retailer (anonymized)
We built an AI insight layer over the existing data warehouse that generates a daily plain-language summary of what changed, the top contributing factors, and the three things leadership should watch. Manual Monday-morning reporting was retired within four weeks of launch.
Banao is a ~300-person engineering company. Vikaas monitors our own pipeline, flags anomalies in our demand funnel, and puts the context in front of the person who can act on it — before any human has had to notice something was wrong. When the system misses something, it is our own ops review that finds out first.
Building AI business intelligence that we depend on for our own planning means we have already found the failure modes a demo never reveals. The discipline that keeps Vikaas honest in our operation is the discipline we carry into yours.
Bangalore and Chandigarh hold our analytics delivery bench, so an AI BI build starts in weeks and stays close to the engineers who ship it. We build to the DPDP Act and deploy to your cloud or on-premise environment where data residency requires it.
From Dubai we build AI intelligence layers for retail, real estate, and industrial clients across the GCC — including work with RAK Ceramics — and keep data inside UAE boundaries where the PDPL and client policy require it. Gulf business cycles and reporting cadences are built into the delivery.
For US clients we build to SOC 2 controls, with the model documentation, access logging, and audit trail that procurement and risk teams require of any system that processes sensitive business data. California and New York operations are our primary US focus.
From Cambridge we support BI modernisation and AI intelligence builds under UK GDPR, where a system that automates a business decision has to be explainable and its outputs auditable, not a black box no one can defend to a regulator.
Adding an AI layer to broken data is more expensive than fixing the data first. We will tell you which you need before you commit to either:
We do not quote an intelligence layer from a brief. We identify the highest-value anomaly or insight gap in your current data, build it, and measure whether it changes a decision before we expand.
We audit your current data and BI landscape, identify the one anomaly or insight gap with the clearest decision impact, build a working prototype over your actual data, and hand back the design, the ROI maths, and an honest read on what the full build would cost and take. Yours to keep either way. Sprint cost credited against the build if you proceed.
We build the semantic layer, the AI models, the integration into your existing stack, and the delivery mechanism — dashboards, digests, alerts — that puts intelligence in front of the right person at the right time.
We monitor accuracy, retrain when data distributions shift, and extend the intelligence layer to additional metrics and teams as the first use case earns its place. Every alert is tracked against what the team actually did with it.
What is AI business intelligence and how is it different from standard BI?
Standard BI answers questions you already know to ask — by building a dashboard that reports on a metric, on a schedule. AI BI monitors data continuously, flags what changed without being asked, traces the cause, and puts the recommended action in front of the person who can act. The difference is between a report and a signal.
Can you add an AI layer to our existing Power BI, Tableau, or Looker setup?
Yes. We extend existing platforms rather than replacing them. The AI capabilities — anomaly detection, NLQ, insight summarisation — sit on top of your current stack and connect to the same data warehouse, so your existing dashboards and reports keep working and the AI layer adds to them.
What data infrastructure do we need to get started?
A data warehouse or operational database your reporting already runs against — Snowflake, BigQuery, Redshift, Postgres, or on-premise equivalents. If metric definitions are inconsistent across tools, we build a semantic layer as the foundation. If the data is genuinely missing, we scope that work first in the Discovery Sprint.
How does natural language querying work — how do you prevent wrong answers?
The NLQ layer translates a plain-text question into a validated SQL query, runs it against the semantic layer where metric definitions are locked, and returns the result with the underlying query visible. Business users can see exactly what was asked of the database. Queries that could return misleading results are flagged rather than answered silently.
What is root cause analysis in an AI BI context?
When a metric moves — revenue drops, a conversion rate spikes — the system attributes the change across the available dimensions: region, product, channel, cohort, time window. It surfaces the factors that explain most of the variance, ranked by contribution, so an analyst or manager sees the probable cause rather than starting a blank investigation.
How are insights delivered — do we need a new tool?
Insights land where the team already works: a daily digest in email, an alert in Slack, a panel inside the existing BI platform, or an executive dashboard in a tool you already use. We connect to your existing communication stack. We do not require your team to adopt a new application they have to remember to open.
How long does it take to see a working AI BI system?
The Discovery Sprint runs two weeks and produces a working prototype over your actual data. A focused first build — anomaly detection and alerting on a core metric set, with delivery into an existing tool — typically runs 6–8 weeks. Banao's ~300-engineer bench means work begins in weeks rather than months.
How do you handle data residency and security for sensitive business data?
We deploy to your cloud environment and keep data in the region your policy or regulation requires — India, UAE, UK, or US. Access controls, audit logging, and role-based permissions are built into the intelligence layer from the start. The data never leaves your agreed boundary.
Bring the KPI someone reads every Monday morning and decides what to do about. In 45 minutes we will tell you whether an AI intelligence layer can surface that signal automatically — and what a build would take.
Book a 45-min scoping call
