Banao — InterviewGod
Screens engineering candidates before a recruiter opens a resume — the same model Banao runs on its own ~300-person hiring pipeline.
··%first-round screening
··hrsrecruiter time saved
AI · Machine learning
We score our own engineering hires on a model we trained — before we build one for you.
Machine learning development for forecasting, classification, recommendation, ranking, and fraud detection — built to the standard we hold our own hiring to.
Book a Discovery Sprint →
The first call is free · 45 minutes · no obligation
02 · What We Build
One lifecycle, five stages — not a notebook that stops after training
The same pipeline that runs InterviewGod's models internally: data in, model trained, validated, shipped, kept accurate.
01
Data
Feature engineering & data pipelinesThe plumbing a model needs before it can learn anything.
02
Model
Custom model developmentBuilt for your decision, not a generic template.
ForecastingPredicts what happens next.
Recommendation & rankingSurfaces the next best action.
Fraud & anomaly detectionCatches what shouldn't be there.
Classification & scoringSorts and routes at scale.
03
Validate
Evaluation & explainabilityChecked against a baseline before it ships.
04
Ship
Deployment & servingBehind an API, not left in a notebook.
05
Operate
MLOps & retrainingRetrains as your data moves.
Drift monitoringFlags accuracy loss the day it starts.
03 · METHODOLOGY
How we actually build a model that earns its place in production
Four checkpoints, in order. Skip one and the model works in the notebook and fails in production.
-
01
Start from the decision, not the algorithm
We write down the decision the model has to change before we pick a technique. No decision, no model.
-
02
Baseline before sophistication
A simple model has to beat the current process first. If it can't, a complex one won't either.
-
03
Validation that survives contact with reality
Tested against data the model has never seen, split the way production will actually split it.
-
04
The same transforms at train and serve
The exact feature pipeline used in training runs in production. No drift between the two.
04 · Failure Modes
Why most machine-learning projects never make it past the notebook
The pattern repeats across teams, tools, and budgets. Four points in the pipeline where it breaks.
01
It was a data problem wearing a model costume
The pipeline shipped clean. The data feeding it was never validated — gaps, drift, and labels no one checked. Most model failures trace back here, not the algorithm.
02
No one owned the decision
The output sat in a dashboard no one acted on.
03
Accuracy chased past the point of value
A metric improved. The business decision it fed didn't.
04
No plan for after launch
No monitoring, no retraining trigger, no owner — so it drifted.
05 · Post-launch care
What most AI vendors leave behind.
Keeping a model accurate after launch is the part that usually turns into a separate quote. Here it's the build.
Left to you, later
–
Uptime is checked. Accuracy is assumed.
–
Retraining, if it happens, is a fresh statement of work.
–
No tracked path back to the last version that worked.
–
Only the vendor's engineers know how it runs.
Ships with every model
✓
Inputs and outcomes monitored — not just uptime.
✓
Retraining runs as a pipeline, on a schedule and a trigger.
✓
Every version tracked, with a safe way to roll back.
✓
Hand-off documentation your own team can run from.
06 · What Happens After Launch
A model that's still accurate a year after we're done.
Without monitoring
- Accuracy drifts silently until someone notices in production
- Retraining is a fire drill, not a pipeline
- The team can't run it without the vendor on retainer
With Banao
- Monitoring and rollback built in from day one
- Retraining runs as a versioned pipeline
- Your own engineers own it at hand-off — no lock-in
Book a Discovery Sprint →
Find out what it takes to keep a model accurate after we leave.
07 · Receipts
Dogfooded · Live
Live
What's already deciding things — including ours.
Deployments are live. Metrics below are finalized in the client metrics pack.
E-commerce marketplace (anonymized)
A recommendation and ranking model live on an e-commerce marketplace, surfacing items it was trained to rank.
··%lift, items/order
··%reduction, stockout
08 · Dogfooding
We run our own company on the models we sell
InterviewGod scores every engineering candidate we hire. Vikaas runs our outbound and lead scoring. Vidya paces upskilling for our own engineers. All three are live inside the same ~300-person operation we're asking you to trust a model with.
"We do not sell you software we hope works. We sell you the software we depend on." — Banao Technologies
Internal · Live Models
InterviewGod
Hiring — scores candidates against a model trained on our own hiring data.
Vikaas
Outreach — sequencing and lead scoring in our own revenue pipeline.
Vidya
Upskilling — paces training for our own engineers.
09 · Where We Deliver
Where we build and deploy machine learning
One delivery model, run from five markets — so the team that builds your model is the team accountable for where it runs.
Delivery hub
Banao engineering & delivery teams
UAE
GCC & UAE
Dubai office. RAK Ceramics and Majra in production.
KSA
Saudi Arabia
Delivered from Dubai. Data-handling terms agreed per engagement.
US
United States
California office. Clients including FootLocker.
UK
United Kingdom
Cambridge office, supporting UK and EU engagements.
IN
India
Bengaluru and Chandigarh offices, where Swiggy, Myntra, PhonePe, Times Internet, Indian Oil, HCL, and CP Plus run in production.
Building since 2016
10 · Honest disqualifiers
When machine learning is the wrong tool
Five conditions that stop us before we scope a build. If any apply, we'll say so — in writing — before a build quote exists.
01
You do not have the data
If the outcome hasn't happened often enough inside your own systems, there's no pattern to learn from yet — collect it first, build later.
02
A rule already works
If a fixed threshold or lookup table already gets the right answer, a model adds cost and drift risk without adding accuracy.
03
No one will act on the prediction
A model nobody reads or acts on is a maintenance liability, not a decision tool. We check for an owner before we check the data.
04
You need an explanation more than a prediction
When the "why" matters more than the "what" — audits, appeals, compliance — a model that can't show its reasoning is the wrong shape.
05
The pattern changes faster than you can retrain
If the underlying behavior shifts weekly and retraining runs monthly, the model is stale before it ships.
None of these apply to you? Your data can probably support a model.
Book a Discovery Sprint
11 · Process
We don't quote a build until we've proven one is worth it.
Most ML engagements start with a build quote before anyone has looked at the data. We reverse the order — and we'll tell you if there's nothing to build.
01
AI Discovery Sprint
Two weeks, fixed price. We test whether your data supports the decision the model would have to make — forecasting, classification, recommendation, ranking, or fraud detection — before any build quote exists. If it doesn't hold up, that's the deliverable, in writing.
02
Build
Decision-first, not algorithm-first. Every model is checked against a baseline and audited for leakage before it ships — the same bar we hold our own hiring models to.
03
Production and retraining
Monitoring, retraining, and versioned rollback are built in from day one. What we hand off is a system your own engineers can run — not one that needs us on retainer.
12 · FAQ
Frequently asked questions
What comes up before a Discovery Sprint — answered before you have to ask.
Book a Discovery Sprint01What is machine learning development?
Building models that forecast, classify, recommend, rank, or catch fraud on your own data — and shipping them into production, not a slide deck.
02How is machine learning different from generative AI or an LLM?
An LLM generates text. These models score, predict, or rank a specific outcome from your data — a demand number, a fraud flag, a ranked list — and that output feeds a system, not a chat window.
03How much data do we need to train a custom model?
The Discovery Sprint answers this on your data, before any build begins — not a rule of thumb applied from outside.
04How long does it take to build and deploy an ML model?
Weeks, not quarters — validated in a fixed-price two-week Discovery Sprint, then built on the timeline that sprint sets.
05What's the difference between a model in a notebook and one in production?
A notebook model is a claim. A production model is monitored, retrained, and answers for a real decision — the standard we hold every model to, including the ones running our own hiring and outreach.
06How do you stop a model from getting worse over time?
Monitoring, a retraining pipeline, and versioned rollback are built in from day one — not added after the first failure.
07Can you build on our existing data and cloud?
Yes — the Discovery Sprint starts from what you already have, not a rebuild of your stack.
08How do we prove ROI before committing budget?
The Discovery Sprint is fixed-price and tells you, in writing, whether your data supports the model — before a build quote exists.
09Do you only build the model, or maintain it too?
Both — monitoring and retraining ship with the build, and you own the system outright, with no retainer required to keep it running.
10Which use cases and industries do you build ML for?
Forecasting, classification, recommendation, ranking, and fraud detection — for teams like Swiggy, Myntra, PhonePe, and RAK Ceramics.
13 · RELATED
Where to go from here
2 WEEKSSprint length
FIXEDPrice, no overrun
0Obligation to build
Before we build, we tell you if one's worth building
A fixed-price, two-week Discovery Sprint gives you a written answer — forecasting, classification, recommendation, ranking, or fraud detection — before any build quote exists.
Book a Discovery Sprint →