Machine learning · Recommendation systems
Generic collaborative filters converge on popular items — the same bestsellers appear in every user's feed, the long tail goes dark, and the system optimizes for clicks on items that would have sold anyway. That is not recommendation; it is ranking the obvious.
Banao builds recommendation systems trained on your interaction data, wired to your catalogue, and tuned to the business metric you actually care about — basket size, return rate, time-to-next-purchase — not the offline accuracy number that looks good in a notebook and disappears under real traffic.
Banao — InterviewGod— ranks every engineering applicant against role requirements, on a model trained on Banao's own hiring outcomes.
A recommendation engine in production is a retrieval layer, a ranking model, a feature store, an A/B testing harness, and the monitoring to catch when it has drifted — we build the whole system, not just the model.
User-user and item-item collaborative filters for interaction-rich catalogues; content-based models for cold-start items and new users — combined in a hybrid that uses each where it has signal.
Two-tower neural retrieval models that turn items and users into dense vectors, so a single approximate-nearest-neighbour lookup finds candidates from a catalogue of millions in milliseconds.
A ranking layer that orders retrieved candidates by the metric you sell on — revenue per impression, add-to-cart rate, or margin — with features the retrieval layer never saw.
The real-time and batch pipelines that turn raw click, purchase, and session events into the features the model expects — consistently, so training and serving see the same signal.
Strategies for new users with no history and new items with no interactions — content embeddings, popularity priors, and fallback policies that keep the long tail visible.
An online evaluation harness that splits traffic, measures the metric you defined, and tells you whether the new model won — with enough statistical power to trust the result.
Serving layers — pre-computed, real-time, or hybrid — that return ranked results within your latency budget, with a cache and fallback so a model failure never blanks a feed.
Monitors on feature distributions, prediction spread, and downstream business metrics that alert before a drift becomes visible to users — and a retraining schedule that keeps the model current.
Every recommendation system trained on historical clicks has the same structural bias: items that have been shown more have been clicked more, so the model scores them higher, so they get shown more again. The feedback loop is implicit in the training data, and a model that does not actively correct for it will converge on a short list of bestsellers within weeks of launch.
The problem compounds when the training objective — click-through rate, or AUC on held-out interactions — is not the business objective. A user who clicks a cheap item and never returns is a win in the training data and a loss in the P&L. Banao builds systems where the ranking objective matches the metric you manage: basket size, second-purchase rate, subscriber retention, or margin per session.
We weight training examples by the inverse of their exposure probability, so the model learns from what users preferred — not from what the previous system chose to show them.
A post-ranking pass that enforces category spread, novelty floors, and freshness constraints — so a user who buys running shoes is not served ten more pairs of running shoes for a month.
We wire the ranking objective directly to the metric in your data warehouse — margin, LTV signal, return rate — so the model has an incentive to surface the item that earns, not the item that was previously popular.
Scoring every item in a large catalogue for every user in real time is not tractable at production latency. The standard solution is a two-stage pipeline: a retrieval stage that generates hundreds of candidates in milliseconds using approximate nearest-neighbour search over learned embeddings, and a ranking stage that scores only those candidates with a richer feature set and the business-metric objective.
The split matters not just for latency but for model quality. The retrieval model can afford to be recall-focused — miss as few relevant items as possible. The ranker can afford to be precise — score the candidates it receives in the right order. Conflating the two stages means sacrificing one for the other. We design them separately and connect them cleanly.
Separate encoder networks for users and items, trained with contrastive loss, stored as index vectors — a dot product or FAISS lookup finds the top-N candidates for any user without scoring the full catalogue.
The ranker sees the candidate item, the user context, the session signals, and the real-time features the retrieval model never had — cross-feature interactions, price sensitivity, inventory state.
We test the two stages together as a system, not in isolation — because a retrieval model with 95% recall and a ranker that reverses the order still produces a bad recommendation.
Metrics are marked pending until the client has approved the figure for publication.
E-commerce client (anonymized)
Built a two-stage retrieval-ranking recommendation engine replacing a popularity-based fallback. The system surfaces long-tail items for returning users and uses content embeddings for cold-start coverage on new products.
Media client (anonymized)
Developed a content recommendation system trained on read-depth and scroll events rather than clicks — so the model learns from engagement, not just the decision to open an article.
InterviewGod, the hiring tool Banao developed and uses for its own ~300-person engineering operation, is a ranking and recommendation system at its core: it reads an application, retrieves the role requirements, and scores each candidate against the criteria that predict success in that specific role.
Developing a recommendation system you stake your own operation on produces a different standard than shipping one and walking away — the discipline that keeps InterviewGod accurate is the discipline we bring to yours.
The Bangalore and Chandigarh delivery bench covers the full recommendation stack — retrieval, ranking, feature engineering, and MLOps — for e-commerce, fintech, and media clients across India and Southeast Asia.
From Dubai we serve GCC retailers, platforms, and financial-services companies that need recommendation systems with data residency inside UAE boundaries and compliance with UAE PDPL requirements.
For US and UK clients we build to SOC 2 and UK GDPR expectations, with the audit logging, model cards, and bias documentation their compliance teams ask for before a personalization system touches user data.
A bespoke recommendation model earns its build cost less often than the pitch implies. We will tell you before you commit a budget:
We do not start by choosing a model architecture. We start by agreeing on what 'better' means in your data warehouse.
We audit your interaction data, define the business metric the system should optimize, and prototype the retrieval or ranking approach that fits your catalogue — then hand back a design, an evaluation plan, and the data-quality findings before you commit to a build.
We train the retrieval and ranking models, build the feature pipelines, wire the serving layer, and deliver the A/B testing harness and monitoring as part of the build — not as optional extras.
We run the online experiment, read the result together, retrain on live data, and iterate on the ranking objective until the business metric moves in the direction you defined at the start.
What is a recommendation system and when does a business need one?
A recommendation system predicts what an individual user is most likely to find useful or buy next, based on their behaviour and the behaviour of similar users. A business needs one when personalizing results at scale would change a metric — basket size, session depth, return rate — and when there is enough interaction data to train a model that beats simpler alternatives.
What data do you need to build a recommendation engine?
At minimum: a catalogue (item metadata) and interaction events (clicks, purchases, ratings, or play-throughs). The more interaction signal you have, the better the model. With thin data we use content-based embeddings and popularity priors for cold-start; as data accumulates the model shifts toward collaborative signal.
How do you handle new users and new items with no interaction history?
New users receive content-based recommendations from their first few explicit signals (category, search, or browse). New items are described by content embeddings so they appear in results immediately — before any user has interacted with them. As interactions accumulate, the collaborative signal takes over.
How do you measure whether a recommendation system is actually working?
Online, with an A/B test that measures the business metric you defined at the start — basket size, session depth, or return rate — not just click-through rate. We build the experiment harness as part of the system and do not launch without a clean way to measure whether it helped.
What is the difference between collaborative filtering and a two-tower model?
Collaborative filtering factors the user-item interaction matrix into latent vectors that capture preferences. Two-tower models train separate neural encoders for users and items, then store the item vectors in an approximate nearest-neighbour index for fast retrieval. Two-tower scales better to large catalogues and handles cold-start items more naturally.
How do you keep a recommendation model accurate over time?
By monitoring the distribution of recommended items (to catch popularity collapse), the downstream business metric (to catch objective drift), and the feature distributions (to catch upstream data changes). We build a retraining schedule keyed to the rate at which your catalogue and user behaviour change.
Can you integrate with our existing e-commerce or content platform?
Yes. We serve recommendations through an API or a pre-computed cache that your platform calls — no change to your catalogue management, checkout, or CMS. We connect the event pipeline to your existing analytics infrastructure rather than replacing it.
How long does it take to build and go live?
A 2-week Discovery Sprint scopes the data, defines the metric, and produces a design. A standard retrieval-ranking system takes 8–14 weeks to build and launch in A/B. Banao's ~300-engineer bench means development starts in days, not the months a fresh hire would take.
In 45 minutes we can tell you whether a recommendation system would move your business metric — and what building one to your catalogue scale would take.
Book a 45-min scoping call
