We build AI recommendation engines for eCommerce platforms, streaming services, SaaS products, and content publishers that need real personalization. ScalaCode has shipped recommendation systems for 13 plus years. We work with clients across 45+ countries, hold ISO 9001 certification, and bring 250 plus engineers to every product team.
Whether you are launching a product recommendation system or building content discovery for a streaming app, we ship the model and the serving stack. We also handle B2B SaaS dashboards and marketplace match algorithms. We lift click-through rate, raise average order value, and grow time on site without breaking your existing stack.
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An AI recommendation engine is a software system that predicts which items, content, or actions a user will most likely engage with. It uses techniques like collaborative filtering, content-based filtering, hybrid models, and increasingly LLM-powered reasoning to rank candidate options. Product teams build recommendation engines to lift click-through rate, increase average order value, and extend session duration in eCommerce, streaming, SaaS, and content platforms.
We build user-item matrices, matrix factorization models, and ALS pipelines that learn from behavior data. These systems work for catalogs above 10,000 items and produce strong picks once you have a baseline of user signals. We tune for sparse data using regularization and implicit feedback weighting. For large catalogs we run distributed ALS on Spark with daily or hourly refreshes, depending on traffic velocity and inventory turn.
We train embedding models, set up semantic similarity scoring, and run cosine matching across product attributes or content metadata. This works when you have rich item data and want to recommend items that resemble what a user already engaged with. We use sentence transformers for text catalogs, CLIP for visual catalogs, and custom-trained encoders for structured attribute data. Embeddings get stored in a vector database for sub-50ms retrieval at runtime.
We combine collaborative filtering, content-based scoring, and your business rules into one ranking layer. This handles inventory pushes, margin targets, and editorial overrides without tearing down the underlying model. Most production systems we ship are hybrid. The signal blend gets tuned per surface. Home page, product detail page, and checkout each reward different mixes of similarity, popularity, and personal history.
We build event-driven pipelines with Redis or Faiss for low-latency serving. Recommendations update inside a session. A user who clicks a category sees a fresh ranking on the next page load instead of waiting for an overnight batch. The serving layer holds candidate generation in memory and runs the final ranking step on a request basis. We hold p99 latency under 80ms for most surfaces, including mobile feed pagination.
We design strategies for new users and new items: popularity priors, content fallbacks, contextual bandits, and onboarding signal capture. Cold-start is where most projects stall, so we plan for it from day one. For new users we lean on session context, traffic source, and a short onboarding quiz. For new items we map embeddings into the existing vector space so they can be ranked against history before behavior data accumulates.
We use RAG pipelines, prompt-engineered ranking, and LLM-generated explanations for product or content picks. This adds a why-we-picked-this layer, which lifts trust and click-through on long-form content sites and high-consideration purchases. We pair the LLM with a retrieval layer that pulls candidate items from a vector store, then the model ranks and writes a short rationale. The rationale also helps SEO on category and listing pages.
We map your data sources, traffic patterns, conversion surfaces, and goal metrics across one or two working sessions, then write a short scoping document.
We pick the algorithm family, the serving stack, the evaluation metrics, and the integration points. You see the written plan before any code ships.
We ship the model, wire it into your product, and set up logging. The experiment platform connects so the first surface can be measured from day one.
We run A/B tests, watch metrics, and retrain on a cadence that fits your data velocity. A runbook hands the system to your team for ongoing operation.
ISO 9001 certified delivery with documented QA on every sprint, code review on every pull request, and a written test plan before each release.
13 plus years of production ML work across eCommerce, media, and SaaS. Our engineers have shipped both classical models and LLM-based ranking layers in live revenue paths.
AWS Advanced Tier partner with deep practice on SageMaker, Bedrock, and Personalize. We also deploy on Azure ML and Google Vertex AI when your stack calls for it.
Rates from $13 to $25 per hour and $1,200 to $4,000 per month, billed against work delivered. No padded discovery phases. No retainers without scope.
Clutch and GoodFirms reviews from product teams across 45+ countries, with named references available on request during the scoping call.
Notebooks and prototypes differ significantly from real-time serving systems.
Pinecone, Weaviate, Qdrant, and pgvector are common production choices.
New user and new item strategies determine first-week experience quality.
Offline A/B with NDCG, MAP, and online A/B with engagement metrics should be standard.
Sub-100ms response is required for in-session personalization.
Combining collaborative, content, and business rules requires architecture skill.
Recommendation engines fail at the data pipeline, not the model.
A pod of ML engineers, data engineers, and a tech lead works as part of your roadmap. Billed monthly. Best for ongoing build and tuning across multiple surfaces, multiple product lines, or a longer measurement program.
An 8 to 12 week engagement that ships a working recommendation surface against a defined metric. Best for teams that need a first model in production before scaling investment, with a clear deliverable list and a written acceptance gate.
A focused audit and repair sprint for systems that are live but underperforming. We diagnose data leakage, ranking bugs, stale models, and broken logging, then ship the fixes inside 4 to 6 weeks. Best when an existing system needs a second pair of eyes.
| Factor | ScalaCode | Generalist Dev Shop | Boutique ML Studio |
|---|---|---|---|
| Recommendation focus | Dedicated ML pods | Occasional projects | Yes, narrow tooling |
| Hourly rate | $13 to $25 | $60 to $120 | $150 plus |
| Time to first model | 6 to 10 weeks | 12 to 16 weeks | 8 to 14 weeks |
| Stack flexibility | Open source and managed | Often locked | Often opinionated |
| Production engineering | Built-in | Add-on | Sometimes partner |
Typical recommendation engine build structures and cost bands. These figures represent observed market ranges, not ScalaCode quotes.
| Build Type | Typical Timeline | Typical Cost |
|---|---|---|
| Basic Product Recommendations | 8 to 12 weeks | $60K to $140K |
| Hybrid Recommendation System | 14 to 20 weeks | $120K to $280K |
| Real-Time Personalization Pipeline | 16 to 24 weeks | $150K to $360K |
| LLM-Powered Recommendations | 12 to 18 weeks | $100K to $240K |
| Cross-Platform Recommendation Stack | 20 to 30 weeks | $200K to $450K |
| Content Discovery for Streaming | 16 to 24 weeks | $140K to $340K |
Product recommendations, frequently bought together, cart upsell rails, personalized search ranking, and category page sorting tuned to margin and stock targets.
Content discovery rails, next-episode picks, personalized homepages, and watch-resume logic across devices and household profiles.
In-app feature nudges, dashboard widget ranking, next-best-action picks, and onboarding step ordering that adapts to role and team size.
Article rails, topic clusters, paywall conversion picks, and newsletter ranking tuned for both engagement and subscription lift.
Item recommendations, matchmaking signals, in-game purchase ranking, and live-ops offer targeting based on play session patterns.
Course recommendations, learning path picks, content sequencing, and remediation suggestions based on assessment performance.
$13-$15/hr
$18-$20/hr
$23-$25/hr
$1,200-$1,500/month
$1,800-$2,400/month
$2,600-$3,200/month
$3,200-$4,000/month
I looked around at several developers to compare costs, but they didn’t fit within my budget. Finally, I reached out to a company in India called ScalaCode. We set up several online meetings over a couple of weeks and came up with an app that did exactly what I wanted within my budget. I can confidently say that ScalaCode has been an excellent choice for me.
Ruddy McKenzie
Founder of RM EPOSIn this heartfelt testimonial, James Ellis, the founder of TipStars, shares his transformative experience working with ScalaCode. He highlights how ScalaCode's expert team helped turn his vision of a tipping platform for artists and art lovers into a reality. James praises their innovative approach, dedication, and seamless project execution, which played a crucial role in the success of TipStars. This platform now empowers artists and enhances the experience for art enthusiasts, thanks to ScalaCode's exceptional development skills.
James Ellis
Owner, Artist-Tipping PlatformScalaCode provides great results, uplifting the collaborative experience with their impressive project management style. The team always delivers as expected, which is manifested by the length of the ongoing relationship with us. Overall, their services have been impressive.
Jaa St. Julien
Pres. & Chief Strategy Officer - St. Julien CommunicationsI have been working with ScalaCode for almost a year and half now. I have this project 4Sale, it’s a marketplace application. I contacted them for the project and we started around 2021. The company is very responsive and always take the extra mile to help you out. I highly recommend them; if you have a project, contact this company. They always respond on time even though there’s a time difference.
Manuel
CEO, 4SaleThe application was basically built from scratch, and was complicated, as the software was to be integrated with a certain Medical EHR software. As the CEO of SHG, I was very pleased with the services, expertise, and support we received from ScalaCode, from the beginning directly through the first LIVE implementation.
Stephen Holmes
CEO, Steve Homes GroupThe iOS and Android apps exceeded the expectations of the internal team. ScalaCode crafts high-quality products that are easy to use and fit the requirements of the client. The team is technically experienced, hard-working, and knowledgeable.
Carolyn Dare
Director, Empowered AchieverI needed a reliable team on-hand, and ScalaCode delivered. Their excellent availability and project oversight made a big impact.
Faid Lalji
Learn ArenaOur XR project had unique hurdles, but ScalaCode grasped it fast and delivered beyond expectations with excellent collaboration.
Alessandro
CEO / Founder (XR Company)A first working model usually ships in 6 to 10 weeks. A production grade system with retraining, monitoring, and a measurement program takes 3 to 5 months on average.
We can start with 3 months of clean event data or roughly 50,000 user actions. Below that threshold, we lean on content embeddings and cold-start fallbacks.
Yes. We use content embeddings, popularity priors, contextual bandits, and onboarding signal capture to serve useful picks before any behavior data is collected from a user.
Yes. We integrate with Segment, GA4, Amplitude, Mixpanel, Snowplow, and custom warehouses. We also write back recommendation events so your downstream reporting stays accurate.
Offline we track NDCG, recall at K, and hit rate. Online we track click-through rate, conversion, average order value, and time on site through controlled A/B tests.
Yes. We deploy GrowthBook, LaunchDarkly, or your existing experiment platform. We also write the analysis templates so product and non-ML teams can read results without engineering help.
We set retraining on a schedule that fits your data velocity, usually weekly or monthly. We monitor for drift and data quality so the model stays useful after launch.
Everything we asked for was done quickly and efficiently. ScalaCode solved problems without delays or excuses.
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