$25K–$100K typical projects
Direct answer
Recommendation system development with me runs $25K–$100K: the low end covers a solid baseline recommender built on your existing data with offline evaluation and simple serving, while real-time hybrid systems with two-stage ranking and A/B measurement reach the top. I bring 7+ years of production delivery, apps used by millions of users, and Top Rated status on Upwork with $100K+ earned and verified client reviews. I start every project with your data and a baseline, because the honest way to sell a recommender is to prove it beats what you already have.
Recommendations are one of the few features with a direct line to revenue — better suggestions mean more engagement, larger carts, longer retention. But most teams jump straight to sophisticated models before auditing their data, establishing baselines, or deciding how they'll measure lift, and that ordering mistake is where the budget goes to die.
You need an MVP or v2 shipped on budget with someone who makes architecture decisions and owns delivery end-to-end.
You need a senior IC to augment the team, rescue a codebase, or lead mobile + AI integration without months of hiring.
You need a reliable senior subcontractor for client projects — clear communication, store-ready quality, white-label friendly.
30 minutes — goals, stack, timeline, budget range.
Fixed milestones, clear deliverables, start date.
Weekly demos, async Slack updates, production standards.
Store launch, documentation, knowledge transfer.
A product with real traffic still shows everyone the same 'popular' shelf. I audit the interaction data, build popularity and collaborative-filtering baselines, evaluate offline against held-out history, and ship the winner behind a clean API. The outcome is a measurable click-through lift over the current experience — and an honest number if the lift isn't there.
An engineering team maintains hand-written 'related products' rules that break every time the catalog changes. I replace them with embedding-based similarity, layered with business-rule re-ranking for margin, stock, and freshness. The outcome shape: related-item modules that maintain themselves as the catalog grows, with the merchandising team still holding override controls they trust.
An app with a chronological or manually-curated feed wants personalization without destroying content diversity. I build a two-stage system — candidate retrieval, then a ranking model over engagement signals — with diversity constraints and an A/B rollout plan. The outcome is a feed measured against the old one on retention and session length, not on how clever the model is.
Every recommender project I take starts by building the dumbest thing that could work: most-popular, recently-viewed, bought-together counts. These baselines take days, not months, and they set the bar every fancier model must clear. A shocking share of production recommendation value comes from exactly these methods, well executed.
This ordering also protects your budget. If a collaborative-filtering baseline gets you 80% of the achievable lift, you can stop at the low end of the range and spend the savings elsewhere. Vendors who open with neural architectures before looking at your data are selling their resume, not your outcome.
Weeks one and two are a data audit: what interaction signals exist, how sparse they are, how far back they go, and whether they're clean enough to learn from. This stage kills or reshapes more recommender projects than any other, and it's better to find out for a few thousand dollars than after the full build.
Weeks three through five produce baselines and offline evaluation against held-out history. Then candidate generation and ranking get built only if the baselines leave lift on the table. The final weeks cover serving infrastructure, an A/B test design, and a staged rollout — because offline metrics and online behavior disagree often enough that shipping without an experiment is guessing.
Freshness is the biggest lever. Batch recommendations recomputed nightly are dramatically cheaper than real-time systems that react to what a user did thirty seconds ago — and for many products, nightly is genuinely enough. Be suspicious of anyone who defaults to real-time without asking about your session patterns.
Other drivers: cold-start requirements (new users and new items need dedicated strategies), data quality (cleaning sparse or polluted interaction logs is real work), and how much serving infrastructure you already have. A batch recommender on clean data with existing infrastructure sits at $25K–$45K; real-time two-stage ranking with cold-start handling and full experiment tooling fills the top of the range.
Ask how they'll know the system works. The only fully honest answer involves both offline evaluation on your historical data and an online A/B test, plus an acknowledgment that the two frequently disagree. Ask what they'll do about popularity bias — naive recommenders converge on suggesting bestsellers to everyone, which looks fine in metrics and adds nothing.
Ask about cold start directly: 'What does a brand-new user see, and what happens to a brand-new item?' Vendors without a crisp answer have only worked on datasets where the problem was already solved. Finally, ask for the failure story. Anyone who has shipped recommenders has shipped one that lost the A/B test.
Skip this if your catalog is small — under a few hundred items, editorial curation beats any model, and users can browse the whole thing anyway. Skip it if your traffic can't power an A/B test: without enough users to measure lift, you'll never know whether the system earns its maintenance cost, and an unmeasurable recommender is a liability with a GPU bill.
Also skip it if your real problem is upstream: bad search, confusing navigation, or thin content. Recommendations amplify an experience; they don't fix one. I've told prospective clients to spend the budget on search instead, and it was the right call.
You approve milestones and a price before any build starts — no open-ended hourly surprises.
You see running software each week, not status reports, so you can course-correct early.
The person who scopes the work is the person who builds it — no junior layers, no agency markup.
Top Rated on Upwork with public client reviews and $100K+ earned, plus contributions to Expensify. Check the receipts before you commit.
Chat-Based Financial Collaboration
Contributed 50+ merged pull requests to New Expensify — the production React Native app used by millions for chat-based expense management — working through Expensify's fully public open-source process alongside their core engineers, and recognized with a Guest Engineer invitation to their Curaçao offsite.
Food Truck Discovery Platform
Production food-truck discovery platform for US cities — customer and vendor React Native apps on iOS and Android, plus customer and vendor web, with AI features. Helps people find trucks nearby while vendors manage schedules, orders, and events.
Rewards & Engagement Platform
End-to-end React Native app for the US market with gamified rewards, real-time engagement, and secure payment flows — built from product scoping to App Store launch.
Expect $25K–$100K for a production system. A batch recommender built on clean interaction data with offline evaluation runs $25K–$45K. Real-time personalization, two-stage ranking, cold-start strategies, and A/B experiment infrastructure push toward $100K. The cost most buyers miss is data cleanup: if your event tracking is sparse or polluted, budget real time for fixing it, because no model recovers from bad interaction data.
As a working floor, you want months of interaction history and thousands of users interacting with a catalog large enough that browsing it all is impractical. More important than volume is signal quality: explicit events like purchases and saves beat raw page views. If you're below that floor, the right move is instrumenting your events properly now and building the recommender in six months — advice that costs a vendor money to give you.
Plan on six to twelve weeks to a live A/B test: two weeks of data audit, two to three weeks of baselines and offline evaluation, then serving work and a staged rollout. The test itself needs to run long enough for statistical significance — typically two to four weeks depending on traffic. So real, trustworthy lift numbers arrive roughly a quarter after kickoff, and anyone promising proven lift faster is skipping the measurement.
Projects typically fall in the $25K–$100K range depending on scope, integrations, and timeline. I provide a fixed-scope proposal after a 30-minute scoping call.
MVPs often ship in 8–12 weeks. Production systems with AI backends or RAG may run 12–20 weeks. Rescue and audit engagements can start within days.
Yes. I work with founders, CTOs, product teams, and agencies worldwide — US, UK, EU, and APAC time zones with async updates and weekly demos.
Yes. I specialize in React Native + Python (FastAPI) + AI (RAG, agents, OpenAI/Claude) under one senior owner — fewer handoffs, faster shipping.
Book a free 30-minute scoping call on this site, hire through Upwork, or email dhairyasenjaliya@gmail.com with your brief and timeline.
30-minute scoping call · Clear milestones · Senior engineer ownership