$20K–$100K typical projects
Direct answer
You can hire me for AI search development on a fixed-scope project basis, with most engagements landing between $20K and $100K depending on corpus size, relevance targets, and how many surfaces the search feeds. I bring 7+ years of production delivery, work as a Guest Engineer at Expensify, and I'm Top Rated on Upwork with $100K+ earned and verified client reviews. A typical build — hybrid retrieval, ranking, evaluation harness, and production deployment — ships in 6 to 12 weeks. The first step is a short scoping call where we measure your current search failure modes against real user queries.
Users who search and find nothing leave, and most product search fails silently — nobody files a ticket saying the ranking was wrong. AI search development replaces keyword-only matching with retrieval that understands intent, and the projects that succeed are the ones that treat relevance as a measurable engineering target rather than a demo.
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 marketplace with 80K SKUs sees users search for products it clearly stocks and get zero results because the query wording never matches the catalog text. I build hybrid retrieval combining keyword and semantic matching, tune ranking against logged queries, and ship behind a feature flag. Outcome shape: measurable lift in search-to-click rate and fewer zero-result sessions.
A company's knowledge lives in wikis, PDFs, tickets, and shared drives, and employees ask each other instead of searching. I build an ingestion pipeline that normalizes those sources, a retrieval layer with access controls preserved, and a search interface with cited results. Outcome shape: one search box that actually answers, with permissions intact.
A SaaS product has search built on a basic database text index, and support tickets keep saying users cannot find things they know exist. I benchmark the current system against real query logs, layer semantic reranking over the existing index, and prove the improvement with a side-by-side evaluation before cutover. Outcome shape: better ranking with zero migration risk.
The first week is diagnosis, not code. I pull your query logs — or instrument them if they don't exist — and build a labeled set of a few hundred real queries with the results users should have seen. That set becomes the yardstick for everything that follows, and it usually reveals that the problem is narrower than expected: specific query categories fail, not search in general.
Weeks two through five are the retrieval build: ingestion pipeline, embedding strategy, hybrid scoring that combines lexical and semantic signals, and a reranking layer. Every change gets scored against the labeled set, so you see relevance numbers move week over week instead of waiting for a big reveal. The final stretch is production hardening — latency budgets, index refresh strategy, monitoring for relevance drift — and a staged rollout where the new search runs shadow traffic before it takes over. You get a system your team can retune later, because the evaluation harness ships with the project.
The single biggest cost driver is data messiness, not data volume. A clean product catalog of a million rows is cheaper to make searchable than 10,000 PDFs with inconsistent structure, embedded tables, and duplicated versions. If your content needs heavy extraction and normalization before it can be indexed, budget for the upper half of the range.
The second driver is the relevance bar. Getting search from bad to good is the affordable part; getting from good to competitive-with-Google-quality on your domain requires reranking models, query understanding, and iteration cycles that add weeks. Third is surface count — one web search box is one thing; feeding the same retrieval layer into an API, a chatbot, and a recommendations module multiplies integration work. A single-surface project on clean data lands near $20K to $40K. Multi-source ingestion with a high relevance bar and multiple consuming surfaces is where $100K engagements live.
The most common failure I see is teams buying embeddings and calling it done. Pure vector search demos beautifully on cherry-picked queries and then loses to plain keyword search on exact-match queries — product codes, names, error strings — which are often the queries users care most about. Production search needs hybrid retrieval, and vendors who don't say that upfront haven't shipped it.
The second failure is skipping evaluation. If there's no labeled query set, nobody can say whether a change made search better or worse, so tuning becomes vibes-driven and stalls. The third is ignoring freshness: an index that lags your content by a week means users find deleted products and stale docs, and trust in the search box dies fast. Any serious proposal should address all three — hybrid scoring, an evaluation harness, and an index update strategy — before it mentions a single model name.
Ask one question first: how will we know the new search is better? A strong candidate answers with a measurement plan — labeled queries, offline relevance metrics, then online click-through comparison — before talking about technology. If the answer starts with a specific vector database or model, you're hearing a tool preference, not an engineering plan.
Then ask what they'd do about queries that semantic search handles badly. Anyone who has shipped search in production will immediately mention exact identifiers, short queries, and misspellings, because those are the cases that burn you after launch. Finally, ask for the smallest version of the project that proves value. A senior engineer will propose scoring your existing search against a labeled set in the first two weeks — a cheap step that de-risks the whole engagement. A vendor who insists the full build must happen before anything can be measured is asking you to carry all the risk.
If your users aren't searching, better search won't help. Check your analytics first: when search usage is a rounding error, the money is better spent on navigation and information architecture. Similarly, if you have under a few thousand items with clean, consistent naming, a well-configured traditional search engine gets you most of the value at a fraction of the cost — I'll tell you that in the scoping call rather than build something you don't need.
The other case to wait on is content chaos. If your documents are badly duplicated, contradictory, or wildly out of date, AI search will surface that chaos faster and more confidently than keyword search did. Sometimes the right first project is a content cleanup with a small retrieval prototype to prove direction, and the full search build comes a quarter later. Sequencing it that way costs less overall and produces a system users actually trust.
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.
Most production AI search projects run $20K to $100K. A single search surface over clean, structured data lands in the $20K–$40K range. Multi-source ingestion, heavy document processing, strict relevance targets, or multiple consuming surfaces such as an API plus a chatbot push toward the upper end. A short paid diagnostic against your real query logs is the cheapest way to get an accurate number.
Six to twelve weeks for most engagements. The first two weeks establish a labeled query set and baseline metrics, the middle weeks build hybrid retrieval and reranking with weekly relevance scores, and the final stretch covers production hardening and a shadow-traffic rollout. Timelines stretch mainly when source data needs extensive cleanup before it can be indexed reliably.
Both, combined. Vector search understands intent and paraphrasing; keyword search wins on exact matches like product codes, names, and error messages — queries users care about most. Production systems that perform well use hybrid retrieval that scores both signals together, usually with a reranking step on top. Any proposal offering pure vector search as a complete solution is a red flag.
Projects typically fall in the $20K–$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