$15K–$80K typical projects
Direct answer
Vector search development — building semantic search, retrieval for RAG systems, or recommendation features on top of embeddings — typically costs $15K–$80K depending on corpus size, quality bar, and how much of the surrounding pipeline exists. I build these systems end to end, backed by 7+ years of production delivery and work as a Guest Engineer at Expensify on a platform used by millions, and I'm Top Rated on Upwork with $100K+ earned and verified client reviews — one verification channel alongside the direct clients I work with worldwide. The engagement starts with a retrieval-quality baseline on your actual data, because until relevance is measured, everything else is guesswork.
Your users don't search with the words in your database, and keyword search punishes them for it. Vector search closes that gap — but the difference between a demo that impresses in a meeting and a system that returns the right answer on query ten thousand is evaluation, hybrid retrieval, and unglamorous pipeline engineering. That second thing is what I sell.
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 SaaS company's help-center search returns nothing unless users guess exact phrasing, so tickets pile up for answered questions. I build hybrid retrieval — embeddings plus keyword scoring — over their articles, tune chunking to how their docs are actually written, and measure relevance against real query logs before launch. Self-service resolution climbs, and the support team sees repetitive tickets drop.
A founder's AI assistant hallucinates because its retrieval returns plausible-looking but wrong passages. I rebuild the pipeline: document-aware chunking, metadata filtering, hybrid search with reranking, and an evaluation harness that scores retrieval quality on a golden set of real questions. Answer accuracy becomes measurable and improves release over release, turning an embarrassing demo into a product the founder can sell.
A marketplace wants 'more like this' and duplicate-listing detection across hundreds of thousands of items with images and descriptions. I build an embedding pipeline over both text and images, stand up vector infrastructure that fits their existing Postgres-based stack, and ship APIs their mobile app calls directly. Matching runs in milliseconds, and duplicate detection moves from manual moderation to an automated queue.
Week 1 establishes ground truth: I take your real corpus and real queries — from logs if you have them, from your team if you don't — and build a golden evaluation set with a baseline score for whatever search you have today. This number is the spine of the project; every later decision is judged against it. Weeks 2–3 stand up the core pipeline: ingestion, chunking, embeddings, a vector store that fits your stack, and a first hybrid retrieval endpoint you can query in staging.
Weeks 4–6 are tuning, which is where retrieval quality is actually won: chunking strategies, metadata filters, keyword-vector score fusion, reranking, and measuring each change against the golden set. Larger engagements extend into weeks 7–12 with production hardening — incremental index updates as your content changes, latency and cost optimization, monitoring for relevance drift — plus integration into your product and handover. You see scores weekly, so you always know whether the system is getting better.
At the low end sits a focused build: one content type, a corpus in the tens of thousands of documents, search quality that beats keyword baseline measurably, deployed behind an API. Costs rise with corpus messiness more than corpus size — PDFs with tables, scanned documents, mixed languages, or content requiring domain-specific chunking all multiply pipeline work. Freshness requirements matter too: a nightly reindex is cheap; near-real-time updates as users edit content require change-data-capture plumbing that is real engineering.
The top of the range covers multi-modal search (text plus images), strict latency budgets at high query volume, permission-aware retrieval where each user may only search documents they're allowed to see, and full RAG integration with reranking and answer evaluation. Infrastructure choice is a smaller driver than people expect — pgvector inside your existing Postgres covers more cases than vendors admit, and I recommend a dedicated vector database only when your scale or filtering needs actually demand one.
The loudest red flag is a vendor with no evaluation plan. If they can't tell you how retrieval quality will be measured on your data — golden sets, relevance metrics, before-and-after scores — you're buying vibes, and the vibes are always good in the demo. Closely related: anyone who demos on their corpus instead of yours. Embeddings that shine on clean English blog posts can fall apart on your legal PDFs or product catalog.
Be skeptical of pure-vector purism. Dropping keyword search entirely is usually a mistake — users searching exact SKUs, names, or error codes are better served by lexical matching, and production systems almost always want hybrid. Watch for premature infrastructure: a vendor reaching for a dedicated vector database cluster for your fifty thousand documents is either padding or defaulting to the stack they know. And treat 'we'll just fine-tune the embedding model' as a warning sign when offered before any measurement exists — it's the most expensive fix, proposed first because it sounds sophisticated.
Give every candidate the same test: a sample of your real documents and twenty real queries, and ask how they would measure whether their system beats what you have. Strong answers talk about golden sets, precision and recall at k, and human relevance judgments; weak answers talk about model names and infrastructure logos. Ask what they would try first if relevance came back poor — you want a layered answer (chunking, hybrid weighting, metadata, reranking) rather than a single hammer.
Ask about failure and cost at scale: what happens when the embedding API is down, what reindexing costs when you change models, what each thousand queries costs at your volume. Vendors who have run these systems in production answer instantly; vendors who have only built demos go quiet. Finally, insist on a small paid pilot scored against the baseline before committing the full budget. Retrieval quality is empirical — any vendor unwilling to be measured is telling you something.
You should end with a search system whose quality is a number, not an opinion: an evaluation harness your team can rerun after any change, scores on a golden set that beat the documented baseline, and a written record of what was tried and what moved the needle. The pipeline handles the full document lifecycle — new content gets indexed, edits propagate, deletions actually disappear from results — because a search index that drifts from your source of truth erodes trust one stale result at a time.
Operationally: latency and cost per query are known and monitored, the system degrades gracefully when an embedding provider has an outage, and relevance drift triggers an alert rather than a slow decline nobody notices. Everything runs in your infrastructure under your accounts, with documentation that lets your engineers change the embedding model or chunking strategy and re-score the impact without me. If a delivery leaves you unable to measure your own search quality, you didn't buy a system — you bought a dependency.
Skip vector search if your users search for exact things — order numbers, SKUs, usernames, error codes. Well-configured keyword search with synonyms and typo tolerance is cheaper, faster, and often better for that shape of query; semantic similarity adds nothing when the query is literal. Skip it too if your corpus is small and stable — a few hundred FAQ entries are better served by curation or a well-structured page than by an embedding pipeline.
Hold off if your content is the real problem. Retrieval over documentation that is outdated, contradictory, or missing returns beautiful matches to wrong answers; fix the corpus first, then make it searchable. And if you're pre-launch with zero query logs, consider shipping simple search first and instrumenting it — real user queries are the raw material for evaluation, and a month of logs makes the eventual vector build both cheaper and measurably better. I'd rather you come back with data than pay me to guess.
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.
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Usually you can use what you have. If you're on PostgreSQL, pgvector handles corpora into the millions of vectors with metadata filtering and transactional consistency with the rest of your data — one fewer system to operate and pay for. Dedicated vector databases earn their place at large scale, very high query volume, or when their specific filtering and hybrid features matter. I recommend based on your measured needs, and I have no reseller stake in either answer.
With a golden evaluation set: real queries from your logs or your team, each paired with the documents a correct system should return, scored with standard retrieval metrics like recall and precision at k plus human spot-checks. We baseline your current search first, then score every change against the same set, so improvement is a number you can see weekly. The harness stays with you after delivery, so future changes never fly blind.
Retrieval quality is the single biggest lever on RAG hallucinations — most wrong answers trace back to wrong or missing context, not the language model. This engagement builds exactly that layer: chunking tuned to your documents, hybrid retrieval, reranking, and metadata filters so the model sees the right passages. I can also add answer-level evaluation and citation checks on top. What it can't fix is a corpus that doesn't contain the answer; no retrieval saves you from missing content.
Projects typically fall in the $15K–$80K 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