NeedleSearch delivers a standard of accuracy in legal research that conventional tools cannot match. Every finding is traced to a named source. Every claim is verified before it reaches the attorney.
Claude and ChatGPT are powerful — but they work with what you give them. Paste a clause, get a response. Paste a 100,000-page data room? You can't. NeedleSearch is the layer between your document library and AI reasoning: upload your files once, and multiple agents search the full collection in parallel — returning a structured answer with every claim traced to a specific page before it reaches you.
NeedleSearch combines an OCR pipeline, semantic vectorisation, and a multi-agent reasoning layer into one end-to-end workflow.
Upload almost anything — 22+ file types, no limit on how many. Every file is encrypted the moment it arrives. We read each page for you automatically, whether it's typed text, a scan, handwriting, or a table, and learn what it actually means so you can find it later by searching for the idea, not just the exact words. Each document is then labelled and organised — automatically, or your own way.
Type your question the way you'd say it out loud and get a straight answer in seconds — with a link to the exact page it came from, so you can check it yourself in one click. Need it faster or deeper? Choose in a tap. And here's the part that matters: it only answers from your own documents — never made-up facts.
Each finding is attributed to a specific document and page number. The original passage appears on hover. The full document opens on click.
The research process is traceable at every stage. The platform conducts the search; the attorney examines the sources and reaches the conclusions. That division is deliberate.
Most AI tools answer with a single pass through a language model. NeedleSearch's agent calls specialised tools, checks what it finds, and loops until it's confident — before it returns an answer with every claim linked to its source.
One pass through a single model. No tools, no independent check, no named source.
The agent calls specialised tools, checks their results, and loops until it's confident — then returns an answer with every claim linked to its source.
Every system was tested on the same document folder, the same questions, and the same known-correct answers. Every verdict was checked against the underlying source passage.
| NeedleSearch multi‑agent |
NeedleSearch single‑agent |
Claude Cowork | Semantic search | Keyword search | |
|---|---|---|---|---|---|
| Overall | 98% | 92% | 69% | 55% | 45% |
| <100 files | 97% | 91% | 79% | 50% | — |
| >100 files | 98% | 93% | 60% | 60% | — |
Multi-agent smart-search holds 97–98% accuracy regardless of collection size. Claude Cowork, the only file-reading (non-indexed) tier tested, is the one that drops on the larger collection — it simply can't read everything in time. Index-based approaches (RAG, smart-search) don't degrade with scale. The two collections used different question sets, so only the ranking of systems within each collection is meaningful — not the size of the gap between collections. Keyword search reflects retrieval recall (150 reformulated queries, exact-fact match), a different metric from answer accuracy, shown here for reference only.
Legal and compliance teams often need to review hundreds, thousands, or even millions of documents to find key information. Data volumes grow exponentially while review costs remain stubbornly manual.
Standard search tools return a list of files. AI chatbots can't read your private archive. The full answer is fragmented across sources, invisible to any single search.
Missing or losing one relevant document can create legal risk — sanctions, adverse inference, dismissal, or default judgment.
NeedleSearch is a dedicated document intelligence platform — not a UI layer over an LLM. Here is what that difference means in practice.
Multiple agents search your document collection simultaneously, each pursuing a different angle of the query. Findings are merged and verified before delivery.
Every claim is checked against its source before the answer is compiled. If a finding cannot be traced to a specific page in your documents, it is excluded.
Designed for collections of millions of documents. Entire data rooms, case archives and regulatory libraries load as a single searchable collection.
Scanned documents, mixed PDFs and image-based files are processed automatically. The same search quality applies regardless of how the document was created.
Dense vector search and lexical search run in parallel on every query. Legal terms, defined clauses and cross-references surface regardless of how the question is phrased.
The full platform runs on your own servers or private cloud. Documents are encrypted at rest, inference runs locally, and air-gapped operation is supported.
NeedleSearch exposes a full MCP server and REST API. Any MCP-compatible agent — Claude, ChatGPT, or your own — can use it as a tool without additional integration work.
NeedleSearch can run entirely inside your own infrastructure. Encrypted at rest, processed locally, never transmitted. For the most sensitive collections, air-gapped operation is supported.
Request private deployment →Standard search tools return a list of documents that may contain an answer. NeedleSearch returns the answer itself, with each claim traced to the passage that supports it.
| NeedleSearch | Keyword search | ChatGPT | Westlaw | |
|---|---|---|---|---|
| Searches your uploaded documents, not the internet | ✓ | ✗ | ✗ | ✗ |
| Follows legal reasoning, not keyword overlap | ✓ | ✗ | ✓ | Partial |
| Every claim linked to exact page and passage | ✓ | — | ✗ | Partial |
| Cannot fabricate a citation | ✓ | — | ✗ | ✓ |
| 100,000-page data room in a single query | ✓ | ✗ | ✗ | — |
| Encryption keys belong to you, data stays in EU | ✓ | — | ✗ | — |
| Open source passage in one click | ✓ | ✗ | ✗ | Partial |
Harvey and Legora are powerful tools for drafting and workflows — built for large firms with six-figure budgets. NeedleSearch brings a different architecture: parallel reasoning agents, on-premise deployment, and no minimum seat count.
| NeedleSearch AI | Harvey AI | Legora | |
|---|---|---|---|
| Minimum entry | 1 user — from $99/mo |
20 seats min
~$288 000/yr
|
10 seats min
~$30 000/yr
|
| Large dataset handling (1M+ docs) |
1M+ documents
Self-hosted: your own storage, no vendor cap
|
Up to 100 000 files
Per Vault
|
Up to 100 000 docs
Tabular Review limit
|
| Parallel research agents |
Yes — explicit task graph
Router → parallel agents → critic → synthesizer
|
Partial
Multi-step planning, no confirmed parallel agents
|
Partial
Agentic workflows, no confirmed parallel agents
|
| Critic agent (post-research QA) |
Yes
Dedicated critic after all research agents
|
No | No |
| On-premise deployment |
Yes — Docker + SaaS
Data never leaves your infrastructure
|
Cloud only
Azure
|
Cloud only
Azure
|
| MCP (Model Context Protocol) | Yes | Not documented | Not documented |
| File size limit | 5 GB per file | 100 MB per file | Not documented |
| Field-level encryption |
Yes — Cosmian KMS
Per-field AES keys, not just at-rest
|
Not documented | Not documented |
No findings without a source. Every answer NeedleSearch returns is structurally tied to a passage in your uploaded corpus — the platform cannot deliver a claim it cannot attribute.
Choose depth and speed. The citation requirement is the same across both — the system delivers nothing it cannot trace to a specific passage in your documents.
Parallel research threads cover the full document collection from multiple angles. A dedicated verification pass checks every finding before the answer reaches you.
A single agent conducts a focused search and returns a cited answer in seconds. Same attribution standard — when you need a result now, not in a minute.
The platform exposes a full REST API and an MCP server. Search, document access and agentic research are available to any application or AI agent holding an API key. Full OpenAPI documentation is included at no additional cost.
A practising international arbitration lawyer, a veteran marketing strategist, an AI/RAG systems engineer, and an enterprise operations executive — each bringing deep domain expertise to the problem.
International arbitration lawyer, 5+ years PQE across ICSID, ICC, CAS, SCC, PCA, UNCITRAL & ICAC. Counsel at Cardinals, former associate at Derains & Gharavi. Based in France.
LinkedIn →AI / RAG systems engineer and full-stack developer. Built NeedleSearch from scratch — agentic pipeline, OCR routing, secure multi-tenant architecture. MSU Faculty of CMC.
LinkedIn →Marketing strategist and entrepreneur, 15+ years across technology, SaaS and enterprise. Co-founder of AYEP'S and Nice3D. Experience with Bayer, Yandex Market, VTB. France & US.
LinkedIn →Enterprise operations & product executive, 15+ years. Background across PepsiCo, Mars, JTI and Syngenta. Leads product operations, enterprise usability and process integration.
LinkedIn →Enterprise pricing is available on request.
Contact sales →
Thousands of pages. One afternoon. Every finding cited.