A connector for each application
Each application answers with its own search, built for people browsing that one tool. The assistant receives whatever each returns, and the weakest search sets the quality of the answer.
The context layer for financial AI
Needl.ai is the context layer between your firm's information and any language model. It connects your documents, data and filings, finds the evidence each question needs within each person's access, and cites the passages its answers come from.
What happens between the question and the answer
A wrapper hands your question and a few excerpts to a general-purpose model and returns what it writes. The context layer first does the groundwork a careful analyst would do, then gives the model the evidence to write from.
A typical LLM wrapper
- Your questionSent with a few files
- A few excerptsCut up before the question was asked
- The model answersWith no trail back to the page
Needl.ai
- Your questionIn your own words
-
The context layer
- Your firm's contextTerms and conventions your administrators set
- Every sourceEmail, drives, chat, filings and data
- Your accessOnly what this person may see
- The right passagesPulled from whole documents when you ask
- The model writesCiting the passages it used
Both start from the same question and can run on the same model, so the difference in the answer comes from what each one gives the model to read.
A wrapper and a context layer, side by side
The gap shows in the work that matters most: a data room of several hundred files, a folder only the deal team can open, a figure the committee will question.
| Compared | A typical LLM wrapper | Needl.aicontext layer |
|---|---|---|
| What it is | A prompt and a general-purpose model, with files attached to the conversation | A platform that connects, reads and organises your firm's information, with the model working on top |
| What the model sees | The excerpts that fit in one conversation | The passages each question needs, found across everything you have connected |
| A large data room | Slow, partly read or over a file limit | Indexed in full, and every question searches all of it |
| Spreadsheets | Flattened into text | Read cell by cell, citing the sheet and range |
| Your firm's conventions | Typed into each prompt | Kept as standing context your administrators maintain |
| Who sees what | Whatever has been uploaded to the workspace | Only what each person's own accounts and entitlements allow, on every search |
| Checking an answer | Find the source by hand | Citations to the exact passages used |
| A new model arrives | The product changes with it | Change the model by configuration and keep your sources, index and workflows |
| Where it runs | The vendor's cloud | Needl.ai's cloud, or a dedicated deployment on AWS or Azure, including your own account |
Why the usual shortcuts fall short
There are three common ways to give a model a firm's context without building a layer for it. Each works on a small scale and struggles on a firm's real library.
A vector database
Documents are cut into chunks and matched by similarity. That works on a small, curated set, but it blurs as the library grows, and it is hard to say why a passage was returned.
A larger context window
A bigger window lets a model read more at once. A firm's information is far larger than any window, so the right passages still have to be found first.
Needl.ai keeps one index across everything you connect and searches the words of your documents directly, the same way for every source.
One layer under every tool your team uses
Your team owns the expertise and the experience its clients see, and the model does the writing. Needl.ai is the layer both work from: connect your sources once, and its apps, your assistant and the applications you build all draw on the same context.
-
Needl.ai apps
Ask questions, monitor what matters and generate reports in the Needl.ai workspace.
-
Your assistant
Claude, Cursor and other assistants that support MCP reach every connected source through one MCP Connector, and answers come back with their sources.
-
Your own applications
Your portals, dashboards and agents bring in the same answers, with their sources, through the Needl.ai API.
Context layer
- Connected sources
- Access rules
- Your context
- Cited passages
Outlook
Gmail
SharePoint
Google Drive
Box
Dropbox
Slack
HubSpot
Snowflake
PostgreSQL- SEC filings
What the context layer does
The parts of the platform that every Needl.ai workflow runs on, from AI due diligence to credit rating reports.
Everything you connect, in one place
Needl.ai connects to the systems your firm already works in, from email, SharePoint and shared drives to chat and public filings, and keeps them searchable in one place. Sources are checked for changes continuously. Plain documents are read with standard parsers, and complex, image-heavy ones with vision models. Data warehouses such as Snowflake, set up with our team during onboarding, are queried directly when a question needs them.
- Email, drives, chat and filings in one place
- New and edited files picked up as they change
- Recent email and chat first, then the history
The passages that answer the question
Documents are kept whole. When a question arrives, Needl.ai searches them for the words that matter and pulls out the passage around each match, so the model reads text in its context instead of pieces cut up in advance. When a passage is not enough, it reads the whole document.
- Search across whole documents
- Passages pulled when the question is asked
- The full document read when it is needed
Answers within each person's access
Each person searches only what their own connected accounts and entitlements give them, and those rules apply on every search. Two colleagues asking the same question can receive different answers when their access differs.
- Each person's own connected accounts
- SharePoint sharing settings carried into search
- Applied again on every search
Answers you can check
Answers drawn from your documents cite the exact passages they used. A reviewer opens the source from the answer instead of searching for it, and an answer that carries no source is marked for checking before anyone relies on it. Past conversations can be reopened or exported with their sources.
- Citations to the exact passage used
- The source opened from the answer
- A clear notice when an answer has no source
Context your team maintains
Your administrators write down the terms, conventions and instructions the assistant should know, and add short guides for recurring questions. Once published, the assistant uses them from the next question, and every version is kept.
Any model, in your environment
Your sources, index, access rules and workflows sit apart from the model, so a deployment moves to a newer or cheaper one through configuration. It can run in your own AWS or Azure account and reach models through your own AI gateway.
Where the accuracy comes from
A model can reason well and still be wrong, because it was given the wrong evidence. The answer reads well and may even cite sources, which makes the mistake hard to see. The context layer decides what evidence the model sees.
Small gaps compound
An agent runs several searches in a row, and its answer is only right if every one of them is. If each step finds the right evidence 95% of the time, a five-step workflow is fully right 77% of the time. At 82% per step, it is right 37% of the time.
| Steps | 95% right at each step | 82% right at each step |
|---|---|---|
| 1 | 95% | 82% |
| 2 | 90% | 67% |
| 3 | 86% | 55% |
| 4 | 81% | 45% |
| 5 | 77% | 37% |
| 6 | 74% | 30% |
| 7 | 70% | 25% |
| 8 | 66% | 20% |
| 9 | 63% | 17% |
| 10 | 60% | 14% |
OfficeQA Pro
133 hard questions over 89,000 pages of raw PDFs, scored with no margin for error, against the strongest published frontier agent in the same setup.
- 16.5ptsMore answers exactly rightNeedl.ai
64.66%
Frontier agent48.12%
- 13×Faster per questionNeedl.ai
2.4 min
Frontier agent31.2 min
- 4.6×Cheaper per questionNeedl.ai
$0.98
Frontier agent$4.55
97.24%
FinanceBench, over the full filings corpus
The benchmark is usually run with each question paired to the filing that holds its answer. Needl.ai ran it with every question put to the whole SEC filings corpus, so the right filing had to be found first, and answered 97.24% of them correctly.
Frequently asked questions
What technology and investment leaders ask about the context layer.
What is a context layer?
The infrastructure between a firm's information and a language model. It connects to the firm's documents, data and filings, keeps them current, finds the evidence each question needs within each person's access, and cites the passages its answers come from. Needl.ai is a context layer built for financial services.
What is an LLM wrapper?
A product built mainly from a prompt and a general-purpose language model, usually with documents attached to the conversation. Its answers are largely the model's own, so they change when the model changes, and it struggles once the documents no longer fit in one conversation.
Can we use it from the assistant we already have?
Yes, if it supports MCP. Claude, Claude Code and Cursor connect to Needl.ai through the MCP Connector. Answers come back with their sources and draw only on what the signed-in person can already see in Needl.ai.
What can our team change on its own?
Your administrators maintain the business context: the terms, conventions and instructions the assistant follows, plus short guides it pulls in when a question matches. A published change applies from the next question, and earlier versions can be restored. Each person connects their own mailboxes, drives and channels, and syncing starts on its own.
Which language models does it use, and can we change them?
Models from OpenAI, Google and Anthropic, chosen for each task: larger models for analysis and drafting, smaller and faster ones for routine steps. Your sources, index, access rules and workflows sit outside the model, so a deployment moves to a newer model, or to your own AI gateway, through configuration, and falls back to the next model in line if a provider is unavailable.
How does it stay accurate on large document sets?
Documents are indexed in full, and each question searches the whole index for the passages it needs. Accuracy therefore does not depend on how many files fit into a single conversation with the model.
Who can see what?
Each person searches only the content their own connected accounts and entitlements give them access to, and those rules apply on every search. Sharing settings from SharePoint are carried into search, so two colleagues can receive different answers to the same question.
Where does it run?
In Needl.ai's cloud, or as a dedicated deployment on AWS or Azure, including inside your own cloud account. Needl.ai is audited to SOC 2 Type II and ISO 27001, and Trust & Security sets out each option.
See the context layer on your own documents
Bring a deal room, a credit file or a research archive. We connect it in your environment and show you the answers, each with its sources.