Your workflows, built on our context layer

Needl.ai's context layer brings your documents and data together under your existing permissions, with every answer traced to its source. On top of it, our engineers build the agents, integrations and workflows your teams need, deploy them in your environment on the model you choose, and stay on to keep them accurate.

What we build on the context layer

Start from a product we already run, or have us build the agent your desk needs. Both run on the same layer, connected to the systems your teams already use.

AI Due Diligence: a data room read in full, and the cited investment committee memo it produces

AI Due Diligence

Our lead product. Load a data room from your drive or by upload, and Needl.ai reads every document, validates the model, flags the risks and drafts a cited investment committee memo, with a first pass in around 30 minutes. It plans the work for the asset class and deal side, and we shape the memo to your committee's format.

  • Private equity, private credit and venture deals
  • Every finding linked to its source page
  • A memo exported in your committee's format
Explore AI Due Diligence
An agent built around a credit desk's process, from a new filing to a draft in the house template, an analyst review and publishing

Agents built for your desk

For the reading-heavy work that fills an analyst's week, we build agents around the way your desk already works: credit reports in your house template, notes on the companies you cover, monitoring that sends the right event to the right person, and assistants that answer from your own documents. Every output carries its sources, so a reviewer can check it.

  • Reports written to your own templates
  • Alerts in real time or as a daily digest
  • Review steps where your process needs them
See what we have built
Needl.ai connected to SharePoint, Google Drive, Outlook, Snowflake, Power BI and HubSpot, with the API and MCP server built on the same layer

Integrations with your systems

Needl.ai connects to the systems your teams already use, from SharePoint, Google Drive and Outlook to Snowflake, Power BI and HubSpot, and follows the permissions set in each one. Where a connector does not exist yet, we build it. Your own developers can build on the same layer through the Needl.ai API and MCP server.

  • Permissions carried over from every source
  • New connectors built on request
  • An API and an MCP server for your own tools
See all integrations

Delivered under your brand

Your name, logo and colours on the app your teams use and on every report they export. Run it on your own subdomain, or offer it to your clients as your own product.

  • App name, logo, favicon and colours
  • Branded PDF and Word exports
  • A sign-in page in your brand
  • Your own subdomain
  • Your own name for the assistant
  • White label for partners
The application and its exported reports carrying a client's own name, logo and colours

How an engagement runs

An engineer learns the work first-hand, then a dedicated team builds, deploys and supports it. Most teams see it working on their own documents within weeks.

  1. 01

    Learn the work

    An engineer works alongside the desk, learns the workflow first-hand and agrees with you a golden set of past work that defines a good result.

  2. 02

    Prove it on your documents

    A proof of concept on your own data, graded against that golden set and signed off by the people who will use it.

  3. 03

    Deploy in your environment

    A dedicated team connects your systems, works through your security review and deploys into your cloud account, a dedicated instance or the Needl.ai cloud.

  4. 04

    Run and improve

    Every release is re-tested against the golden set. We tune the workflow with your team and add new use cases to the same platform.

Included in every engagement

Engineers who learn your work

An engineer works with your team from the first week, backed by a dedicated team of ML, data, frontend and DevOps engineers.

Your choice of model

OpenAI, Anthropic and Google models, or open-weight models hosted in your own environment. Your workflows stay the same when the model changes.

Deployment where your data lives

Your own AWS or Azure account, a dedicated instance we operate for you, or the Needl.ai cloud, with the same controls in each.

Security review support

We work through your security questionnaires with you and share our SOC 2 Type II report and ISO 27001 certificate under NDA.

Accuracy measured on your work

A golden set of your own past work, agreed up front. Every release is re-tested against it before it reaches your team.

Support after launch

Contractual response times, tuning and model updates from the team that built it. Custom work is built into the platform itself, so it stays supported as Needl.ai improves.

Built with our clients

“Needl.ai has gone above and beyond in delivering value. We are already interested in expanding its scope across the company.”
Trading Desk Executive, Institutional Trading Desk
  • A credit rating agencyReport drafting and a research assistant, built with the agency's analysts and running in its own AWS account under its own brand.
  • A marketing analytics platformOne assistant across its Snowflake, Power BI and Google Drive data, running in its own AWS account and offered to its clients under its own name.
  • A US private equity firmA diligence cycle that used to take three months, completed in one week.

Frequently asked questions

What teams ask before starting an engagement.

Is Needl.ai a platform or a services company?

A platform with an engineering team on top. The context layer, which connects your sources, indexes them and returns cited answers under your permissions, is the product. Our engineers use it to build and run the agents, integrations and workflows your teams need.

How soon can we see it working?

Within weeks. An engineer learns the workflow first, then we run a proof of concept on your own documents and grade it against your past work before anything goes live.

How is success measured?

Before the proof of concept starts, we agree the success criteria with you and a golden set of your own past work. Outputs are graded against it, and every later release is re-tested against the same set.

Can we start with one workflow and add more later?

Yes. New workflows are added to the same platform and the same connected sources, rather than started as separate projects. If the work involves reading, comparing or summarising documents and data, we can build an agent for it.

Can we use our own brand?

Yes. Your name, logo and colours can appear on the app, the sign-in page and every PDF and Word export, on your own subdomain. Partners can also offer Needl.ai to their clients under their own brand.

Which models do you support?

OpenAI, Anthropic and Google models, including through your own Azure or AWS account, and open-weight models hosted in your own environment. Your data and workflows stay separate from the model, so a switch needs no rework.

Where does it run?

In your own AWS or Azure account, in a dedicated single-tenant instance that we operate, or in the Needl.ai cloud. Trust & Security sets out the controls in each.

Who supports it after launch?

The team that built it. We provide support with contractual response times, ongoing tuning and model updates, and extend the platform to new use cases as you need them.

Start with one workflow

Tell us where you want to begin. The first conversation covers the workflow, your data and where it should run.

See it in action

Watch AI Due Diligence and the workflows on this page run, then talk through your own.

Book a demo

Build a custom workflow

Tell us about the work, and we will scope a proof of concept on your own documents.

Talk to us

Offer it under your brand

For partners who want to deliver Needl.ai to their own clients as their own product.

Partner with us