One client was seeing around 50% extraction accuracy. The work had not been automated.
LPs and fund administrators do not buy document extraction services to get just a fraction of the extraction correct. They buy the service because they want to remove the manual work involved in collecting, interpreting and entering information.
One ADI client was seeing around 50% extraction accuracy from its existing Canoe service on the documents and fields that mattered to its process. At that level, the promised reduction in manual work had not been delivered.
Your team still has to check the outputs, find missing values, correct mistakes and decide whether the information can be trusted. The service may have completed the first pass, but the operational burden remains with the people who were supposed to have been freed from it.
That is not the automation firms thought they were buying.
The problem with accuracy at this level is not simply that some fields are wrong. It is that your team does not necessarily know which fields are right, so everything still has to be reviewed.
That matters in alternative assets, where an incorrect entity, reporting period, valuation, commitment figure or capital activity can flow directly into investor reporting, reconciliations and financial decisions.
The impact gets worse as volumes grow. When firms are processing thousands of documents from GP portals, email inboxes and data rooms, every exception becomes another manual task. The service fee may be paying for the first pass, but your firm is still paying people to check and complete the work afterwards.
The result is an awkward halfway house where the service has changed the process without removing the cost, delay or operational risk.
Many firms are now experimenting with ChatGPT, Claude, Copilot and internally built agents. These tools can produce impressive results, but they cannot compensate for unreliable source data.
If the underlying extraction is wrong, AI simply gives the organisation a faster and more convincing way to consume incorrect information.
Before a team can search across its documents, automate reporting, build agents or ask questions about its portfolio, the information needs to be extracted accurately, linked to the correct entities and supported by a clear audit trail.
ADI was built for this next stage. The aim is not simply to extract information, but to make the underlying data reliable enough for AI and automation to be used across the business with confidence.
We start by auto-categorizing your documents using a trained language model. For each document category, we engineer the extraction process to reliably achieve a target schema for your processes. We also embed data quality checks to catch anomalies, but our approach has achieved accuracy rates in excess of 98% out of the box. Before human in the loop.
Moving from accuracy at this level to above 98% changes the economics of the workflow. Instead of reviewing every output because errors could be anywhere, your team can focus on a much smaller number of clearly identified exceptions.
ADI then adds the context, controls and workflow capability needed to make that information useful. The platform can:
The result is not simply extracted data. It is information that can be searched, understood and used with confidence.
For firms already paying significant annual fees for document extraction, ADI offers a broader operating layer around that data, including NLP dashboards, conversational AI, and workflow automation.
The difference is not incremental. It is the difference between a service that assists a manual process and software that can actually remove it.
ADI is designed for organisations receiving significant volumes of information from funds, managers and service providers, such as family offices, fund administrators, fund of funds, pension funds, endowments and other institutional allocators.
These organisations do not need another carefully managed AI demonstration. They need fewer manual checks, faster access to reliable information and greater control over how that information moves through the business.
Some firms use ADI to replace an existing extraction provider. Others use it alongside their current systems to improve extraction, search, governance and workflow automation.
The starting point is the same in both cases: your documents and a clear definition of success.
We will process a representative set of your alternative asset documents and measure the results against the fields that matter to your team.
You will see what can be extracted accurately, which exceptions still require review and how the resulting information can support the wider workflow.
If we cannot materially improve the accuracy and operational outcome, there is no reason to move forward.
Book a conversation with ADI and test the platform against the documents your team processes every day.
ADI helps alternative asset owners, fund administrators and financial services organisations turn fragmented documents into trusted, searchable and actionable data.