Do I need ADI if I'm happily using Claude?
Whenever we show a financial institution what ADI can do with documents, one question now comes up quickly:
Couldn’t we just do this with Claude?
It is a perfectly reasonable question. Claude can analyse text and images, extract structured information, generate code and connect to external tools through its API. Anthropic also offers enterprise features including role-based permissions, audit logs, data retention controls and integrations. This is not an argument that Claude is incapable or unsuitable for enterprise use.
In fact, ADI can use Claude when it is the right model for the task.
The distinction is not really Claude versus ADI. It is the difference between having access to a powerful AI model and having a complete, controlled operational process that uses AI to remove work from the business.
A model can be given a document and asked to identify names, dates, amounts, legal terms or other information. For a proof of concept, that can be impressive. A team can upload a few files, refine a prompt and quickly demonstrate that the required information can be found.
The difficulty begins when that experiment has to become part of a live operation.
Take a customer onboarding process. The customer may submit several documents depending on whether they are an individual, a company, a trust or another type of entity. The system must first establish which documents have been received, identify what each document is, determine which fields should be extracted and fit the results into a consistent structure.
The extracted information may then need to be normalised, checked against existing customer and counterparty records, validated against business rules, presented for review and passed into another system. Any missing documents, conflicting information or potential errors must be routed to the right person for a decision.
Claude may be used within several parts of that process. It is not, by itself, the entire process.
ADI provides the surrounding data, workflow and control layer required to make that process repeatable. Its proposed onboarding workflow includes document preprocessing, categorisation, extraction against a defined target schema, normalisation, entity resolution, data-quality checks, approval and override workflows, and delivery into the customer’s existing portal or downstream systems.
One of the biggest differences between an AI demonstration and an operational system is what happens to the output.
Imagine that an investment document refers to “ABC Holdings”, while the firm’s general ledger, portfolio system and CRM each hold slightly different versions of that company’s name. Extracting the words “ABC Holdings” accurately does not tell the business which legal entity, account, portfolio or asset the document belongs to.
That requires context.
ADI includes capabilities that connect extracted information to the firm’s existing master data. In an alternatives workflow, that could mean matching a call notice to the correct end client, portfolio and private asset before a debit or credit is posted to the general ledger.
Without that connection, the system has read the document but has not completed the job.
This is why the target schema matters. It defines the precise information the operation needs, how each field should be represented and where it must go next. The model is being asked to contribute to a known operational outcome rather than simply return a plausible interpretation of the document.
Large language models are probabilistic. Their ability to interpret varied and complex documents is precisely what makes them useful, but it also means their outputs cannot automatically be treated as unquestionably correct.
A production process therefore needs more than confidence in the model.
ADI wraps model outputs in deterministic controls such as format checks, mathematical reconciliations, required-field checks and comparisons against reference data. The purpose is to catch potential errors before they enter a ledger, reporting process or customer record.
Human review remains important, but it should be focused on genuine exceptions. It should not mean asking an operations team to recheck every field because nobody knows which outputs can be trusted.
ADI’s workflows can present the source document alongside the extracted information, show where the data originated and allow an authorised user to approve or override the result. The platform records the model, prompt, source location and human interaction associated with the output, creating a traceable audit trail.
That is a fundamentally different proposition from copying an AI response into another system and hoping it is right.
There is another problem with building an operational process around direct access to a single model: the market is moving too quickly to assume that today’s best option will remain the best option for every task.
ADI is model agnostic. It can use a frontier model such as Claude for work that requires its reasoning and language capabilities, while using OCR, smaller open-source models, structured queries or local processing where those approaches are faster, cheaper or better suited to the confidentiality requirements.
The choice should depend on the use case.
For one document category, the priority may be accuracy. For another, it may be speed, cost, local deployment or the ability to process large volumes concurrently. A production architecture should be able to make those choices without forcing the customer to rebuild the entire workflow each time the model landscape changes.
ADI also aims to reduce unnecessary frontier-model consumption. Once extracted information has been validated and stored in a structured database, future questions may be answered through a structured query rather than repeatedly sending the original documents back through a large language model.
The purpose is not to use the most powerful model for every step. It is to achieve the required operational outcome with the appropriate combination of models, software and controls.
Document extraction is becoming easier and more widely available. Legacy providers are adding AI features, new document-processing companies continue to enter the market, and internal technology teams can now build credible prototypes with general-purpose models.
That makes extraction increasingly difficult to sell as a standalone product.
The more valuable question is what the business can do after the information has been extracted.
Can employees search and interrogate the document repository? Can extracted cash flows be reconciled? Can exceptions be assigned and approved? Can the information be connected to the correct client, company, security or asset? Can the same platform be extended into invoices, onboarding documents, tax records, trust documents or transaction data?
ADI’s strategy is to compete on this wider software, workflow and extensibility layer rather than treating document extraction as the finished product.
This is also why ADI is deployed around the customer’s existing operation rather than asking the customer to reorganise itself around a generic AI tool. The platform can connect to existing portals, databases, filing systems and applications, while forward-deployed engineers tailor the schemas, models, prompts, controls and workflows to the firm’s requirements.
For individual research, experimentation and early prototyping, direct access to Claude may be all that is required.
For a recurring financial operation involving multiple document types, master data, approvals, exceptions, downstream systems, security requirements and audit obligations, the model is only one part of the solution.
The better question is therefore not whether Claude or ADI is more capable.
It is whether the organisation needs an intelligent response from a model or a dependable operational outcome from a complete system.
ADI exists to deliver the second.
Already experimenting with Claude or another language model? Review how much of the surrounding work is still being performed manually. That is usually where the real automation opportunity begins.