Knowledge work automation
9 min read
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Jun 5, 2025
Content Creator
Despite all the buzz around the latest AI applications, most teams still work like it's 2021. They are stuck copy-pasting between tools, manually reviewing PDFs, and patching together brittle RPA and IDP automations.
Over a year ago, we released V7 Go, our AI knowledge work automation platform. Our vision was clear: create an intuitive solution for complex, document tasks like financial analysis, tax audits, contract reviews, and lease abstraction. But we quickly discovered a significant gap between the potential of AI and its practical implementation.
The first wave of generative AI tools disappointed in business settings for two critical reasons:
The accuracy problem
For document-intensive industries, accuracy isn't optional. Standard AI tools often hallucinate facts, misunderstand industry jargon, and struggle with real-world document complexity. ChatGPT might impress in casual conversation, but it can be dangerously unreliable when extracting financial data or interpreting legal clauses.
Recent GPT models have become more accurate (especially considering that not long ago, only 7% of their cited references were correct) but they still fall short of the accuracy needed for certain business applications.
To address this, we developed specialized features like AI citations that highlight information sources in specific documents, and Knowledge Hub integration that ensures the AI pulls information from your internal company knowledge (via Google Drive, SharePoint, or other data sources) instead of making things up.
The implementation barrier
Even with these accuracy improvements, we encountered another challenge: implementation complexity. Setting up effective AI automations for industry-specific tasks requires considerable expertise that most teams don't have.
The example automation workflows above may appear simple, but each step can contain multiple layers of reasoning. The diagram view highlights only the points where reasoning follows clear, deterministic branches based on conditional logic. The "Main" part itself can already involve multi-step reasoning across several AI models.
The truth is, not everyone on your team is an AI expert who knows the ins and outs of prompt engineering. Few professionals have the time or specialized knowledge to configure customized document processing workflows that deliver accurate results in production.
We saw this firsthand. Organizations would get excited about the potential of our technology but then hit a wall when trying to implement it because:
Prompting is not an established business process. Most teams don't want to learn how to "talk" to an LLM. They want consistent results, not guesswork.
AI outputs are often unpredictable or difficult to parse. That makes compliance a nightmare and automation both risky and challenging.
Every use case is different. One tax audit isn't like another. One firm's contract clauses won't match another's templates.
The result? Businesses either: A) abandon their AI initiatives, B) burden a tech-savvy team member with figuring it out, or C) hire expensive consultants who leave behind AI workflows no one can maintain.
What we were witnessing wasn’t a failure of ambition but a pattern we’ve seen with every major shift in technology. The initial surge of interest created inflated expectations. Teams expected instant results. But without the right tools, expertise, or infrastructure, those early efforts often stalled or underdelivered.
Then came the quiet phase. The hype faded. Budgets were re-evaluated. Yet underneath the surface, something more valuable was happening. Teams that stuck with it began to refine their goals. They stopped chasing novelty and started looking for repeatable outcomes. The question shifted from “Can we use AI?” to “How do we build something that actually works for our business?”
It’s in that quieter, more focused phase that real progress happens. And that’s where the next generation of AI systems is starting to take hold.
The rise of agentic AI
What was needed wasn't just better AI, but a fundamentally different approach. This is where agentic AI comes in. It represents a shift from single-step question-and-reply chatbots to AI agents and systems that can execute multi-step tasks, use specialized tools, and navigate complex workflows.
Gartner has named agentic AI the #1 strategic tech trend for 2025, predicting that a virtual workforce of specialized agents will assist, offload, and augment human work and traditional applications. This represents a fundamental evolution in how AI can be applied to business processes.
That's exactly why we're now launching Concierge—an AI agent orchestrator designed for professionals who aren't AI engineers.
Concierge is a centralized AI that understands your workload and delegates tasks to specialist agents based on the specific requirements. |
Rather than making you learn how to build with AI, Concierge acts as your AI operations manager, delegating tasks to specialized AI agents based on your needs.
The key difference is that you don't need to know which agent does what or how to configure them. Concierge handles all that complexity invisibly, making decisions about which specialized capabilities to deploy based on your documents and requests.
Conversational agentic AI for lawyers, financial analysts & underwriters
The spreadsheet view that used to be the default interface offered by V7 Go is powerful, but it’s not always intuitive, especially if you are configuring workflows for the first time. In real-world projects, you might have nested collections, entity rows, and dozens of files—finding the right information can be a maze.
V7 Go lets you scale data extraction and document processing through a table-style interface, where each row represents an input document or other entity that moves through multiple AI steps. This used to be the default experience of interacting with the platform.
And while structured fields are great for repeatable tasks, they’re less flexible for ad hoc questions (“What’s the most common risk clause in these contracts?” “Which companies had unusual expenses this quarter?”).
Conversational UI solves this. It’s the most natural way to interact with knowledge work:
You don’t need to know how the agents work—just describe your goal, and the Concierge figures out the rest.f
You can ask follow-up questions, dig deeper, or pivot your analysis on the fly.
Receive answers with full traceability—every output hyperlinked to its source
Chain together complex workflows (“Extract key terms from these contracts, then compare them, then generate a summary report”)
For teams, you can restrict access so some users only interact via chat, reducing the risk of accidental edits or misconfigurations.
Every conversation is saved as a Case, creating a living audit trail of your analysis.
Importantly, Concierge doesn’t replace the structured table interface but builds on top of it. You can still access, edit, and export data in table form. This is especially useful if you want to configure and customize the steps of the workflows. But now, you can also explore, analyze, and act through conversation.
Concierge is our answer to the question: “What if you could have all the power of agentic AI, but interact with it as naturally as you would with a colleague?”
Think of Concierge as the touchpoint with your virtual workforce of AI agents. It’s an AI assistant capable of orchestrating the right agents, tools, and data to solve your problem, then showing you exactly how it got there.
Let’s ground this in reality. AI often has a greater real-world impact in unglamorous administrative tasks than in headline-grabbing applications like cancer detection. This practical outlook shapes V7’s strategy, focusing on use cases and AI automations which can deliver immediate business value.
Here’s how agentic AI is changing the game in key industries:
Finance & private equity
Analysts used to spend days reconciling portfolio company reports, normalizing metrics, and drafting investment memos. Now, they upload all source documents to a V7 Go Case, run extraction agents, and use Concierge to ask questions like “Which companies had one-time expenses this quarter?” or “Summarize Q4 performance with highlights and adjustments.” The result: hours saved, errors reduced, and every answer traceable
Learn more:
Legal services
Lawyers preparing due diligence or contract reviews can upload hundreds of documents, then use Concierge to extract key clauses, flag risks, and generate summary reports. No more manual clause-by-clause review. And because every finding is linked to the source, nothing is lost in translation.
Learn more: AI for Contract Review: The Leading Tools You Should Know
Insurance
Claims adjusters and underwriters use V7 Go to process claim forms, policy documents, and supporting evidence. Agents extract key data, flag inconsistencies, and even compare new claims to past cases for fraud detection. Concierge can answer questions like “What’s the average settlement for claims of this type?” or “Are there any red flags in this file?”
Learn more: AI Agents for Insurance Underwriting and Claim Processing
Real estate & lease abstraction
Asset managers, analysts, and legal teams use agentic AI to extract clauses, rent schedules, renewal terms, and critical dates from lease agreements and property contracts. Instead of manually scanning PDFs, they can upload documents into a new Case, where agents handle parsing and structuring. Concierge makes it easy to ask questions like “Which leases have escalation clauses kicking in this year?” or “Summarize key terms for all new commercial tenants.”
Learn more: Automating Lease Abstraction and Real Estate Document Review with AI
In every case, the story is the same: AI handles the drudgery, humans focus on judgment, and trust is built through transparency.
Explore more use cases:
The future of knowledge work in the age of agentic AI
Many white-collar professionals spend a significant chunk of their working hours on repetitive tasks like document handling. This inefficiency of working with unstructured data carries costs beyond wasted hours—delayed decisions, overlooked insights, and the grinding frustration of performing work well below one’s pay grade.
Agentic AI isn’t another tool on the shelf. It’s a shift in how work happens that moves the burden of execution from people to AI systems, without losing context, nuance, or control.
V7's approach includes multiple layers of making sure that AI results are accurate:
Breaking complex processes into detailed chains of thought rather than single-step analyses.
Using deterministic computation (like Python) for financial calculations rather than relying on LLM reasoning.
Pinpointing exact document locations through precise AI citations and hyperlinks.
Cross-checking against similar cases to ground decisions in precedent.
V7 Go’s Concierge is built for teams who want the benefits of generative AI without reinventing their processes. It turns the friction of manual document review, cross-functional coordination, and compliance-heavy reporting into something fluid, traceable, and automated.
We’ve moved beyond the hype phase. The question is no longer whether AI can help with knowledge work. It’s whether your software can keep up with the complexity and pace of your business. If you want to discuss your use case, book a demo and let’s see if agentic AI powered by V7 Go is the right fit.
