Most deal teams are fully operational on Deal Intelligence within one week and process their first target's financials within 48 hours of onboarding.
By Avik Ghosh, Managing Founder, illuminis
Buyers looking at an AI due diligence platform often expect a long implementation project. Deal Intelligence is built to avoid one. Most firms are fully operational within one week, and the return is usually visible in the first deal cycle.
That timeline matters for PE firms and M&A teams that cannot pause live processes while they set up a new tool. Implementation causes almost no disruption to existing workflows.
The quickest part of onboarding is the due diligence work itself. Most M&A teams are running their first target's financials through the platform within 48 hours of onboarding.
This works because the platform does not need clean inputs. It reads financial documents in the formats deal teams already receive, from audited statements to management-prepared reports to QuickBooks exports. It then maps each line item to a standard chart of accounts. Financial data from virtual data rooms comes in automatically, so nobody has to type it in by hand.
How soon you see results depends on the use case. The guidance breaks down like this:
Portfolio monitoring takes longer because the platform connects to each company's ongoing data, not a single set of documents. For PE firms managing 5 or more portfolio companies, the platform delivers ROI within the first quarter.
Software projects often stall when they ask people to change how they work. Deal Intelligence does not require portfolio companies to change their systems or processes. It works with the tools they already use, including QuickBooks, NetSuite, Sage and Xero, and standardizes the data automatically.
The same applies to deal teams. The AI does the first pass of data processing and pattern finding, then hands over structured findings and flagged items for human review. Analysts keep the work that needs their judgment and spend less time pulling data out of documents.
Some firms consider building their own analytics instead. Building in-house means a large investment in data engineering and data science staff. A purpose-built platform is faster to deploy and comes with proven analytical models and ongoing data sourcing.
The platform also keeps improving after launch. It learns from every deal your team processes, so its accuracy improves and your team has less manual work to do over time.
To see how onboarding would work for your team, visit the product page.