Applied AI for financial sponsors and their portfolio companies
Websley works with lower-middle-market funds and portfolio companies. We find the work consuming your team’s hours, prove one workflow on your data in two weeks, and move it into production only when it meets standards set by your best people.
01 / The problem
Companies are telling employees to use AIwithout defining where it should be applied, what good looks like, or who is accountable for results.
Priorities are left to individuals
Employees decide for themselves which work to automate, how to approach it, and which tools and models to use.
Quality depends on the user
The same work produces different results, with no shared criteria grounded in the organization’s best historical work.
Value is never underwritten
Time savings are assumed without a baseline, a plan to capture the capacity created, or a view of the full cost to operate the workflow.
No one owns performance
Quality, failures, usage, model selection, and cost receive little oversight after the initial experiment.
02 / Why Websley
Built for production
Websley identifies and underwrites the right opportunities, then builds them into dependable production workflows. After launch, we manage performance, optimize cost, and measure realized value.
Proof before you commit
Every engagement delivers a working prototype on your data before either party commits to a production build.
Standards set by your best people
Performance is measured against real historical work, guided by the people whose judgment you trust most.
Managed after launch
After a workflow ships, we manage its quality, reliability, usage, and cost, and we report the value actually realized.
03 / Where it applies
Representative workflows
We target workflows that run every week or every month, consume meaningful team hours, and have a clear standard for good output. We build workflows that fit this profile, at the fund or inside a portfolio company.
Deal evaluation
CIM screening, initial readouts, and data packs, built from the data room and delivered in your own templates.
Portfolio monitoring & reporting
KPI collection, variance narratives, and LP materials, normalized across every portfolio company.
| Revenue | EBITDA | Net debt | |
|---|---|---|---|
| Portco A | 4.2 | 1.1 | 6.8 |
| Portco B | 7.9 | 0.6 | 11.2 |
| Portco C | 3.1 | 0.9 | 2.4 |
Operating‑company functions
Month-end close, forecast preparation, and reporting inside the portfolio company’s own systems.
04 / The engagement
Assess, then build, then run
A structured evaluation of candidate workflows, a tested prototype on your data, and an underwritten value case. There is no obligation to continue into Build.
The validated workflow goes into production in your environment, with data connections, deterministic controls, human review, and testing against agreed acceptance criteria.
After deployment, we monitor quality, reliability, usage, and cost. As model capabilities and pricing evolve, we retest and improve the workflow and measure realized benefits against the original value case.
05 / Where it runs
Built alongside the tools you already use
Workflows connect to your existing applications, files, and data sources without rip-and-replace implementations or disruption to how your teams work today.
Control
Designed for companies that handle confidential information
Your data stays in your systems
Client data remains in your approved systems. Only workflow-relevant content goes to model providers you approve, and it is never approved for use in model training.
You set the boundaries
Each workflow operates within predefined data, system, and action boundaries. Higher-risk actions require explicit approval.
Limited visibility by design
Websley sees how workflows perform, not what your documents say. Operational logs shared with us exclude document contents.
06 / The firm
The work we automate is work we have done
Websley was founded by Colin Brosnan, a former private equity associate who built and deployed AI agents for investment and portfolio workflows. Before that, he spent two years at McKinsey & Company and studied computer science at the University of Michigan. He has screened the CIMs, assembled the data packs, collected the portfolio KPIs, and he knows where the hours go.
Start with two weeks
The Assess phase ends with a tested prototype on your data, an underwritten value case, and a scoped path to production.
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