Staticent - Strategic Finance Powered by AI

Straticent is a cutting edge, Agentic AI platform, built fully AI native by the leading finance and tech experts in the world, meant to fully automate your entire Strategic Finance function. Life is too short to be doing budget vs variance analysis, leave it to our AI agents, they just don't automate your finance function, they straight up adopt it, raises them on its own and sends you postcards whilst you chill on a tropical island.

Ok I am all out of buzzwords now.

Well, the actual goal really is fairly straight forward. Make the process of taking business decisions as data driven as possible. Large companies that can afford a big finance team and sufficient IT support usually have this data available to them. Medium and small companies? Not so lucky.

Most small businesses don't even have a single source of truth. Financials are in your accounting software, customer and invoicing details in billing, manpower costs in HR, expenses in a wide combination of mail threads, bank statements and vendor invoices. Even if they do have all of the data in one place, they are never organized in a meaningful way; data needs to be timely, relevant (for the context) and most importantly has to tell the story of what is happening.

In fact, the only place where this is currently available (albeit manually) is in a detailed financial model (with cost and revenue drivers). For most mid-market companies, this is limited to specific milestones like fundraising and almost never to make regular yet important decisions. FP&A Softwares have solved the data aggregation problem with API integrations, but their adoption outside of the enterprise segment is less than 10%1.

Why Current SaaS Tools Fall Short

SaaS tools have some inherent limitations which make them irrelevant and not feasible for most companies.

1. Lack of Dedicated Resources

Most companies with less than $50-75M in revenues don't even have large enough finance teams for dedicated FP&A function, rather a single team of 10-15 people depending on the size and complexity. They handle everything right from vendor management, compliance, treasury, budgeting, diligence and accounting. Consequently, a proactive approach viewing decision making through a detailed matrix of cost and revenue drivers is a multi week project reserved for specific occasions – fundraise, significant campaigns, pricing changes or a large build vs buy decisions.

2. The implementation mountain

SaaS tools come with an implementation problem – significant upfront investment of both time (minimum 3-6 months) and cost >$25k per annum to even get started. It is sort of like trying to rent and move into a house, with just a few images of the house. And you constantly need the packers and movers every time you move a table (pun intended). Implementation is just a fancy term for the vendor's onboarding team to force fit your revenue and cost drivers into their rigid templates – works well for a mature business model with no major changes but doesn't work for a business still experimenting with their pricing, positioning in their growth stage.

3. One-Size-Fits-All Approach

Mid-market SaaS softwares followed the same theme as larger enterprise planning tools like Anaplan and Workday's Adaptive Insights. They offered the same features – access controls, high level scenario analysis, standard BvA templates, preset reporting (read graphs from power bi) for companies which are far younger in their life cycle. A large company using these tools, use them with their army of finance team and IT support, to standardize the process. Large companies also have very different objectives – shareholder guidance, ensuring quarterly results are close to estimates, organizing a budgeting process spread across multiple segments. For significant decisions, they have the luxury to rely on their dedicated finance team to do the work in excel and go back and update their budgets post approvals.

Mid-market FP&A SaaS never evolved beyond a standardizing tool, which like I mentioned earlier is irrelevant for a dynamic business, with a small finance team. The most tell-tale sign – request for any product demo and you will go through an assessment call to see if your business is "fit for their product".

4. Prohibitive Costs

And finally, the current average cost of $30-75k per annum plus maintenance mean tools for fractional CFOs are virtually non-existent. Fractional CFOs don't make enough money from their clients to make this economics work. Most companies until they hit $20M revenues can actually do very well with a fractional CFO; they also most likely can't afford a full time CFO, (a 175k+ per annum CFO + two junior finance people at $75k+ vs fractional CFO at ~ 3-5k per month + 3 salespeople is a no brainer for any fast-growing company).

Even for companies with a small finance team, a tool at an average of $30-75k per annum is an expensive proposition for automating data collection and standardizing reports since they will need people to work on it anyway and no less than 7-10 days a month if one has to do it properly; the team is better off hiring a junior finance person for the same cost.

So, what is our glorious plan to solve it?

In the post LLM world, now it is possible to produce autonomous systems that can make strategic finance seamless and available to everyone. An AI native platform can make the grunt work i.e planning, analysis, tracking, iterating easier with bulk of the work being done by AI; so the finance professional can spend their time on what matters the most – asking the right questions, taking the right decision (backed by data to the extent possible).

An AI engine specifically trained on relevant industry context, offer two massive advantages over rule based softwares:

1. Scalability and Flexibility

It is far easier to build a financial model and make changes with NLP than manually doing it in excel. No need for extensive implementation or constant hand holding for every change in the business model.

2. Autonomous Agents

Can do bulk of the work by themselves – BvA, Root Cause analysis, scenario modelling, error detection etc. A well-trained Agentic AI system can bring down most of this regular work by more than 80%.

LLMs alone are not the magic fix. They still throw random hallucinations, most outputs lack relevant context, and definitely not have or offer the industry know-how in detail. But LLM is an indispensable and important glue that can power a hybrid AI system i.e proprietary domain specific intelligence layer + LLMs that makes this whole process as seamless as possible.

Why This Matters Now More Than Ever

Dynamic proactive decision making is more relevant now than ever. Rapid adoption of AI across the board, consolidation by Private Equities (HVAC, CPAs, Law firms), glut of VC capital for new entrants are threatening old business playbooks across industries. Two weeks of analysis, one week of review, one week of decision making simply won't cut it. Unlike the last two decades, companies that don't move forward, actually move backwards now.

Critical questions like below shouldn't be a multi week finance + IT project

What is the runway of my company or surplus cash that would be available in the next 8 weeks after accounting all the receivables and payables?
What would be the impact of pricing change for my repeat customer cohort?
Is the company managing its working capital efficiently? How much cash are they leaving on the table due to early payment to vendors and regular overdue receivables? Should they encourage early payment with discounts and use the funds elsewhere?
How should a SaaS company adding AI modules, price their product for different customers? What would be the gross margins after factoring in token usage which are not directly linked to the prices? Is there a potential to increase pricing based on the outcomes we are offering?
What happens when old economy businesses like Manufacturing and Industrial Services adopt new AI tools for support functions? What is the impact on my margins?
How can a founder who has one eye on eventual exit to a roll up private equity – standardize his operations and escape the dreaded Adjusted EBITDA hammer?

Our Vision

This is at the essence of what we are trying to solve. Every business should be able to rely on a digital assistant which can do the grunt work – aggregate, analyse, normalize data, understand past trends and forecast future scenarios. Founders and CFOs should use their expertise and intuition to focus on what matters, backed ably by a digital ally.

Focus on the strategic, delegate the repetitive!

Why Straticent?