inPact builds enterprise software that unlocks contracts as an accessible source of data & visual insight to help business teams make better strat decisions.
The founding team started the company having been selected by Entrepreneur First, a leading talent investor funded by Reid Hoffman (founder of LinkedIn), the founders of DeepMind and PayPal, amongst other leading investors globally (under 3% acceptance rate). We raised institutional funding within only two months of conceiving idea while in the middle of the global pandemic.
With under 10 weeks of our early fundraise, inPact launched its first version of the product through an Alpha program with two companies, who then worked closely with us to provide detailed feedback on performance and features.
For one company, they were looking to keep track of their long-term client contracts and automate revenue analysis. The intention is for our tool to: (i) track termination and renewal dates and alert our client to engage end-customers to avoid churn and create upsell opportunities, (ii) conduct pricing analysis by comparing pricing against services rendered under each contract by segment; and (iii) create forecasts for revenue under contract / revenue at risk (i.e. expiring contracts). The other company uploaded a number of partnership agreements across various markets in dual-language contracts with different negotiated terms. They used our tool to keep track of termination and renewal dates in order to decide whether to terminate, negotiate for better terms, or renew.
Since the end of the Alpha program, we have continued development on technology and product, revamping the design completely to provide a better user experience. We have also continued to improve our backend ML infrastructure, which enables us to provide our core technology offering better.
Currently, we are working on a paid pilot with a large merchandiser (over $100m annual turnover) that wants to automate the processing of a large volume of purchase orders it receives from well-known retailers. Our OCR technology plays a significant role in this pilot, as the information is displayed in complex tabular formats.
Recently, inPact was also one of the seven companies accepted into the first legaltech accelerating by top ANZ firm Allens. We have also been admitted to NVIDIA’s Inception Program, the premier program for AI and Data Science startups; and globally renowned 500 Startups’ Global Launch SF acceleration program with a view to expand into the US market.
Problem or Opportunity
Our mission at inPact is to to make contracts a useful and accessible source of data and insight to business decision-makers at large enterprises, beyond just legal departments.
Gartner studies show that 97% of these documents are left to collect dust at organizations, and the information contained within them remains unused.
And unsurprisingly, this study also found that 87% of companies had low capabilities in their ability to analyze their data and extract business insight.
This lack of visibility over their own internal data can cost companies the equivalent of 10% of the top-line each and every year.
In particular, contracts are one of the most valuable sources of business-critical information within an organization, representing every asset, liability and relationship of the company.
However, given the technical legal language and unstructured nature, the information they contain is difficult to access and therefore rarely used in decision making.
Solution (product or service)
inPact's software is capable of:
- Reading through thousands of complex company contracts at a time;
- Extract the key commercial information and business terms into a structured database;
- Run business analysis out of the box, generating key business metrics and visualizations that drive decision making across corporate functions like growth, procurement and finance; and
- Present them in a format that makes it easy to illustrate and report the findings.
For example, our software can read through thousands of client service and sales agreements in minutes, extract the type of services and pricing formula, and calculate metrics like average contract value, trends around deal size and pricing with breakdowns by product and type customer. This helps the client make decisions about sales, marketing, pricing and resourcing strategies.
Ultimately, the end goal is to help clients better leverage their own data to optimize the way they run their business. By doing so, we estimate we can claw-back at least 10% of the value leak mentioned, which for our average target enterprise client with an annual turnover of $250m amounts to $2.5 million value capture per year.
In the absence of our solution, we have seen several approaches attempting to use contract data to seize business opportunities and/or prevent value leakages: A. Manual contract data entry (i.e. keep excels with contract information fragmented in team silos): Very poor data quality due to lack of discipline in manual data entry. Minimal contract details are recorded - insufficient for business analysis. Siloed records prevent cross-department intelligence.
B. Impromptu teams to clean up the mess: Rounding up operations and strategy staff to manually review contracts, manually record and analyze data for business purposes. This takes a long time, deviates overqualified resources from core business functions and is not a continuous / sustainable solution. After the exercise, it gets worse again.
C. External consultants to clean up the mess: Similar to above but paying 6-digits to a big four firm to spend 3-6 months manually reviewing contracts and data from other systems of record to make one-off recommendations. A lot of value has been lost already by the time the company authorizes budget to hire external consultants, and again this is not a sustainable solution. D. Major contract management transformation: Implement full contract lifecycle management software and processes both pre- and post-signature. It can take over a year, involves external consultants and has a high risk of failure as it involves changing core operating practices across departments, creating new roles, and going through an arduous change management process. The value of post-signature analysis of contract data is very limited as it depends on pre-signature conclusion of the contracts on-platform, which is utopic (not all counterparts will agree to it).
Advantages or differentiators
Until recently, most AI-powered contract analysis software have focused on helping lawyers review contracts more efficiently and effectively. They are not designed to extract and analyse commercial data and terms useful to business stakeholders, which requires a fundamentally different product, from UI/UX to training data sets, processing pipeline and machine learning models.
We believe the potential value captured using the contract base of a company as a source of business intelligence, and have designed that product from the grown-up specifically for that purpose.
And we are not the only ones to see this:
- Sequoia-backed SirionLabs has been blending contract management software with consulting services for business improvement - Instabase raised over 100m from Index Ventures, A16z and others to build software that analyses unstructured documents for automation and insight taking a low-code DIY approach, - More recently, YC company Klarity in SF raised a 2m seed round to turn contracts into structured data to be funnelled to traditional ERPs through integrations
However, we view these competitors as striking different sub-optimal trade-offs between scalability and delivery of insight.
Our unique value proposition is offering pre-configured business analysis by functional use case right out of the box. For example, revenue and churn analysis for the head of sales, working capital and vendor management for procurement and finance. You can think of these analytical dashboards as in-app subscriptions capable of delivering immediate value with minimal change management or involvement of IT and or superusers.
Money will be spent on
The investment will be spent primarily to build-up our product and sales teams. We plan to hire a head of product, two full-stack engineers and a data scientist on the product side, together with a head of direct sales and two account managers on the distribution side.