An AI platform for evidence-backed software decisions.
A platform that replaces months of manual software research with instant, evidence-backed recommendations. Built for companies that need to make better buying decisions, faster.
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Book Your CallA platform for better buying.
The problem
Choosing the right software for a business takes months of manual research, vendor demos, and spreadsheet comparisons. Most teams end up picking based on gut feel rather than real evidence.
The approach
We built a platform that automatically researches and scores software options across five dimensions, then gives decision-makers clear, evidence-backed recommendations in seconds.
Months of research, gut-feel decisions.
A company needs new software. Someone searches online, opens dozens of tabs, and closes most of them because pricing is hidden. A spreadsheet gets created. Meetings happen. Vendor demos eat weeks. By the time a decision is made, the research is already outdated.
After months of back-and-forth, the team typically picks the option with the best sales pitch, not the best fit. Confidence in the decision is low.
For companies evaluating software as part of a business deal or acquisition, the problem is even worse. Hundreds of hours of analyst time, manually reading documentation and comparing features, with no clear way to verify whether the information is still accurate.
Research, score, cite.
The platform automatically researches software products, pulls information from vendor documentation and review sites, and scores each option based on how well it fits what the buyer actually needs.
Every data point is linked back to its original source, so users can see exactly where a recommendation came from and how confident the system is. Nothing is assumed.
The result is a ranked list of options with clear explanations. Which features match, which are missing, and why one product scores higher than another. No black box.
Five ways we evaluate a product.
Each product is scored across five dimensions. The final score reflects a weighted combination of all five, so the most important factors carry the most weight.
Must-have features
The features the buyer marked as essential are weighted heavily. Features that are missing penalise the score. Nice-to-have features add a small bonus.
Category fit
How well the product's category and use-case tags match what the buyer is looking for. A CRM built for enterprise sales will not score well for a small e-commerce team.
Verified specs
Specific claims about the product (pricing tiers, integrations, compliance certifications) are verified against source documentation. Each one carries a confidence rating.
Use-case alignment
The buyer's description of what they are trying to do is matched against how the product describes itself. Catches alignment that structured fields sometimes miss.
Data quality
Products with complete, up-to-date information score higher than those with gaps. Partial data is flagged so buyers know where to dig deeper.
Ask questions, get grounded answers.
Alongside the scoring engine, we built an AI assistant that lets users ask plain-language questions about any product. It draws on the same verified data (not the internet, not the vendor's marketing) to give grounded answers.
Users can build a buying profile through conversation, compare two or more products side by side, or dive deep into a single product's strengths and gaps. The assistant knows when it does not have enough information and says so.
How we keep data fresh.
The platform continuously researches products in the background. It reads vendor documentation to extract specific features and pricing, then cross-references review sites to understand how real users experience the product.
Every piece of data is tagged with a confidence score and a link to where it was found. When information changes, the system updates automatically. Buyers always see current data, not a six-month-old snapshot.
What this tells us.
Show your working.
Every recommendation links back to its source. Buyers do not have to trust a score. They can see exactly what drove it and verify it themselves.
Depth beats speed.
The platform does not just say a product has a feature. It says which pricing tier includes it, how confident the system is, and where that information came from.
Know when you do not know.
The system flags low-confidence data and incomplete profiles rather than guessing. Honest uncertainty is more useful than false confidence.
Scores should be explainable.
Every score breaks down into five clear dimensions. Buyers can see what matched, what was missing, and what hurt a product's ranking.
Want the same result on your stack?
Tell us what you need automated. We audit your workflows and show you the ROI before writing a line of code.