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akta.pro
Private company data and signals API for the agent economy
618 followers
Private company data and signals API for the agent economy
618 followers
Private company data with 4x the depth and 2x the coverage of PitchBook, plus 100+ event signals and news across companies, industries, and topics. Source and diligence deals or make outreach lists and trigger outbound. Built for financial services and GTM teams, pay-as-you-go.
How do you keep the event signals fresh, especially when a company has very little public information?
akta.pro
@lucyjane great question. Besides leveraging alternative data from social feeds (x, LinkedIn, Youtube, blogs etc.) to supplement news / press coverage, we go beyond to find relevant event signals. We tier event signals into primary, secondary, and indirect. While there may be little coverage where the company in question is the primary subject, often times the company may be mentioned alongside other companies, although not the main subject of that signal (secondary). Further, there are indirect event signals based on broader industry (e.g., regulations, m&a) or competitors (layoffs, c-suite hiring, bankruptsy, launch, price drop) that have implications on the company being tracked.
How granular are the sentiment and impact scores? Can developers access the underlying scoring fields through the API??
akta.pro
@zerotox yes, numeric scores on a scale of 0 to 1. Raw scores are available in the api response for developers to use.
I’ve worked with company research tools before, and keeping data organized is usually the hardest part.
akta.pro
@grant_w1 Agreed, and it's most of the work. We do the organising upstream rather than at query time — resolution, dedup, categorisation happen before anything lands in a response, so what you get back is already settled. Less elegant than it sounds, but it's the only way the latency works.
@siddhant_masson That makes a lot of sense. Handling the messy part before it reaches the user probably makes the API much more reliable.
The challenge seems less about collecting data and more about keeping the relationships between companies, events, and sources accurate over time.
akta.pro
@grant_w1 That's the part people underestimate, and private companies are the hardest version of it. Public companies have a ticker and a filing calendar, so identity and timing are both solved for you. Private companies have neither.
So the organizing problem comes first. Entity resolution is the foundation everything else sits on: resolve identity once, across 20M+ companies under one stable identifier, and every dataset after that inherits it.
The refresh design follows from the same idea. Once records are resolved to a stable entity, you can update fields at different rhythms and still have them land on the same profile, so a funding round from the news pipeline and a headcount trend from a completely different source both attach real-time to the same company without creating duplicate entries or needing manual interventions that a legacy database needed
@saswat_nanda2 That’s a good point. Having a stable entity layer seems like it becomes the backbone for everything else, especially when different sources update at different speeds.
A lot of tools can collect more data, but keeping the same company context intact as that data changes is probably the harder problem.
Documentation.AI
As a developer, having this available through API, MCP, and CLI makes the product flexible and interesting to use. Congrats on the launch!
akta.pro
@roopreddy Thanks Roop. That was the intent,. We wanted the choice of surface to be about how you work rather than a tradeoff, so API, MCP and CLI all return the same shapes.
Lancepilot
20M+ companies with 70+ fields is a serious amount of coverage. Congrats to the team! 🚀
akta.pro
@priyankamandal Thank you! Coverage was the part we spent the longest on, since a private markets API is only as useful as the tail it reaches.
Worth adding that the 20M+ isn't a fixed ceiling either. If you hit a company that isn't in the database yet, there's a company addition endpoint that takes the submission and builds the profile. It's free and asynchronous, so you fire the request and either poll the status endpoint or take a webhook when it lands.
Practically that means a miss on an obscure company becomes a short wait rather than a dead end.
The combination of private company data and real time signals is probably more useful than either one along.
akta.pro
@veronica_ivy spot on, either data in isolation is commoditized. Tying signals to companies (particularly private) with entity resolution and de-duped is where the combination starts providing utility.
Love that you built this API-first for AI agents rather than dashboards, giving agents structured company signals to act on feels like the real unlock here.
akta.pro
@ilko_kacharov thanks Ilko. With API-first approach, you basically give the end user (whether developer or business) to customize their own way of digesting information. Interestingly, lot of business users are using MCP with Claude to create extremely versatile dashboards / mini-apps.
akta.pro
@ilko_kacharov Thanks, and that's the bet. We believe strongly that the future is headless - what matters then is whether the data arrives in a shape an agent can act on: resolve identity, deterministic schemas, signals structured. Build for that and the interface question mostly answers itself, since anyone can put whatever surface they want on top.