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.
@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.
Reasoning models changed the economics of proprietary data. For years the winning move was a large team of analysts manually cleaning, normalizing and QAing structured datasets. That model is going obsolete.
Demand growth is in agents now, and agents do not want a handful of structured fields in a subscription model. They want a large, reliable corpus of structured qualitative knowledge they can reason over and scale consumption as needed. That changes how you architect the platform.
So we made entity resolution the foundation everything else sits on. Every company gets a canonical identity connecting parents, subsidiaries, products, executives, investors, news and hiring signals.
Once identity is solved, coverage stops being a fixed list you either have or lack and becomes an extensible graph, where anything new resolves against identities that already exist, including data you bring in yourself.
The real-time news pipeline runs on those same identities, with no batch refresh window. Every article goes through:
Deduplication and entity resolution back to company IDs
Classification against an 100+ event taxonomy
Mapping to NAICS, SIC, IAB and IPTC codes
Scoring for impact, sentiment and story centrality
Each of 20M+ companies are tagged to 30k+ industry codes and qualitative source-traceable data across 70+ fields.
The noise gets filtered along the way, and whatever survives is queryable the moment it lands.
The schema is built for reasoning over a corpus rather than browsing rows in a dashboard, which changes the interface too. Everything is API-first, with MCP and CLI as first-class surfaces built for token efficiency and composability inside agent workflows.
The goal is simple: make private markets data programmable for anyone building with AI.
I enjoy seeing tools that improve private market research. I would ask how akta.pro manages global coverage since company information varies widely across regions and markets.
Report
I question how teams measure success after using this API. would the main benefit be saving research time or finding better business opportunities?
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.
akta.pro
Happy to go deep on the technical side.
Reasoning models changed the economics of proprietary data. For years the winning move was a large team of analysts manually cleaning, normalizing and QAing structured datasets. That model is going obsolete.
Demand growth is in agents now, and agents do not want a handful of structured fields in a subscription model. They want a large, reliable corpus of structured qualitative knowledge they can reason over and scale consumption as needed. That changes how you architect the platform.
So we made entity resolution the foundation everything else sits on. Every company gets a canonical identity connecting parents, subsidiaries, products, executives, investors, news and hiring signals.
Once identity is solved, coverage stops being a fixed list you either have or lack and becomes an extensible graph, where anything new resolves against identities that already exist, including data you bring in yourself.
The real-time news pipeline runs on those same identities, with no batch refresh window. Every article goes through:
Deduplication and entity resolution back to company IDs
Classification against an 100+ event taxonomy
Mapping to NAICS, SIC, IAB and IPTC codes
Scoring for impact, sentiment and story centrality
Each of 20M+ companies are tagged to 30k+ industry codes and qualitative source-traceable data across 70+ fields.
The noise gets filtered along the way, and whatever survives is queryable the moment it lands.
The schema is built for reasoning over a corpus rather than browsing rows in a dashboard, which changes the interface too. Everything is API-first, with MCP and CLI as first-class surfaces built for token efficiency and composability inside agent workflows.
The goal is simple: make private markets data programmable for anyone building with AI.
Try for free. Code PH50 gets you 50 credits, no card required: playground.akta.pro/signup/?coupon_code=PH50 — If anything in the docs at https://docs.akta.pro/ is wrong or hard to follow, tell me and it usually gets fixed the same day
I enjoy seeing tools that improve private market research. I would ask how akta.pro manages global coverage since company information varies widely across regions and markets.
I question how teams measure success after using this API. would the main benefit be saving research time or finding better business opportunities?
TestMu AI
Congratulations on the launch! Private-market data definitely needed something more developer-friendly for a while. Akta seems like a great solution.
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!
Nas.com
How do you handle companies with very limited public information?