LightfieldAI-native CRM that builds itself and does work for you
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akta.pro
Private company data and signals API for the agent economy
260 followers
Private company data and signals API for the agent economy
260 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 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.
Nas.com
How do you handle companies with very limited public information?
akta.pro
@nuseir_yassin1 for such instances we rely on alternative data such as social media, blogs, podcasts etc. if there is limited information the company itself. We leverage a smart validation logic - if company has very limited public data, not all parameters are populated - only the one's which can be backed by sufficient data are populated while others are left out. Prioritizing accuracy and fidelity yet retaining good fill rates.
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.
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.
I question how teams measure success after using this API. would the main benefit be saving research time or finding better business opportunities?
akta.pro
@new_user___209202627e87af67bf41b28 It shows up as time first and opportunities later. Week one is usually the boring win, where the cleaning and matching layer that used to sit in front of every agent just goes away, because the company data comes back structured and the news comes back already filtered instead of as pages to read through. That is also where the cost drops, since retrieval stops coming out of token spend. The better part comes later, when the signals start doing the work for you and a funding round or an exec change surfaces without anyone going to look for it. GTM teams count that in meetings booked, investment teams in deals they saw before the round was announced.
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