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akta.pro
Private company data and signals API for the agent economy
159 followers
Private company data and signals API for the agent economy
159 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.
akta.pro
Hey Product Hunt,
Sid here, co-founder at akta.pro. Private company data and signals API for agents, priced by consumption.
We were building AI agents for private markets and kept hitting the same two dead ends.
Legacy databases have company data, but they gate it behind a UI, charge by seat, carry no signal layer.
Search APIs return what ranks on SEO. Your agent burns tokens reading 100s of pages to find one event.
akta.pro gives an agent both sides, already structured.
Company data: 20M+ companies with 70+ fields, i.e., 2x coverage of PitchBook, 4x depth of ZoomInfo/Apollo.
Fundamentals: firmographics, management, funding history, investors, revenue and financial estimates.
The fields most databases skip: competitive moat, gtm motion, business model, tech stack, AI maturity.
News and signals: monitor a company, a sector, or a topic in plain language.
Feeds are de-duplicated, matched to the right company, and scored for impact and sentiment.
Tagged across 100+ event types like funding round, exec change, expansion etc., that work as triggers.
Alternative signals tied to the same company ID: headcount trends, web traffic, jobs, social posts, reviews.
On cost, company data runs 5x cheaper than legacy databases like PitchBook. News runs 10x cheaper than putting the same work through Claude or Parallel search APIs, where retrieval comes out of your token spend.
Used today by AI builders shipping agents, GTM teams triggering outbound, and investors screening startups.
Works as an API, over MCP, or CLI.
Try for free. Code PH50 gets you 50 credits: playground.akta.pro/signup/?coupon_code=PH50
If you have evaluated legacy databases, news or search API providers, or GTM intent data, I am happy to do a specific comparison in the comments.
Here all day, and the critical feedback is the useful kind.
PicWish
@siddhant_masson you mentioned dropping 80% of noise before a response. how are you handling entity resolution upstream to match messy news feeds to the right 20M+ company IDs without blowing up latency?
akta.pro
@mohsinproduct Resolution isn't in the query path at all; tagging happens at capture.
At ingestion, we first use fast candidate-generation and filtering layers—names, aliases, domains, locations, industry/context signals, and our company graph to reduce 20M+ entities to a very small candidate set. Only then do semantic NLP and our in-house resolution models score and disambiguate those candidates. Confidence thresholds determine whether we accept the match or run additional resolution passes. The core algorithm is built in-house and patent pending.
This keeps resolution latency manageable even at feed scale. More importantly, resolution happens at capture, not in the query path. By request time, each article is already mapped to a company ID, so serving is essentially a lookup against a pre-resolved index.
That upstream resolution is also what lets us confidently remove ~80% of irrelevant feed noise. Our recent benchmarks show 93%+ entity-mapping accuracy, ahead of frontier reasoning models on the same task: akta.pro/benchmarks
@siddhant_masson Many congratulations Siddhant, Shiv, Neeraj and team! 😊
When I first met Neeraj discussing the challenges of building AI agents for private-market research. What stood out immediately was how clearly they understood the gap: traditional databases lock valuable company data behind expensive interfaces, while search APIs often return too much noise and consume excessive tokens.
Akta pro brings both sides together through a structured API: deep private-company data across 20M+ companies, enriched with 100+ real-time signals covering funding, hiring, leadership changes, partnerships, expansion, and more.
I’m endorsing it because the team has focused on the details that matter: clean entity resolution, deduplicated data, relevant signals, and consumption-based pricing. It makes private-company research faster, more intelligent, and far more useful for building automated workflows.
It’s built for AI agents, investors, and GTM teams that need actionable intelligence. Definitely worth checking out if you’re working in AI, financial research, or B2B growth. :)
I like that this goes beyond basic company profiles. Signals around funding, hiring, and other events can add much more context.
akta.pro
Thanks @awesome_america. Exactly, profiles and firmographics are table stakes at this point, so beyond the profiles we built out signals as well, which is where we saw the real gap.
The triangulation beyond what company's website says, such as job postings, partnerships, hiring etc. is where you unlock unique intelligence.
Real-time event triggers make it actionable for developers, sellers, investors.
There are also structured fields for company assessment, GTM motion and tech capability, so you get a view on how a business actually operates and competes rather than just what it looks like on paper.
The 100+ event signals are what caught my attention. Having those alongside company data could make research much faster.
akta.pro
Thanks @alan_robert, and that pairing is exactly why we built both instead of picking one. An event on its own only tells you something happened, so the useful version is being able to pull the full company picture in the same breath.
Full list of type codes is here if you want to see the range: News Types - akta.pro API Documentation.
How often are company profiles updated? Private company data can change pretty quickly.
akta.pro
@yahya_rogers Good question - our refresh is field-aware and varies across 70+ fields.
Event-driven fields are updated real-time across thousands of publishers. Funding rounds, transactions and leadership changes get picked up by the real-time news pipeline as the event breaks, then reconciled onto the company profile
Alternative signals run closest to live. Job posts and web traffic are near real time. Headcount updates monthly, since it's only meaningful read as a trend rather than a point-in-time number
Stable firmographics like legal name, company type, founded year and industry codes run on verification cycles that are dynamically scheduled rather than fixed. How often a company gets re-checked depends on its size, news volume, website updates and overall activity, so the ones actually moving get looked at more often than the ones sitting still.
Happy to go deeper on any specific field if there's one you care about.
akta.pro
@yahya_rogers Great question.
Refresh is field-aware rather than one global interval, since volatility differs enormously across the 70+ fields. Stable firmographics such as legal name, company type, founded year, industry codes run on verification cycles that are dynamically scheduled rather than fixed.
How often a company gets re-checked depends on its size, news volume and overall activity, so the ones actually moving get looked at more often than the ones that aren't.
Event-driven fields like funding rounds, transactions and leadership changes are picked up by the real-time news pipeline as the event happens, then reconciled onto the profile weekly.
Alternative signals are close to live: job posts & web traffic can be found near real time, and headcount monthly since it's read as a trend rather than a point.
Having more coverage is great, but the real test is whether the data stays actionable for teams using it daily.
akta.pro
@aria_turner Completely agree, and it's the right test. Coverage is the easy axis to compete on; what kills daily use is noise — an analyst who gets 200 items a day stops opening it by week two. So we optimised for precision over volume and drop roughly 80% of what comes in before it ever reaches a response.
The event types such as funding, hiring, layoffs, partnerships etc. in the news signals endpoint can help make it actionable for teams using it daily.
Check out the benchmarks for signals precision and recall here: https://akta.pro/benchmarks/company-news-retrieval
@siddhant_masson Exactly, I think that’s where a lot of data products struggle. Having more information is easy, but turning it into signals teams can actually trust and act on daily is the harder part.
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.
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.