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.
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.
@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.
@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.
Report
Really like the consumption based pricing approach. Paying for actual usage makes much more sense for agent workflows.
@imtiaj_ahmad that's true. For so long high quality datasets were tied to large enterprise contracts, but that doesn't work for the agent economy. Our endeavor is to provide institutional grade data with consumption based business model to lower the barrier for entry for agent developers all over the world.
Congrats on shipping! Reliable private company data through a clean API is essential for autonomous AI agents to make real decisions. Simple, high-utility infrastructure for dev teams.
@thisiskp_ Thanks! Reliable is the word that matters most there, and in practice it comes down to schema and data consistency. Same shape every call, stable field names, nulls where data genuinely doesn't exist. That means you write the parsing logic once and it keeps working, instead of defensive handling around every field that might or might not show up.
@syed_shayanur_rahman Thanks. In most legacy private markets tools the API is an afterthought. Responses come back bulky, the structuring is off, and you end up writing a normalization layer before the data is usable.
MCP usually repeats the problem. What gets exposed is a subset of the API wrapped once and shipped, so you inherit every constraint of the original surface plus a few new ones.
We built the other way round. Structured JSON with deterministic schemas, full access across every offering rather than a curated slice, and rate limits set for how agents actually query. The MCP server and CLI are designed around real workflows, not mechanical wrappers over endpoints.
Would love to hear feedback on what is working and what is not
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.
akta.pro
@imtiaj_ahmad that's true. For so long high quality datasets were tied to large enterprise contracts, but that doesn't work for the agent economy. Our endeavor is to provide institutional grade data with consumption based business model to lower the barrier for entry for agent developers all over the world.
Netlify
Congrats on shipping! Reliable private company data through a clean API is essential for autonomous AI agents to make real decisions. Simple, high-utility infrastructure for dev teams.
akta.pro
@thisiskp_ Thanks! Reliable is the word that matters most there, and in practice it comes down to schema and data consistency. Same shape every call, stable field names, nulls where data genuinely doesn't exist. That means you write the parsing logic once and it keeps working, instead of defensive handling around every field that might or might not show up.
TestMu AI
Congratulations on the launch! Private-market data definitely needed something more developer-friendly for a while. Akta seems like a great solution.
akta.pro
@syed_shayanur_rahman Thanks. In most legacy private markets tools the API is an afterthought. Responses come back bulky, the structuring is off, and you end up writing a normalization layer before the data is usable.
MCP usually repeats the problem. What gets exposed is a subset of the API wrapped once and shipped, so you inherit every constraint of the original surface plus a few new ones.
We built the other way round. Structured JSON with deterministic schemas, full access across every offering rather than a curated slice, and rate limits set for how agents actually query. The MCP server and CLI are designed around real workflows, not mechanical wrappers over endpoints.
Would love to hear feedback on what is working and what is not