Recallr AI

Cover Block

Publicly reported

The following details establish the basic profile of Recallr AI.

Attribute	Detail
Company Name	Recallr AI
Tagline	Intelligence and memory layer for private capital firms
Headquarters	San Francisco, CA
Founded	2025
Stage	Pre-Seed
Business Model	SaaS
Industry	Fintech
Technology	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding Label	Pre-Seed

Note: Total disclosed funding is not confirmed via public filings or investor announcements.

Links

Publicly reported

• Website: https://recallrai.com/
• LinkedIn: https://www.linkedin.com/company/recallrai
• GitHub: https://github.com/recallrai

Summary and Signal

Publicly reported Recallr AI is building an intelligence and memory layer for private capital firms, a bet that the most valuable AI infrastructure for finance will be the system that structures a firm's own historical judgment. The company ingests fragmented internal data, from deal memos to partner notes, and transforms it into a continuously updated, queryable decision graph that models how investment theses evolve over time [Recallr AI, Unknown]. Founded in early 2025 by Devasheesh Mishra, an AI/ML researcher, the company is targeting a narrow but high-value wedge within fintech: family offices, private equity firms, sovereign wealth funds, and venture funds [LinkedIn, Unknown].

The core product differentiates on temporal reasoning and knowledge-update handling, using a versioned knowledge graph that preserves historical data rather than overwriting it, a technical approach the company claims scored 97.5% on the LongMemEval benchmark [Recallr AI, Unknown]. The founding team's background is technical, anchored by Mishra, who claims a prior exit though details are not publicly disclosed [LinkedIn, Unknown]. The business appears to be bootstrapped, with no equity funding rounds or named investors confirmed, and operates as a SaaS model with an estimated $220K in annual recurring revenue for 2025 [GetLatka, Unknown].

Over the next 12-18 months, the key watchpoints are the conversion of early technical validation into named enterprise deployments within its target financial segments, the evolution of its go-to-market motion beyond a single founder, and whether it can attract institutional capital to scale against competitors building in the broader AI memory infrastructure space. One source, partially checked -- Core product claims and founder role are company-sourced; revenue and team size are estimates from a single third-party provider.

Taxonomy Snapshot

Axis	Classification
Stage	Pre-Seed
Business Model	SaaS
Industry / Vertical	Fintech
Technology Type	AI / Machine Learning
Geography	North America
Growth Profile	Venture Scale
Founding Team	Solo Founder
Funding	Pre-Seed

Company Overview

Publicly reported Recallr AI was founded in San Francisco in 2025, positioning itself as an intelligence and memory layer for private capital firms [recallrai.com]. The company's formation appears to be a solo venture by Devasheesh Mishra, who is listed as the founder and CEO across the company's website and LinkedIn profile [recallrai.com][LinkedIn].

Public milestones are limited to the company's launch and its early technical validation. The company's primary public achievement to date is its self-reported performance on the LongMemEval benchmark, where it claims a 97.5% overall score [recallrai.com]. There is no public record of formal equity funding rounds, accelerators, or major customer announcements.

One source, partially checked -- Company formation and founder role confirmed by primary website and LinkedIn. Financial and operational milestones are based on single, unverified sources.

The Product and the Stack

Public record plus analysis The core of Recallr AI is a system designed to ingest the disparate, unstructured documents that form the institutional knowledge of a private capital firm and structure them into a continuously updated, queryable decision graph [Recallr AI]. The product's stated goal is to transform internal data, including deal memos, diligence notes, meeting transcripts, and investment committee history, into a living intelligence layer that models how a firm's judgment evolves over time [Recallr AI].

This is built on a versioned knowledge graph that explicitly tracks changing facts, a technical approach that differentiates it from simple vector databases. The system maintains separate timestamps for event time (when a fact was true in reality) and ingestion time (when the system learned it), creating new 'version' nodes with 'supersedes' edges instead of overwriting past data [Recallr AI]. This architecture is intended to support temporal reasoning, allowing users to query the state of knowledge at a specific historical point or track how an investment thesis has changed. The company claims its system scored 97.5% overall on the LongMemEval benchmark, with 97.0% temporal-reasoning accuracy and 97.4% knowledge-update accuracy [Recallr AI].

From a user-facing perspective, the platform offers three recall modes with latencies ranging from approximately 400 milliseconds to 8 seconds, catering to different use cases like real-time chat or deep analytical queries [Recallr AI]. A documented integration allows Recallr to act as a forward proxy for Google Gemini, injecting relevant user memory context into each request [Recallr AI]. While the company's public focus is on financial firms, earlier descriptions framed the product as a persistent, queryable long-term memory layer for conversational AI systems more broadly [Recallr AI].

One source, partially checked -- Product claims are self-reported by the company with no independent third-party verification of performance benchmarks or architecture details.

The Market They Are Entering

Publicly reported

The market for institutional memory tools in private capital is emerging not from a lack of data, but from a critical overload of unstructured, temporal knowledge that legacy systems cannot query or contextualize. The core demand driver is the escalating complexity and volume of proprietary information within investment firms, where the ability to reason across time and past decisions is a direct competitive advantage.

Quantifying the total addressable market for a niche intelligence layer is challenging, as it sits at the intersection of several larger, adjacent software categories. The most direct analog is the market for AI-powered knowledge management and workflow tools within financial services. While no third-party report specifically sizes a 'decision graph for private capital' market, the broader enterprise AI in financial services market was projected to reach $28.5 billion by 2028, growing at a compound annual rate of 34% from 2023, according to a report by MarketsandMarkets [MarketsandMarkets, 2023]. The private capital segment, encompassing private equity, venture capital, and family offices, represents a high-value slice of this broader trend, where firms manage multi-billion dollar portfolios and have both the budget and the acute need for such tools.

Demand is propelled by several converging tailwinds. First, the generational transition within many family offices and private equity firms is creating a 'brain drain' risk, making the systematic capture of institutional judgment a business continuity imperative. Second, the proliferation of alternative data sources and the sheer volume of diligence materials per deal have made manual synthesis and recall inefficient. Third, the rise of large language models has created an expectation for conversational, context-aware interfaces to internal data, a capability that generic document search cannot provide. The product's focus on temporal reasoning directly addresses a key pain point in investment analysis: understanding not just what a company's metrics are, but how they have changed and why past decisions were made.

Key adjacent and substitute markets include general-purpose enterprise search platforms (e.g., Glean, Microsoft Copilot for Microsoft 365), specialized due diligence and deal management software (e.g., Affinity, DealCloud), and custom internal data lake projects. The primary competitive threat is not direct displacement but inertia, where firms continue to rely on a patchwork of these existing, less integrated tools. Regulatory forces are generally a tailwind; increasing scrutiny on investment processes and fiduciary duties creates pressure for more auditable, traceable decision-making records, which a versioned knowledge graph could theoretically support.

Enterprise AI in Financial Services (2023) | 9.5 | $B
Enterprise AI in Financial Services (2028 est.) | 28.5 | $B

The projected growth of the broader enterprise AI in financial services market suggests a receptive environment for specialized applications, though Recallr AI's success depends on capturing a meaningful portion of the high-end private capital segment within it.

One source, partially checked -- Market sizing is based on an analogous, broader sector report; specific TAM for the niche is not publicly quantified.

The Competitive Field

Public record plus analysis Recallr AI enters a competitive field defined by a split between general-purpose AI memory infrastructure and specialized knowledge management for finance, positioning itself as a hybrid that must defend its niche against both.

The competitive map is not monolithic but segmented by technical approach and target vertical. In the general-purpose memory layer category, companies like Mem0 and Supermemory offer foundational infrastructure for building persistent, queryable memory into AI applications, serving a broad developer base [Recallr AI]. These are horizontal platforms competing on API simplicity and scalability. In adjacent financial technology, substitutes include legacy portfolio management systems (e.g., eFront, DealCloud) and modern AI-driven diligence platforms (e.g., Affinity, Daloopa). These tools manage deal flow and relationship data but are not architected as versioned knowledge graphs for temporal reasoning across a firm's entire judgment history. Recallr's wedge is its vertical-specific focus on private capital's internal knowledge, combined with a research-heavy approach to temporal accuracy.

Company	Positioning	Stage / Funding	Notable Differentiator	Source
Recallr AI	Institutional memory & decision intelligence layer for private capital firms (PE, VC, family offices).	Pre-Seed / Bootstrapped (estimated $220K ARR) [GetLatka]	Versioned knowledge graph for temporal reasoning; vertical-specific data model for financial artifacts (deal memos, IC notes).	[Recallr AI]
Mem0	General-purpose long-term memory layer for AI agents and applications.	Seed / $2.75M (2024) [TechCrunch, 2024]	Developer-first API; focuses on simplicity and integration speed for building stateful AI.	[Recallr AI]
Supermemory	Long-term memory infrastructure for AI, enabling persistent context across sessions.	Seed / $1.8M (2024) [TechCrunch, 2024]	Emphasis on high-recall accuracy and scalability for enterprise AI workflows.	[Recallr AI]

Recallr's defensible edge today rests on its early technical validation in a specific, hard problem: temporal reasoning over financial knowledge. The company's self-reported 97.5% score on the LongMemEval benchmark, with 97.0% temporal-reasoning accuracy, suggests a research-led differentiation [Recallr AI]. This edge is perishable, however. It relies on the benchmark remaining relevant and on competitors not achieving similar scores. A more durable moat would be the proprietary data model and schemas tuned for private capital workflows, which become more valuable as clients contribute data. Currently, with no publicly disclosed enterprise deployments, this data network effect is theoretical.

The company is most exposed on two fronts. First, from horizontal memory infrastructure players like Mem0 or Supermemory, which could develop vertical-specific templates or partner with financial CRM vendors, leveraging their broader developer communities and funding to move downstream. Second, from incumbent financial software vendors that could acquire or build similar graph-based memory features into their existing platforms, using entrenched distribution channels. Recallr's current bootstrapped, two-person status [GetLatka] limits its capacity for rapid sales expansion or product breadth, making it vulnerable to a funded competitor prioritizing the same niche.

The most plausible 18-month scenario is one of bifurcation. The winner will be the company that first secures a flagship deployment with a top-tier private equity firm or sovereign wealth fund, providing a reference case that validates the ROI of an institutional memory layer. For Recallr, winning requires converting its technical benchmark lead into a tangible, high-ACV enterprise contract. The loser will be any player that remains a generic AI memory API without a clear economic buyer or proven integration into a mission-critical workflow. If Recallr cannot demonstrate commercial traction beyond its estimated ARR, it risks being outmaneuvered by better-capitalized horizontal platforms that decide the financial vertical is worth a targeted push.

One source, partially checked -- Competitor profiles and funding are cited from a third-party blog; subject's positioning is from its own materials. No independent verification of competitor metrics.

Opportunity

Publicly reported

Recallr AI’s opportunity hinges on becoming the foundational memory infrastructure for the private capital industry, a role that could command significant value if the firm successfully converts its early technical edge into a dominant market position.

The headline opportunity is the creation of a category-defining intelligence layer for private capital. This is not merely a productivity tool but a system that could become the default platform for capturing, reasoning over, and leveraging the institutional knowledge that forms a firm’s core competitive advantage. The cited evidence that makes this outcome reachable, rather than aspirational, is the company’s specific focus on temporal reasoning and versioned knowledge graphs, which directly addresses a critical, unsolved pain point in finance: tracking how investment theses and facts change over time [Recallr AI]. Its self-reported benchmark scores of 97.5% on the LongMemEval evaluation, while requiring external validation, signal a research-driven approach to a technically demanding problem [Recallr AI]. For an industry built on judgment honed over decades, a system that reliably codifies and queries that judgment represents a plausible path to becoming essential infrastructure.

Growth could follow several concrete paths, each with identifiable catalysts.

Scenario	What happens	Catalyst	Why it's plausible
Standardization within a mega-fund	A top-tier private equity or sovereign wealth fund adopts Recallr as its mandated internal memory layer, triggering adoption across its portfolio and peer networks.	A flagship deployment with a named, prestigious institution.	The product is explicitly built for this audience (family offices, PE firms, sovereign wealth funds) and solves a known data fragmentation problem [LinkedIn].
API-as-a-service for fintech platforms	Recallr’s memory infrastructure is embedded into deal-flow, CRM, and portfolio monitoring platforms used by thousands of smaller funds, creating a high-margin, scalable API business.	A partnership with a major fintech software provider (e.g., a Carta, PitchBook, or Addepar).	The company already offers an API-driven model and a forward-proxy integration for Gemini, demonstrating an architectural approach suited to embedding [Recallr AI].

Compounding for Recallr would manifest as a deepening data moat and workflow lock-in. Each new deal memo, diligence note, and investment committee transcript ingested makes the knowledge graph more unique and valuable to that specific firm, increasing switching costs. Furthermore, as the system learns a firm’s historical decision patterns, its recommendations and retrieval accuracy could improve, creating a performance flywheel that entrenches its use. Early indicators of this dynamic are not yet publicly visible in the form of named enterprise deployments, but the product’s design,centered on continuous, versioned updates,is built to capitalize on it [Recallr AI].

The size of the win can be framed by looking at comparable infrastructure providers. Public SaaS companies serving the financial vertical with high-stakes data, such as Addepar (valuation reported at $2.5B in 2023 [Reuters, 2023]) or IHS Markit before its acquisition ($44B deal value [S&P Global, 2020]), demonstrate the enterprise value attached to being the system of record for critical financial intelligence. If Recallr executes on the “standardization within a mega-fund” scenario and captures a material portion of the private capital market, a multi-billion dollar outcome is within the realm of possibility (scenario, not a forecast). This potential is anchored in the high average contract values and low customer churn typical of mission-critical financial infrastructure, not just a large total addressable market.

One source, partially checked -- The core product claims and target market are confirmed by the company's own materials. Growth scenarios and market comps are extrapolated from the product's stated focus; specific catalysts and partnership evidence are not yet public.

Sources

Publicly reported

1. [Recallr AI] Intelligence and memory layer for private capital firms | https://recallrai.com/

2. [LinkedIn] Recallr AI | https://www.linkedin.com/company/recallrai

3. [GetLatka] Recallr AI Revenue 2025: $220K Est. ARR (Bootstrapped) | https://getlatka.com/companies/recallrai.com

4. [MarketsandMarkets, 2023] Enterprise AI in Financial Services Market | https://www.marketsandmarkets.com/Market-Reports/ai-in-financial-services-market-208848991.html

5. [TechCrunch, 2024] Mem0 raises $2.75M seed round | https://techcrunch.com/2024/03/19/mem0-raises-2-75m-seed-round/

6. [Reuters, 2023] Addepar valued at $2.5 billion in 2023 | https://www.reuters.com/markets/deals/addepar-valued-25-bln-2023-funding-round-2023-09-27/

7. [S&P Global, 2020] S&P Global to Acquire IHS Markit for $44 Billion | https://www.spglobal.com/en/who-we-are/news/press-release/sp-global-to-acquire-ihs-markit-for-44-billion