Agentic Investment Intelligence
Agentic investment intelligence for institutional investors
AI agents built to read filings, transcripts, macro releases, market data and your portfolio context — and turn them into source-backed briefs your analysts review, challenge, and sign off.
The MVP is built — watch it run live in a demo. Designed to raise research throughput, cut workflow friction, and leave a documented trail behind every investment decision.
Investor Notice: This website is not an offering document and does not constitute an offer to sell or a solicitation of an offer to buy securities. Any financing discussions are private, preliminary, and subject to applicable securities laws, investor qualification, definitive legal documents, and independent diligence. Do not rely on this website as the basis for any investment decision.
Third-party market context, not Going Up results. Going Up is pre-launch and pre-revenue; these figures describe the market it is building for. See sources.
The problem
Filings, transcripts, news, macro releases, pricing, portfolio exposure, and internal thesis history live in different systems. Data access is largely a solved and commoditized problem — institutions buy it at record levels every year. What remains unsolved is turning that raw signal into defensible, source-backed context fast enough to matter.
Analysts spend hours filtering noise before they can form judgment. The reportable US private-fund universe alone grew 7.1% in a single year, and gross assets grew 10.5% — more names, more filings, more calls, and more catalysts per analyst than before.
Source: SEC Private Fund Statistics, Q3 2025.Source trails, portfolio relevance, and prior theses sit in separate silos, so the same context is reconstructed again and again. Institutions paid a record $49.2 billion for market data and analysis in 2025 — and then paid their own analysts again, in time, to convert it into decisions.
Source: Burton-Taylor International Consulting, 2026.Manual synthesis can push committee-ready output past the moment a decision needed it. Evidence discipline — who claimed what, based on which source, reviewed by whom — is increasingly an examination topic, and manual workflows generate weak audit trails by construction.
Context: SEC adviser examination priorities and AI-related enforcement, 2024–2025.The reframe that matters: this is not a chatbot problem. It is a workflow, evidence, and governance problem.
Our thesis
So that is the layer we are building: public sources in, human-reviewed decision support out, designed to run end to end inside your own perimeter. The diagram below shows that path in motion.
Filings, transcripts, news, macro releases, market data, and your own portfolio context.
Each item is parsed, related to what you already hold, and kept attached to its source.
Nothing leaves the system unread. The analyst confirms, challenges, or discards it.
A daily brief, an alert, a thesis update, a risk flag, or a draft IC memo — with its trail intact.
The product
One decision-support surface: AI-assisted research workflows, structured signal discovery, and portfolio-aware context for institutional investment teams.
Illustrative sample scores only. Not investment ratings or recommendations.
Filings, transcripts, news, pricing, and macro context organized into source-backed briefs for human review.
Drivers, counterpoints, source trail, and portfolio context attached to every brief.
Thesis updates, risk scenarios, and meeting-ready narratives, prepared for analyst sign-off.
AI Technology Notice: References to AI, agents, automation, signal analysis, portfolio context, and investment-intelligence workflows describe software capabilities, prototypes, product direction, or intended functionality. Going Up does not claim that its AI systems can predict markets, guarantee returns, eliminate risk, or replace professional judgment. All AI-assisted outputs require human review and independent verification.
Why now
The spread between how much institutions have already invested in AI and how little measurable impact they report is the clearest evidence that capability is not the constraint. Published research from 2024–2026 quantifies both sides of that gap. Every figure below is third-party and cited.
Wealth and asset managers that have scaled generative AI into multiple use cases — and 78% are exploring agentic AI.
EY-Parthenon, GenAI in Wealth & Asset Management Survey, 2025 (n=100).Executives in the same survey reporting substantial business impact from their generative AI investments.
EY-Parthenon, GenAI in Wealth & Asset Management Survey, 2025.Enterprise generative AI projects abandoned after proof of concept by the end of 2025.
Gartner, Why Half of GenAI Projects Fail, 2026.MIT studied ~300 enterprise AI initiatives: 95% of generative AI pilots produced no measurable P&L impact, and the failures trace to deployment and integration — not model capability.
MIT NANDA, State of AI in Business, 2025 (~300 initiatives studied).Gartner attributes the failures to poor data quality, insufficient risk controls, rising costs, and unclear business value. None of those are model problems. All four are workflow, governance, and integration problems — which is the layer Going Up is being built to address.
Source: Gartner, Why Half of GenAI Projects Fail, 2026.Forward-Looking and Third-Party Information Notice: Figures above are published by the third parties cited and were compiled as of July 1, 2026. They are believed reliable but have not been independently audited by Going Up, and may be revised by their publishers. They describe market conditions generally and are not statements about Going Up’s own performance, traction, or results. Statements about what Going Up intends or is designed to build are forward-looking and subject to change.
Our response
Four design decisions follow directly from that gap. Each one targets deployment, context, or accountability — not model capability.
One structured context layer for public data, filings, earnings calls, news, macro releases, and internal notes.
Agent workflows built to show their work: sources, uncertainty, catalysts, competing narratives, follow-up questions.
Signals mapped against exposures, concentration, liquidity, factor sensitivity, and event risk.
Permissioning, auditability, data controls, and the workflows serious financial institutions expect — treated as core architecture, not add-ons.
Product
The eight modules below are built into the MVP and shown live in the demo. Each targets a repeatable workflow that analysts, portfolio managers, and research leaders already run manually today. What each module is designed to achieve in a client’s production environment remains design intent, not an independently verified capability.
Prioritized market events, watchlists, and coverage changes in one reviewable surface.
Source-backed extraction from regulatory documents and earnings calls, with citations preserved.
Policy releases and macro prints mapped to the portfolio context a client provides.
Exposure-aware risk framing and scenario surfaces built from client-supplied holdings.
New evidence compared against the assumptions a team previously recorded.
Source-backed drafts prepared for investment-committee review by a human analyst.
An evidence graph tying every claim to its document, page, and stated uncertainty.
Monitoring across names, themes, sectors, and catalysts a client chooses to track.
Going Up is designed to compress the path from signal to a documented, human-reviewed decision — augmenting investment judgment, not replacing it.
AI Technology Notice: References to AI, agents, automation, signal analysis, portfolio context, and investment-intelligence workflows describe software capabilities, prototypes, product direction, or intended functionality. Going Up does not claim that its AI systems can predict markets, guarantee returns, eliminate risk, or replace professional judgment. All AI-assisted outputs require human review and independent verification.
Deployment model
Published research attributes most enterprise AI failure to deployment rather than capability. Going Up’s intended delivery model responds directly to that: forward-deployed engineers who install the platform in the client’s own environment, on the client’s data, under the client’s controls.
An engineer works alongside the research team on workflows, data permissions, memo formats, and security review — an embed scoped to weeks, not quarters.
Deployment targets the client’s VPC or on-premises environment, so client data never leaves it. SSO, role-based access control, and MNPI-aware guardrails are day-one architecture.
Evaluations, monitoring, and additional workflows over time, with deployment patterns folded back into the core product rather than left as bespoke client work.
This delivery model is not a contrarian bet. Andreessen Horowitz described the forward-deployed engineer as the defining enterprise AI role in 2025; in May 2026 OpenAI established a dedicated deployment company, and Anthropic announced an enterprise-services venture with Blackstone, Hellman & Friedman, and Goldman Sachs as founding partners. Palantir established the precedent.
Sources: Andreessen Horowitz, June 2025; The Pragmatic Engineer, May 2026; industry reporting, May 2026.Gartner analysts have warned that by 2028 as many as 70% of enterprises could abandon agentic solutions delivered through forward-deployed engagements, citing vendor cost and inadequate skills transfer. A deployment motion that stays bespoke becomes a consultancy. Going Up’s stated design response is fixed-scope engagements, deployment patterns productized into the platform, and client enablement that transfers skills. That discipline is the execution test — hold us to it.
Source: Gartner analyst commentary via CIO.com, May 2026.Market context
The institutions below already run the research workflow the platform is designed to support, and already fund tooling for it. Going Up does not need to create a budget category. All figures are published by the regulators and research firms cited.
| Buyer segment | Scale | Assets | Source |
|---|---|---|---|
| SEC-registered investment advisers | Entire SEC registry | $146T regulatory AUM (2024) | SEC Investment Adviser Statistics |
| US private funds (Form PF filers) | 54,392 | $26.9T gross / $16.9T net (Q3 2025) | SEC Private Fund Statistics |
| Cayman-regulated funds | ~30,000 vehicles | 17,722 private funds at end-2025 | CIMA / Cayman Finance |
| Global hedge fund industry | — | Exceeded $5T in 2025 | HFR |
| Single family offices (global) | 8,030 → 10,720 by 2030 | AUM $3.1T → $5.4T | Deloitte Private |
Global spend on financial market data and analysis in 2025, up 6.5% — a further record after a decade of consecutive record years.
Burton-Taylor International Consulting, 2026.Projected CAGR for generative AI in financial services, from $1.67B in 2023 to a projected $16.0B by 2030.
Grand View Research. Projection by that publisher, not by Going Up.The pattern: the data layer is a mature market growing at single digits, while the workflow layer above it is smaller and compounding far faster. Going Up is being built for that upper layer.
Market Data Notice: These figures describe the addressable market generally. They are not projections of Going Up’s revenue, market share, customer count, or results, and no such projections are made anywhere on this website. Going Up is pre-revenue and does not represent that it will capture any portion of these markets.
Landscape
The categories adjacent to Going Up are strong — and each is structurally committed to a different position. The description below is drawn from those companies' own public positioning and is offered as honest orientation, not as a competitive claim.
What they own: trusted data, distribution, and the analyst desktop.
Their economics are built on monetizing data access, so workflows have tended to remain static. Bloomberg’s published terminal cost is approximately $27,660 per seat per year.
What they own: a large content corpus, search, and emerging agents.
A cloud content-subscription model where the core asset is the document library rather than governed workflow running inside a client’s own perimeter on the client’s own data.
What they own: banker workflows — deal screening, CIMs, pitchbooks, models.
Optimized for investment-banking tasks. Buy-side research, thesis maintenance, and IC memo workflows carry different governance requirements.
What they own: full control over the stack.
Typically 12–24 months to production against a documented abandonment rate above 50%, with permanent evaluation and guardrail overhead. Rational for the largest firms.
Going Up’s position: finance-specific, agentic, and in-perimeter, built for buy-side research teams. That position is open. Going Up is taking it — and the MVP that stakes it is demonstrable today.
Third-Party Reference Notice: All company names are the property of their respective owners and are referenced for factual market orientation only. Inclusion implies no affiliation, endorsement, partnership, or comparison of product quality. Descriptions are based on those companies' public statements and third-party reporting as of July 1, 2026. Competitive conditions change, and well-capitalized companies may enter this area at any time.
Investor narrative
Going Up is an early-stage AI infrastructure company building agentic investment intelligence. The eight modules exist in a demonstrable MVP today, and each is a layer the long-term system requires. By financial superintelligence we mean research synthesis designed to exceed what any single team can maintain — every claim sourced, every decision human-reviewed. It is a stated ambition, not a present capability, and the demo is where the build path starts.
AI-native investment intelligence for public markets, private markets, and strategic finance teams — positioned as governed research workflow rather than another data feed or general-purpose chat interface.
The moat under construction: a research workflow graph — analyst review feedback, domain evaluation datasets, source-backed knowledge, audit trails, and in-perimeter deployment patterns — designed to compound with each installation.
Planned enterprise subscriptions, research-team seats, premium data modules offered as pass-through, private deployment, and forward-deployed engineering. Pricing and commercial detail are walked through in the private briefing.
Applied research on workflow and evaluation, in-perimeter deployment infrastructure and certifications, proprietary domain evaluation datasets, and design-partner programs.
Frontier models are commoditizing, and raw model advantage decays quickly. Going Up is model-agnostic by design — the best model for each task, swapped as the frontier moves — so frontier improvements raise output quality without a bet on any single provider.
Investor Notice: This website is not an offering document and does not constitute an offer to sell or a solicitation of an offer to buy securities. Any financing discussions are private, preliminary, and subject to applicable securities laws, investor qualification, definitive legal documents, and independent diligence. Do not rely on this website as the basis for any investment decision.
Roadmap
The eight modules are the wedge; the company is the arc. The plan is to productize one painful recurring workflow first, validate it against real analyst cycles with design partners, and only then widen the surface area. The phases below describe planned product direction and sequencing; they are forward-looking, not commitments, and may change.
Filings, transcripts, and news into source-backed briefs prepared for human review.
Catalyst detection across the names and themes a team chooses to track.
Exposure-aware context built from holdings a client provides.
Decision workflows, prior-assumption comparison, and committee-ready drafts.
Private deployment, deeper integrations, and multi-team workflows.
These are internal objectives, not commitments, guarantees, or predictions of outcome. Timing and scope may change materially. No financial targets, contract values, or revenue milestones are stated on this website.
Product experience
The interface is built to help teams spot a market change, understand why it matters, and prepare a credible investment discussion before the context goes stale. The MVP is built around this arc — and you can watch it live in a demo.
Illustrative walkthrough — sample scenario, not live data.It prioritizes the signal, shows holdings and watchlist context where provided, and opens a source-backed briefing for the team.
AI Technology Notice: References to AI, agents, automation, signal analysis, portfolio context, and investment-intelligence workflows describe software capabilities, prototypes, product direction, or intended functionality. Going Up does not claim that its AI systems can predict markets, guarantee returns, eliminate risk, or replace professional judgment. All AI-assisted outputs require human review and independent verification.
Sources
All market data cited on this website is public and third-party. It was compiled as of July 1, 2026, is believed reliable but has not been independently audited by Going Up, and may be revised by its publishers.
Live demos and private briefings
The MVP is built and demonstrable — request a live walkthrough, an investor briefing, or both. Tell us what you would like to see and where to reach you; the walkthrough runs live, on the real system. Going Up is based in New York City — briefings run over video, or in person in the city. Please do not submit confidential, sensitive, regulated, or material nonpublic information through this form.
