Problem
Investment teams are buried in filings, transcripts, news, macro releases, price action, and internal notes while decision windows keep getting shorter.
Investor briefing
The governed AI research layer institutional finance is missing.
Going Up is building agentic investment intelligence: AI agents designed to read filings, transcripts, market data, macro events, and portfolio context, and turn them into source-backed briefs your analysts review, challenge, and sign off. The MVP is built. We show it live.
The stated long-term ambition: financial superintelligence for institutional investors — research synthesis designed to exceed what any human team can maintain, every claim sourced, every decision human-reviewed. We are not claiming it exists. We are claiming a build path — and its first layer is demonstrable today.
Stage
Early stage — MVP built, demo on request
Focus
Institutional research workflows
Position
Agentic, in-perimeter, evidence-first
Investor Notice: This website is not an offer to sell or a solicitation of an offer to buy securities. Any financing discussions are private, preliminary, 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.
Short-form pitch deck
The core thesis, built for a first investor conversation.
No serious round closes from a web page, and this one does not try to. The argument is below in eight slides; the proof is the live MVP demonstration that opens every briefing.
Market evidence
The demand is documented. So is the failure of the alternatives.
The case for this category does not rest on a narrative. It rests on published research from regulators, primary research firms, and company disclosures. Each figure below is third-party, cited, and compiled as of July 1, 2026. None of them describe Going Up’s own results.
Wealth and asset managers that have scaled generative AI into multiple use cases; 78% are exploring agentic AI.
EY-Parthenon WAM Survey, 2025 (n=100).Executives in the same survey reporting substantial business impact — the spread that defines the opportunity.
EY-Parthenon WAM Survey, 2025.Enterprise generative AI projects abandoned after proof of concept by end-2025.
Gartner, 2026.Global spend on financial market data and analysis in 2025 — a further record after a decade of records.
Burton-Taylor International Consulting, 2026.A buyer who has already funded internal pilots that stalled is not a skeptic who needs educating. Budgets, executive attention, and board pressure already exist. The open question is which approach converts that spending into governed, auditable production use.
Third-Party Data Notice: These figures are published by the parties cited, are believed reliable, and have not been independently audited by Going Up. They may be revised by their publishers. They describe market conditions generally and are not representations about Going Up’s traction, revenue, customers, pipeline, or future results.
Opportunity
A workflow platform for investors who need traceable decisions.
Move from raw documents and market movement to a sourced research brief ready for expert review.
Surface what changed, why it may matter, which sources support it, and what might weaken the thesis.
Map catalysts, risks, and scenarios against holdings, watchlists, sectors, factors, and liquidity context.
Category validation
The question of whether institutions pay for AI research infrastructure is already answered.
Between mid-2024 and mid-2026, adjacent companies raised at scale on real revenue from this same buyer base — market context, cited below: not a comparison to Going Up and not a claim about its prospects.
AlphaSense
$7.5B valuation · June 2026Raised $350M at a $7.5 billion valuation after reporting more than $600M in ARR in Q1 2026, serving 7,000+ enterprises. Its investor list included strategic capital from the buyer side of the market. It demonstrates that this buyer base pays at scale — while its cloud content-subscription model remains distinct from in-perimeter deployment.
Rogo
~$2B valuation · April 2026Raised $160M led by Kleiner Perkins at approximately $2 billion, roughly five years from founding, serving 35,000+ professionals across 250+ institutions. Notably, it ships forward-deployed bankers embedded inside client firms — independent validation of the services-led delivery model, applied to sell-side workflows.
Hebbia
$700M valuation · July 2024Raised $130M from a16z at a $700M valuation on approximately $13M of ARR, specifically on the thesis that the durable layer is agentic document workflow for financial institutions. Its published pricing of $10,000 per professional seat established a per-seat anchor for the category.
What this means for an early-stage entrant: the category question is closed, and the remaining question is positioning. It also means the window is finite — capital this concentrated tends to decide category winners in quarters rather than years.
Third-Party Reference Notice: Company names, valuations, and financial figures are the property of and were published by their respective owners or by third-party research firms, as cited in the sources section of this website. Inclusion implies no affiliation, endorsement, or partnership. These figures describe other companies and are not indicative of, and imply nothing about, Going Up’s valuation, performance, or prospects.
Built into the MVP
The product discipline: source-backed AI, not magic predictions.
The stack below is what the live demo walks through. What each layer is designed to achieve in a client’s environment remains design intent: judgment stays human, and outcomes are never guaranteed.
Investor console
Illustrative
New filing anomaly
Review
Portfolio exposure drift
Monitor
Macro catalyst cluster
Brief
Illustrative workflow only. No output shown here is investment advice, a securities recommendation, or a promise of product performance.
Structured market context
Filings, news, macro data, transcripts, pricing context, and portfolio inputs organized for retrieval.
Research workflows
AI-assisted steps for monitoring, summarizing, source-checking, scenario building, and memo drafting.
Review and governance
Human review, source trails, audit-friendly outputs, permissioning, and responsible AI controls.
Business model logic
Enterprise software, with a deployment layer priced on top.
The model is land-and-expand, deployment-first: a fixed-scope paid pilot, then seats, then firm-wide modules and private deployment. The price points this model is designed to sit among are already established — and published by others, as market context.
Fixed-scope, forward-deployed engagement delivering source-backed analyst briefs for one focused coverage universe.
Seat-based access across analysts, portfolio managers, and research leads, with usage-gated modules.
Premium data modules, deeper integrations, private deployment, and multi-team workflows.
Intended revenue layers: annual platform subscription, research-team seats, premium data modules offered as pass-through under the client’s own entitlements, private deployment, and forward-deployed engineering. The margin logic depends on productizing deployment — folding connectors, guardrail configurations, evaluation sets, and memo formats back into the platform so that less is bespoke with each installation.
Institutions already fund research tooling at these levels: Bloomberg terminals at approximately $27,660 per seat per year and Hebbia at $10,000 per professional seat, both as published by the companies or the cited research. Market context only — these are not Going Up’s prices, and Going Up has published none.
No Projections Notice: This website states no revenue projections, financial forecasts, target contract values, customer counts, valuation, or expected returns of any kind. Descriptions of the business model are statements of current intent regarding a product under development and are forward-looking. They may change materially, and there is no assurance any element will be implemented or prove commercially viable.
Defensibility
The moat design: assets only deployments can accumulate.
Frontier models are commoditizing, and raw model advantage decays quickly. The thesis is that the workflow, not the model, is the product — so defensibility must be built from assets that compound outside the model. These are the four Going Up is designed around.
The research workflow graph
Analyst review feedback, domain evaluation datasets, source-backed knowledge layers, audit trails, and portfolio context. By design, it is both the quality engine and the switching cost: a client’s own accumulated context lives inside it.
In-perimeter installation
Systems deployed inside a client’s VPC or on-premises environment, integrated with SSO, RBAC, and MNPI-aware guardrails, become load-bearing infrastructure rather than a tool that is easily swapped — by design, the deployment itself is the switching cost.
Governance as a requirement
The SEC opened AI-washing enforcement in March 2024, charging two advisers a combined $400,000 over misleading AI claims. Institutions increasingly cannot deploy research AI whose outputs they cannot evidence, which makes source trails and audit logs procurement gates rather than features.
Model-agnosticism
Best model per task, replaceable as the frontier moves. If models improve, output quality improves without Going Up funding that research. If model pricing falls, input costs fall. The layer that captures frontier progress without paying for it is the layer worth owning.
Forward-Looking Notice: The defensibility described above is design intent for a product under development, not an achieved or verified competitive position. Going Up has no deployments, customers, or accumulated data assets at this time. Whether any of these advantages materialize depends on execution and on competitive conditions that may change.
Risks and execution conditions
What must go right — stated plainly.
This is deliberately the least promotional section on this website, because credibility with institutional counterparties depends on it. The opportunity described elsewhere on this site is conditional. These are the principal risks as management currently sees them, each paired with the design response. This is not an exhaustive list of all risks.
Enterprise trust bar and long sales cycles
Institutional buyers require security reviews, SOC 2 Type II, data-permission audits, and references before production access — a process that can run 6–12 months and has ended many well-funded startups.
Design response: fixed-scope paid design-partner pilots that deliver value during review; compliance certification treated as a funded milestone; forward-deployed engineers who navigate the client’s security process rather than waiting on it.
Incumbent and adjacent-player response
AlphaSense is shipping agents, Rogo’s backers are funding buy-side expansion, and terminals are adding AI surfaces. Well-capitalized companies may enter this space at any time, and the open position described on this site may not remain open.
Design response: focus on in-perimeter deployment, which conflicts with incumbents' cloud and data-licensing economics, and prioritize assets that are slow to copy. Model-agnosticism keeps Going Up complementary to, rather than dependent on, any single ecosystem.
Services drag
Gartner analysts have warned that by 2028 as many as 70% of enterprises could abandon agentic solutions from forward-deployed engagements over vendor cost and skills lock-in. A deployment motion that stays bespoke becomes a consultancy with a software garnish, and carries consultancy margins.
Design response: productize relentlessly — fixed-scope engagements, deployment patterns folded into the platform after each install, declining bespoke hours tracked as an internal metric, and client enablement that transfers skills.
Model commoditization cuts both ways
If frontier models become capable enough, thin workflow layers may be absorbed by the model providers themselves, removing the need for an independent vendor.
Design response: concentrate value in what models cannot ship — the client’s accumulated evidence graph, domain evaluation datasets, in-perimeter trust posture, and audit record — so that better models make the product better rather than redundant.
Data licensing dependencies
Premium content such as transcripts, estimates, and alternative data carries redistribution costs and vendor leverage, which can compress margin or restrict product scope.
Design response: anchor the initial product on public and client-owned data, offer premium content as pass-through modules rather than absorbing licensing risk, and let in-perimeter deployment mean the client’s existing entitlements travel with them.
Stage risk: team, concentration, and capital
Going Up is an early-stage company with no product in production, no customers, and no revenue. Any early revenue would concentrate in a handful of design partners. Key-person risk is real, and the plan depends on hiring scarce technical talent in a competitive market. Early-stage companies frequently fail entirely.
Design response: a milestone set built around checkable operational progress rather than narrative, and an explicit founding-team search treated as part of the plan rather than an afterthought.
Risk Disclosure Notice: The risks described above are those management currently considers principal. They are not exhaustive, and additional risks not presently known or currently deemed immaterial may also impair the business. Nothing in this section should be read as a prediction of outcome or as assurance that any mitigation will succeed. Any person evaluating Going Up must conduct independent diligence and rely on definitive documentation rather than this website.
SEC-aware communication posture
Built to invite diligence, not publish an offering.
No terms, no projections, no traction claims — on purpose. This page communicates the way the product is designed to work: evidenced, governed, reviewable.
No public deal terms
This page does not state a round size, valuation, security type, minimum investment, closing date, or other offering terms.
No return promises
The page avoids guarantees, market-prediction claims, performance projections, and language suggesting risk-free or certain investment outcomes.
Qualification before details
Deeper materials should be shared privately only after appropriate investor qualification, legal review, and confidentiality controls.
Definitive documents control
Any investment decision would need to be based on definitive legal documents and independent diligence, not this marketing page.
Securities Notice: A business may not offer or sell securities unless the offering is registered or qualifies for an exemption from registration. If Going Up conducts any financing, the applicable path, investor qualification process, disclosures, and required filings should be determined with qualified securities counsel before any offer or sale is made.
The next step
Diligence starts with the demo.
Request the private briefing. It opens with the live MVP demonstration, then covers product direction, technical architecture, commercial assumptions, compliance posture, roadmap, and the founder-level plan. The deeper materials are private; the demo is not — ask, and we will show you the system working.
01
Live MVP demonstration
02
Product roadmap and architecture
03
Target customer and go-to-market thesis
04
Security, data, and compliance roadmap
05
Capital plan, milestones, and diligence materials
Live demos and private briefings
See the MVP live. Then let’s talk.
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.
Direct briefing line
+1 (716) 226-3043