Tortoise.
For Jesse, Adam & Nak —
Tortoise provides memory for agents that computes how knowledge changes. Current memory is text: Markdown files or knowledge graphs that store semantics. Facts pile up; every update makes the agent rediscover the logical connections and dependencies; inconsistencies multiply and drift makes hard-earned insights unrecognizable. That's why agents can't yet run without supervision; they keep wasting tokens reinventing the wheel or undoing previous work.
Instead of storing memory as text, Tortoise builds a logical, probabilistic world model:state (what is), context (who and when), and epistemics (the reasons and beliefs behind decisions). New data doesn't trigger rewrites. It connects to the model; confidence is quantified structurally, from sources and the web of arguments that support, negate or mitigate, and a deterministic algorithm calculates how new findings affect every node in the graph. Decisions stay auditable; drift is minimized. Agents reason with hundreds of variables instead of losing the plot past a few. Multiple agents collaborate on a shared world model and build on previous learning.
Both individuals and companies are already trying memory solutions, and they're dissatisfied. My tweet explaining the problem with traditional solutions and how we think about Tortoise reached 1M+ views, 850+ likes, and 17 inbound leads (and counting); 15 of 17 confirmed the dissatisfaction and willingness to try. The leads are builders: devs, PMs, and tech-industry veterans, heavily vibe-coding. 60% want Tortoise for personal knowledge management, 40% to power an agentic application, and one also wants to introduce it to the rest of his company.
The economics: at measured per-session costs and typical usage, a regular user is ~$2/mo all-in (storage ~$0.1, LLM ~$1.8) against a $25 Pro / $149 Team price, deeply margin-positive. Heavy usage runs $12–37/mo and is usage-metered; extraction (internal to hosted, being optimized) is the calibration lever, and the business model exists for exactly this (M3 ≈ $3K MRR, M12 ≈ $99K MRR, Y5 ≈ $101M ARR). Default alive by month ten: gross burn to default alive ≈ $640K, with a 50% buffer, a total ask of ≈ $960K.
We're entering this market bottom-up with the segment we already have demand in: vibecoders and indie hackers. They onboard in five minutes and get value from the first session by making a decision on the graph (which vitamins to buy, which screen). As AI's early adopters, they happily wire their own workflows and integrations, and many contribute in open source. They help us progress up the food chain by acting as recommenders, and help us scale horizontally by building vertical apps.
I started my career as a chef in the best restaurants in the world, and found they struggled to remain innovative, lacking systems to collaborate on and manage their knowledge. That sent me on a decade-long quest to understand why smart groups fail to think together: experimental psychology at Oxford, organization design consulting (BCG, Oxford visiting lecturer, etc.), collective-intelligence tools for DAOs, and a previous failed startup that gathered community data into an AI hive mind. Then I started using agents and stumbled upon the same problem, now more pressing than ever. I paused everything else and jumped head-on.
M12, that's a ~$1.2M ARR business; in five years, ~$101M ARR at ~97k customers, profitable along the way, on Gateway X's own thesis: build on your own terms, don't pass the valuation on.
Contents
What is it?
The bottleneck for intelligence isn't compute; it's learning. Agents start forgetting after a few thousand tokens, and today's memory systems are text: Markdown files or knowledge graphs that store semantics. Facts pile up; each edit makes the agent rediscover the logical connections; drift soon makes your knowledge unrecognizable, and hard-earned insights are lost.
Tortoise is a memory system for agents that enables them not just to store and recall facts, but to understand the logic behind decisions, comprehend how each component fits into a larger whole, and learn over cycles without drift.
Category.
Memory infrastructure for agents. Every relationship expresses how one belief supports, contradicts or mitigates another; when new evidence arrives, an algorithm propagates confidence through the graph, updating the world model without rewriting files or recomputing from scratch. Memory becomes an active reasoning system, not passive storage.
Form factor.
A foundational service: use it solo with your agents, as infrastructure to power applications, or together with your team. Hosted or self-hosted.
Customer + edges
Who.
- AI builders: devs, product managers and tech veterans who are building with agents ("vibe coding"), in entrepreneurial communities. The 17 inbound leads fit this profile exactly: 60% want Tortoise for personal knowledge management, 40% to power an agentic application (two of them building investment applications), and one wants to use it personally and bring it to his company.
- Later: teams (pending permission management and automated curation) and enterprises deploying autonomous workflows (pending compliance, audit).
Why this segment.
Indie hackers and vibe coders are AI's neophiles and early adopters, and they're capable of building workflows and integrations themselves; many are open-source enthusiasts. Set up right, the platform gives them a contribution loop (skills and integrations marketplace). From there, the roles compound: self-users recommend to companies and build applications on Tortoise.
Knowledge edge.
Distribution edge.
White space
Two reasons this doesn't already exist:
Alternatives and why they fail this customer
| Alternative | Why it fails |
|---|---|
| Markdown files + retrieval (the default) | Summaries edit the source data: compression, drift. Knowledge gets quickly outdated because agents are lazy and miss some of the logical connections and dependencies between docs. |
| Mem0 / Zep / Honcho / etc | Retrieval systems with a graph. No epistemic layer: unreliable when answering "why do we believe this?" and can’t propagate how new evidence changes beliefs without drift or significant token costs. |
| Flagship AI Labs | Companies don’t trust them with their data. The Labs’ incentives are for increasing usage or tokens (e.g., editing markdown files), not for providing higher intelligence with fewer tokens. |
| Company Brains | They’re spread thin trying to do everything (harnesses + integrations + memory, etc.) in an era when customers are becoming more accustomed to customization, and agents can complete an integration for virtually no cost. We can integrate with them later on. |
The gap over time
Models keep improving and retraining will become more common, but many kinds of knowledge (business strategy, R&D, investment decisions) need to evolve continuously while staying transparent and deterministic. That belongs in memory, not inside model weights.
Why now, how big
Why now
- Cost crossed the line. Running agents reliably (extraction, embedding, reasoning loops) is now cheap enough that the economics work: measured per-session extraction costs ~$0.03 at the median. 6 months ago this was 20x more expensive.
- Capability crossed the line. Models are now intelligence-wise, capable of doing far more. That's exactly why memory became the bottleneck: the agents that forget the trade-offs, concerns, and arguments behind decisions burn tokens reinventing the wheel or advancing incoherent initiatives. Compute is no longer the constraint; learning is.
- The category just got named. Customers are actively searching.
How big: the whole model
~97,000 customers at ~$100/mo (blended) can make us $100M ARR, while there are 250,000+ agent-building developers and teams today, and the agent economy is compounding.
| Horizon | Customers | MRR |
|---|---|---|
| M3 (demo day) | 300 Solo + 1 Team | ~$3K |
| M12 | 1.4K Solo + 1.1K Pro + 384 Team | ~$99K (~$115K w/ 2 enterprise) |
| Y5 | ~97K customers | ~$101M ARR (agent-count tailwind could 10×) |
Assumptions: 10% of customers buy overage; 8% Team with 30-day cycle; CAC $75 Solo / $400 Team; 2 enterprise deals Y1, 8+ Y2 (9–12mo cycle). Top-down sanity: TAM $28B memory & orchestration (Mordor 2030), SAM $5.7B, SOM $114–285M.
Show, don't tell
Demo video
What the machine does
- Connects your data (feeds, docs, conversations) into a world model: state, context, epistemics.
- Propagates confidence deterministically when new evidence arrives; no rewrites, no drift.
- Ranks options in a decision (Tortoise Decide): agents usually get lost past a few variables; here they can reason with hundreds or even a million.
- Leaves an audit trail: every decision is provenance-bound, showing which source, which reasons, which confidence.
Planned fast path (design, not shipped)
Ship a prompt with ~10 example decisions a user can test in the first session: which vitamins to buy while scrolling Amazon, which monitor, or a real startup decision if they have one. The decision → analysis output is the aha moment.
Demand
B2C → B2B: the builders who use it for themselves and to power their applications.
6a · Users: builders, on record
Memory is a real challenge. Played around with OKF and Graphs so far. Currently designing a .md based graph… I'm wondering if Tortoise could be a good fit.
Graph surfaced a note vector missed in 72% of them, +2 notes on average. Your drift point still beats us. How does tortoise keep the "why" when the same fact comes back with a different reason?
Most agent memory systems are still pretty basic. Markdown files with retrieval layered on top… Knowledge graphs are designed to represent what is known, not to continuously learn and update beliefs.
6b · End customers: through builder apps
The end-customer wedge is the 40% building agentic applications: when those apps commercialize, Tortoise is the infrastructure underneath; the non-technical segments they serve become reachable without us building vertical apps. Two of the 17 leads are building investment applications; one lead also wants to bring Tortoise to the rest of his company (a Team-tier signal).
Channels
Founder-led content on Twitter and LinkedIn: the thesis tweet is the proof (1M+ views, 17 leads); strategy is content-first amplified with ads and later on referrals. Scaling via paid UGC (influencers).
Economics
Pricing: feature tiers by use case, usage on top
| Tier | Price/mo | Write ops incl. | Nodes | Overage |
|---|---|---|---|---|
| free | $0 | 10K | 10K | — |
| solo: use it yourself | $9 | 10K | 25K | $5 / 10K write ops |
| pro: build with it | $25 | 50K | 100K | $5 / 10K write ops |
| team: use it together | $149 | 200K | 600K | $5 / 10K write ops |
Tiers are feature tiers by use case: solo (personal knowledge management, the 60% of our leads), pro (power an agentic application, the 40%), team (company-wide); each with included usage to get going, usage metered on top.
The margin story
A typical user costs ~$2/mo to serve (storage ~$0.1, LLM ~$1.8 at measured per-session costs), against $9, $25 and $149 tiers: ~80% gross margin before usage. Heavy usage is metered on top (usage tokens, extraction weighted), so power users pay their way as the product does more of the work.
Break-even
Operating break-even comes at roughly 1,600 customers, where gross margin covers the future team. Projected default alive by month ten, on a gross burn of ≈ $640K, with a 50% buffer: a total ask of ≈ $960K, comfortably within Gateway X's check.
Compounding
Wedge + resolution
The entry point is the builder segment we already have demand in (17 inbound leads, two building investment applications), entered through founder-led content and MCP/library distribution, at the Solo/Pro price, cheap enough to explore, which is the point.
How the model resolves
Horizontal infrastructure so we can focus and double down on our USP of a technical edge. Builders customize or ship the vertical apps.
Meant to build
I started my career as a chef, working in some of the best restaurants in the world, until I realized my culinary heroes were struggling to remain innovative because they had no good way to manage tacit knowledge. I then spent a decade on the question: why do intelligent people, together, get dumber? From a residency in experimental psychology at Oxford, to considering an MBA but ending up instead collaborating with one of the professors and facilitating workshops for their executive education programs, training BCG consultants, and then moving to work on DAO governance and launching a Web3 venture studio focused on collaboration tools. Seeing the dysfunctions of aggregating knowledge in forums and chat groups, we launched a startup to organize that data into a Hivemind to power a chatbot. Generated $100k+ of revenue, but the Web3 market didn't grow as we hoped.
A year later, building agents, the same problem returned: LLMs forget halfway through a session, and every memory system is text with retrieval layered on top. I suddenly realized I had all the pieces to solve this problem, and paused everything else to focus on it.
Team by archetype
| Archetype | Who | Status |
|---|---|---|
| Founder / product / sales | Daniel Ospina | Solo founder; built MVP + validated algorithm; owns the funnel |
| Research | Connor | Advisory stake (2%); co-developed the logical-chains ontology |
| CTO & CMO | Current gaps | Key hires post-raise |
Iteration receipts
My original go-to-market was going to be a couple of example applications that memory powers better, including a bot for founder-led content that uses the logic of the memory. Then the thesis tweet went insanely viral and generated inbound leads. The demand was for the memory system itself, so I pivoted the go-to-market away from the example applications.
In a conversation with a potential customer, they showed me the detailed ontology they had developed within their enterprise. And another solo dev also mentioned how controlling the ontology is important for them. I quickly moved ontology expansion packs as a roadmap priority.
Self-reflection
| Trait | Receipt |
|---|---|
| Compass points north | I won’t sell memory I wouldn’t trust in my own stack; Tortoise powers every decision I make, including this memo. |
| Action-oriented | Working alpha in 3 weeks; provisional patent filed; thesis tweet 1M+ views; daily calls with customers. |
| Admits unknowns | Extraction cost poorly measured (improving now); wedge is decided but unproven. |
| Stake-honest | Team gaps named on the team table. |
Why Gateway X
I went through Techstars with a previous startup and learned the hard way how valuable mentors are when they have skin in the game. I want partners who prioritize telling me the truth over being nice and maintaining the relationship. The investment size here is attractive because it eliminates distractions: enough to reach default alive and never raise again.
And I strongly resonate with the thesis of building profitable businesses:maximizing market share is not necessarily the best path to profitability.