A non-engineer founder in 2026 can ship a defensible AI product to paying users in 6 to 12 weeks — for roughly the cost of a small used car — provided they treat idea-to-product as a disciplined service category and not as a weekend hackathon. That sentence would have read as a fantasy in 2022 and as a sales pitch in 2024. By mid-2026 it is the operating reality, and the founders who refuse to act on it are watching peers with the same idea, less domain depth, and more discipline ship past them.
This is the manifesto for that reality. The thesis is simple: the gap between “I have an AI idea” and “I have a paying AI product” used to be a tech cofounder and a year. It is now a partner, a plan, and a quarter. The work in the middle has a shape, and the shape has commitments. Below is what those commitments are, written down, numbered, and signed.
Table of Contents
- Why a Manifesto, and Why Now
- The Nine Commitments
- What Founders Should Do With This
- Frequently Asked Questions
- Closing
Why a Manifesto, and Why Now
The arithmetic shifted under everyone’s feet, and most of the writing about AI MVPs has not caught up.
In 2023, the question “can a non-engineer founder ship a real AI product without a tech cofounder?” had one honest answer: not really. The agentic-coding tools were demos. The model APIs required engineering judgment most founders did not have. The SERP for “AI MVP” was a sea of agency landing pages quoting six figures for six months of work that often ended in a Streamlit demo and a Notion doc.
In 2026, the same question has a different honest answer: yes, with discipline. The discipline matters. Without it, the founder ships a toy, runs out of runway, and concludes the technology was the problem. With it, the founder ships a product that has paying users, an eval suite that catches regressions, an inference bill they understand, and a code repository they can hand to a real engineer if and when they raise.
Three structural shifts created this opening.
First, agentic coding crossed a usability threshold. The SWE-Bench Verified leaderboard — the most-watched benchmark for autonomous coding — moved from roughly 12% in early 2024 to consistently above 65% in late 2025, with frontier systems now exceeding 80% on selected slices (Anthropic SWE-Bench results, arXiv 2310.06770). That benchmark is not a synthetic curiosity. It tracks the ability of an agentic system to read a real GitHub issue, navigate a real repository, and ship a passing patch. When that number was 12%, founders needed engineers. When that number is 80%, founders need judgment about engineering, which is a different and far more learnable skill.
Second, the developer-tool stack collapsed in price and expanded in capability. Cursor Pro at $20 per month, Cursor Business at $40, Anthropic’s Claude Code Max at $100 to $200, OpenAI’s Codex CLI, and the deploy-from-prompt platforms — v0, Bolt, Lovable, Replit Agent — together let a single non-engineer produce, deploy, and iterate on full-stack software. Stack Overflow’s 2025 Developer Survey reports 84% of professional developers now use AI tools daily; the majority pay for them. The same tools work for non-developers — slower, but they work.
Third, the buy-side procurement category fell apart. The 2023 archetype — a six-month “AI strategy” engagement ending in a slide deck — has been pricing itself out of relevance as the underlying tools collapsed in cost. McKinsey’s State of AI in early 2025 found that 78% of organizations now use AI in at least one function, meaning translation work is no longer scarce. BCG’s 2024 study Where’s the Value in AI? found roughly 10% of enterprise AI value comes from algorithms and roughly 70% from people and process change — the work that can only be done inside the founder’s product, with the founder.
The category that filled the gap is idea-to-product as a service: a milestone-priced engagement — roughly $30K of planning, $80K of build, $40K of hardening — that takes a non-engineer founder from “I have an AI idea” to “I have a shipped product with paying users” in 6 to 12 weeks. It is not consulting. It is not staff augmentation. It is not a no-code platform. It is a specific operating discipline with specific commitments.
The nine that follow are the commitments. Each is written so a founder can quote it back to a vendor and so a vendor can sign it line by line into a statement of work. Drift from these commitments correlates strongly, in our experience, with drift from a shipped product.
The Nine Commitments
1. The Founder Is The Protagonist
The non-engineer founder is the operator of the product, the owner of the customer, the keeper of the domain knowledge, and the only person in the room whose name is on the cap table. Every commitment that follows is in service of keeping that founder in the protagonist’s chair, not relegated to a “stakeholder” passing requirements to engineers behind glass.
The convention this overturns is the 2010s agency model in which the founder briefs the agency, the agency disappears for six weeks, and the founder receives a build that is technically what they asked for and operationally wrong. That model survives in 2026 because it is comfortable for both sides — the founder gets to outsource a hard thing, the agency gets to bill on schedule — and it produces the highest rate of post-launch divorces in the services industry.
It is right now because the same agentic-coding tools that let a solo engineer ship 5× more work also let a founder participate in the build at a fidelity that was impossible in 2023. A founder who can read a pull request title, click into a Cursor session, and say “this is the wrong call for our user” is a force multiplier. A founder who has been excluded from the work cannot make that call until launch day, when the call is much more expensive.
What an idea-to-product partner needs to do to encode this:
- Onboard the founder into the same GitHub repo as the engineers — read access at minimum.
- Run a weekly working session where the founder watches a Claude Code or Cursor session live, asks questions, and votes on tradeoffs in real time.
- Write the PRD with the founder, not for the founder, and version it like code.
- Keep the founder one click away from inference costs, eval pass rates, and deployed-feature usage.
The quote, for a founder to read back at a vendor proposal review: “I am the protagonist. Build with me in the room, not for me from the next room.”
2. Evals Before Code
The most expensive failure mode of a non-engineer-built AI product is shipping something that seems to work in a demo and silently fails in production. The mechanism that prevents that failure is the eval suite — a fixed set of inputs, expected behaviors, and quality thresholds, written down before any code is shipped.
The convention this overturns is the SaaS-era feature-list PRD. A SaaS PRD says “the user can filter by date.” A 2026 AI PRD says “given these 80 representative customer queries, the assistant must produce a useful answer on 75 of them, must refuse on 4, and must escalate to a human on 1 — and we will run that check on every commit.” Without the second discipline, the founder cannot tell a regression from a Tuesday.
It is right now because frontier models update on cycles of weeks, not years. In the eighteen months ending Q2 2026, OpenAI shipped GPT-5 then GPT-5.4, Anthropic shipped Claude Opus 4 through Opus 4.6, Google shipped Gemini 3.1 Pro, and the open-weights frontier moved from Llama 3 to Llama 4 Scout. Each transition silently changes the behavior of any system pinned to a “stable” model alias. The only mechanism that lets a non-engineer founder detect a regression caused by an upstream model update is an eval suite they can re-run on demand.
What an idea-to-product partner needs to do to encode this:
- Draft the eval suite — typically 50 to 150 cases — during the PRD phase, not after the first sprint.
- Use a real harness (Promptfoo, LangSmith, Ragas, Anthropic’s eval tooling, or a custom one). Tool choice is negotiable; the presence of one is not.
- Wire the eval run into CI so the build fails when a threshold is missed.
- Teach the founder to read the eval report — green vs. red, regression vs. flake — as part of weekly review.
The quote: “Write the test for ‘is this output good?’ before you write the code that produces it.”
This commitment is the operating core of the eval-first build playbook, which expands the practice for the scoping stage of any AI feature.
3. Six-To-Twelve Weeks, Milestone-Priced
A 2026 idea-to-product engagement that takes a non-engineer founder from idea to shipped product should clock in at 6 to 12 weeks and should be priced in milestones, not in time-and-materials hours. The shape we see working is roughly: ~$30K for a 2–3 week planning and PRD phase, ~$80K for a 6–8 week MVP build phase, and ~$40K for a 2–3 week hardening and handoff phase — about $150K all-in for a serious shipped product. Numbers shift with scope; the structure does not.
The convention this overturns is the 2023 six-month, six-figure-per-month “AI strategy” engagement that ended in a deck. McKinsey, BCG, the Big Four — all built their AI service lines on that shape. It survives in 2026 mostly inside large enterprises where procurement habits are sticky and the founder is not at the table. A founder who has personally written the check pushes back on it within thirty seconds.
It is right now because the agentic-coding tools have compressed the build phase by a factor of three to five. Work that took an agency of four senior engineers six months in 2023 takes a forward-deployed engineer with Claude Code and Cursor 6 to 8 weeks in 2026 — and produces better-quality output because the engineer can iterate faster against the eval suite. GitHub’s 2024 Octoverse showed AI-touched repositories growing roughly 2× year-over-year, and the underlying productivity story is consistent across primary developer surveys.
What an idea-to-product partner needs to do to encode this:
- Quote in three milestones, not in hours. Each milestone has a fixed price and a single named deliverable.
- Cap the planning milestone at three weeks. Past that, the team is dithering.
- Cap the build milestone at eight weeks. Past that, the scope was wrong — call it.
- Cap the hardening milestone at three weeks. Past that, the product was not actually ready to launch.
- Include a stop-loss in each milestone: if the team and the founder agree the scope is wrong, both sides exit without fight.
The quote: “Three milestones. One quarter. Two used cars. Sign or don’t.”
4. Working Software Over Slideware
At the end of the planning milestone, the founder receives running code, not a deck. At the end of the build milestone, the founder receives a product deployed behind their own domain on their own infrastructure, with users — even if those users are five friends — not a demo URL. At the end of the hardening milestone, the founder receives operational documentation, a runbook, and a green CI pipeline, not a “next steps” slide.
The convention this overturns is the consulting-industry artifact of “phases” that produce documentation rather than software. A 2023 idea-validation engagement could produce three rounds of decks before a single line of code was shipped. The decks were billable because the underlying technology was unfamiliar enough that translation work was the value. The technology is no longer unfamiliar. Translation is no longer scarce. Shipped software is.
It is right now because the agentic-coding tools made the cost of producing working software in week one of an engagement collapse. There is no defensible reason to spend six weeks on diagrams when a 90-minute Claude Code session can produce a working scaffold the founder can click through. The diagrams are still useful — for thinking, for alignment, for handoff documentation — but they are not the deliverable.
What an idea-to-product partner needs to do to encode this:
- Ship a deployed prototype by the end of week 2, no exceptions. It does not need to be the right prototype. It needs to be a real one.
- Replace every “slide-only” deliverable with a software equivalent — a Notion page with live data, a deployed demo, a working eval report.
- Treat the PRD as a living document that is updated alongside the code, in the same repo.
- Refuse to bill a milestone whose only artifact is a slide deck.
The quote: “If the only artifact I can show my next investor is a deck, you have shipped nothing.”
5. The Founder Owns The Stack On Day One
Every piece of work product — the repository, the deployment, the API keys, the model accounts, the eval suite, the prompt registry, the agent definitions, the database, the domain — belongs to the founder from day one. The partner has access; the founder has ownership. At any point in the engagement, the founder can fire the partner and continue with another team without forklift work.
The convention this overturns is the legacy agency pattern of building inside a “vendor sandbox” — a separate GitHub org, a separate AWS account, a separate “vendor handoff document” that magically appears at the end of the engagement. That pattern made sense in 2014 when agencies needed to protect their own tooling. It makes no sense in 2026, when the tooling is commodity and the agency’s edge is in the engineering judgment, not in the sandbox.
It is right now because lock-in is operationally expensive for the founder and operationally cheap for the partner to avoid. Setting up infrastructure inside the founder’s GitHub org, the founder’s Vercel account, the founder’s Anthropic account, and the founder’s domain takes about a day. Not doing it costs the founder weeks of forklift work in any future transition. The only reason not to do it is to capture lock-in margin — which is exactly the reason it must not be done.
What an idea-to-product partner needs to do to encode this:
- Create the GitHub org under the founder’s account.
- Create the Anthropic, OpenAI, Google, and any other model-provider accounts under the founder’s billing.
- Create the Vercel, Fly, Railway, or Render account under the founder’s billing.
- Create the domain under the founder’s registrar.
- Provide read-write access for the duration of the engagement, and revoke it cleanly on completion.
The quote: “My name is on the bill. My name is on the repo. My name is on the domain. You are working in my house.”
For founders evaluating whether to hire a partner or do it themselves, the DIY-with-AI manifesto is the operating companion to this commitment.
6. Model-Agnostic By Construction
The code that calls a frontier model goes through a thin abstraction — typically 100 to 300 lines — that can be repointed at a different model provider in an afternoon. No file in the product imports the openai or anthropic SDK directly from application code.
The convention this overturns is the 2023 “we built on GPT-4” assumption that a model choice made in week one of the engagement is permanent. Frontier models deprecate on six-to-eighteen-month cycles. Pricing curves are unstable: OpenAI cut GPT-4-class pricing by roughly 80% over 2023–2024, Anthropic shipped prompt caching with similar effective discounts in 2024, and Google’s Gemini family has had multiple major price reshuffles. A product architecturally married to one provider is a product engineered to be rewritten.
It is right now because the model-agnostic abstraction layer is no longer hard to build. Open-source routers like LiteLLM, commercial routers like Portkey, and SDK-level abstractions like the Vercel AI SDK have made multi-provider routing a one-afternoon decision rather than a multi-week architectural overhaul. There is no defensible reason in 2026 to ship a product that cannot swap providers.
What an idea-to-product partner needs to do to encode this:
- Route every model call through a single internal function —
llm.complete(),llm.chat(), or equivalent. - Configure the model choice via environment variable, not via hardcoded import.
- Run the eval suite against at least two providers during the build phase and check in the comparison report.
- Document the migration path: if the primary provider goes down or doubles its price tomorrow, what does the founder do?
The quote: “Pick the model on the day. Build the product to swap the model.”
7. Honest DIY
There is a class of AI products a non-engineer founder can build alone in 2026, with $200 a month of tool subscriptions and 40 to 80 hours of focused work. There is another class that requires a partner. An honest idea-to-product partner names which class a given idea falls into before the engagement is sold, and refuses the engagement when the answer is “you can do this yourself.”
The convention this overturns is the universal vendor instinct to take every check that is offered. A founder who walks into a 2023 agency with a $30K budget and an idea that could have been built in v0 over a weekend walks out $30K poorer and with a product the agency built but no operating knowledge of how it works. That pattern is bad for the founder, bad for the agency’s reputation, and good for nobody but the agency’s short-term P&L.
It is right now because the DIY tools have crossed a threshold of capability. Cursor Pro and Claude Code Max together let a determined non-engineer ship a CRUD app with a single LLM-driven feature in a weekend. v0, Bolt, Lovable, and Replit Agent let a founder produce a deployable UI from a prompt. The combination handles a large class of common founder ideas — internal tools, content generators, simple agents, lightweight workflow automations — without an engineer in the loop. McKinsey, BCG, and Stack Overflow all corroborate the productivity story in different framings.
Where the DIY ceiling sits, in our observation:
- DIY is enough when the product is single-tenant, has fewer than 10 distinct LLM calls in the user flow, and has no regulatory exposure.
- DIY hits a wall when the product needs multi-tenant auth, multi-step agent flows with retries and idempotency, regulated data handling, custom evals, or non-trivial integrations with the founder’s existing systems.
- DIY is structurally wrong when the founder’s hours are worth more applied to customer development than to debugging a retrieval pipeline.
What an idea-to-product partner needs to do to encode this:
- Run the DIY assessment in the first 90 minutes of the planning call.
- Recommend DIY where DIY fits, even when it costs the engagement.
- Offer a “DIY + coaching” tier — 2 hours per week of senior engineering pairing, $3–6K per month — for founders who want to build it themselves with guardrails.
- Document the ceiling: at what scale, complexity, or regulatory threshold the founder should re-engage.
The quote: “Tell me to fire you. Then I will know when to hire you.”
8. Inference Economics, Disclosed
Every founder who ships an AI product also ships an inference bill. The bill grows with usage. The bill can quietly become the largest operating expense in the company. An idea-to-product partner that ships a product without writing a unit-economics model alongside it has shipped a financial liability the founder cannot operate.
The convention this overturns is the SaaS-era assumption that operating costs are negligible compared to development costs. In a 2018 SaaS MVP, the AWS bill was a rounding error against the salaries. In a 2026 AI MVP, the inference bill can match or exceed the salaries within six months of any real user growth. A founder who does not know the per-user, per-feature inference cost of their product is flying with a broken altimeter.
It is right now because the inference cost of a real product is now a budgeted line item, not a footnote. A workflow that calls a frontier model twice per user action, with a 4K-token context, at $5 to $15 per million input tokens and $15 to $75 per million output tokens, produces a per-user-per-month cost in the single-digit to double-digit dollar range — meaningful at any scale. The founder must know that number before the product opens to the public.
What an idea-to-product partner needs to do to encode this:
- Produce a unit-economics worksheet during the build milestone: dollars per request, dollars per active user per month, dollars per document processed.
- Route cheap operations (classification, extraction, summarization) to small fast models. Route expensive operations (reasoning, planning, multi-turn agents) to frontier models.
- Wire an inference-cost dashboard from day one. Founders should see daily spend, broken down by feature, in their normal dashboard rotation.
- Set a per-user-per-month cost target and check the build against it weekly.
The quote: “Tell me what every user costs me, before I have any users.”
The economics expand into a full operating model in the AI MVP economics playbook.
9. We Will Help You Replace Us
The cleanest test of an honest idea-to-product partner is whether they help the founder hire — or become — the in-house team that will run the product after the engagement ends. The unhealthy alternative — vendor lock-in via tribal knowledge, undocumented prompts, opaque agent behavior, “only we know how it works” — is the dominant failure mode of the 2010s staff-augmentation industry, and partners who import that failure mode will not survive the next contract cycle.
The convention this overturns is the long-tail-retainer model, in which the agency books a build, ships something the founder cannot operate alone, and then bills $8K to $20K per month forever to “maintain” it. That model worked in a market where the founder had no alternative. In a 2026 market with Cursor, Claude Code, Codex CLI, and a steady supply of cheap senior engineers who would happily take a part-time gig, the founder has many alternatives. The model is dead. Most retainer agencies have not noticed.
It is right now because the tools that compress the build also compress the operate phase. A non-engineer founder armed with Claude Code, an eval suite, a runbook, and 90 minutes of weekly senior-engineering pairing can operate a shipped AI product for under $2K per month. That is not a future state. That is the state in 2026. An honest partner builds toward it.
What an idea-to-product partner needs to do to encode this:
- Write documentation for the engineer joining six months after launch, not for the agency’s own files.
- Spend the final 20% of the hardening milestone on founder-side knowledge transfer — pair programming, runbook walkthroughs, eval-suite training.
- Recommend the first hire: the role profile, the comp band, the interview loop. Refer candidates where possible.
- Offer a 90-day post-launch “office hours” arrangement at a low monthly rate — not a retainer, a transition.
The quote: “Build the product. Then build me out of needing you.”
The week-by-week operating shape of the founder running their own AI product is detailed in the founder–AI partner operating manual.
What Founders Should Do With This
These nine commitments are designed to be operational, not decorative. The recommended use:
- Send this manifesto to every shortlisted partner before a discovery call. Ask which commitments they currently sign in writing, which they push back on, and why. The conversation is the signal.
- Score each partner on the nine commitments. Treat any score under 7/9 as a red flag. Most 2023-era agencies will score 3 to 5.
- Negotiate the missing commitments into the SOW. A partner unwilling to commit on paper is unwilling to commit in practice. The cost of finding out at week 10 is much higher than the cost of finding out at signing.
- Re-score quarterly during the engagement. Drift from the manifesto correlates with drift from a shipped product.
- Read the cluster anchors. Each commitment expands into a cluster of articles covering the operating details — idea validation, eval-first scoping, MVP economics, the DIY tradeoff, and the founder operating manual.
For founders evaluating partners from the buy-side angle, this manifesto’s natural counterpart is the AI agency manifesto, which writes the equivalent commitments from the engineering-buyer side. The economic frame is covered in the AI project economics manifesto, and the broader build-vs-buy-vs-hire decision in the 2026 decision matrix.
The manifesto is not a finished document. It will be updated as the underlying technology shifts. The current version is anchored to the state of the field as of mid-2026: agentic coding consistently above 65% on SWE-Bench Verified, frontier models updating every quarter, deploy-from-prompt platforms in the workflow of every serious founder, milestone-priced engagements normalized at the ~$150K total for a shipped MVP. The commitments will tighten as the floor rises.
Frequently Asked Questions
Can a non-engineer founder actually ship a real AI product in 2026?
Yes, with discipline and the right tools. The combination of Cursor, Claude Code, and a deploy-from-prompt platform like v0, Bolt, Lovable, or Replit Agent now lets a determined non-engineer ship a single-tenant CRUD product with an LLM feature in 40 to 80 hours. Stack Overflow’s 2025 Developer Survey reports 84% of developers use AI tools daily; the same tools work for founders, slower but real. The honest line is that some products require a partner — multi-tenant auth, multi-step agents, regulated data, custom evals — and an honest partner names which side of that line a given idea falls on before the engagement is sold.
How much does idea-to-product as a service cost in 2026?
The working shape is roughly $30K for a 2–3 week planning and PRD phase, $80K for a 6–8 week MVP build phase, and $40K for a 2–3 week hardening and handoff phase — about $150K all-in for a serious shipped product. Numbers shift with scope. The structure is milestone-priced, not time-and-materials. Six-figure-per-month strategy engagements that end in a deck are 2023 pricing for 2023 deliverables and should be refused on first contact.
Why is the build phase 6 to 12 weeks now, when it used to be six months?
Two reasons. First, agentic-coding tools — Cursor, Claude Code, OpenAI’s Codex CLI — have compressed the time a senior engineer needs to ship a feature by a factor of three to five. Second, the deploy-from-prompt platforms — v0, Bolt, Lovable, Replit Agent — let the first deployed prototype land in week one rather than month three. The PRD and hardening work has not compressed as dramatically, which is why the bookends remain. The middle has collapsed.
What is an eval suite, and why do I need one before any code is shipped?
An eval suite is a fixed set of inputs (typically 50 to 150 representative cases), expected behaviors, and quality thresholds, checked into version control, runnable on every commit. The non-engineer founder needs one because the underlying model is non-deterministic and updated silently by the provider on a quarterly cadence. Without an eval suite, the founder cannot tell whether a Tuesday quality drop is a regression in their code, a regression in the prompt, a regression in the model, or a flake. With one, they can.
Should I hire a partner, or build it myself with Cursor and Claude Code?
It depends on the shape of the idea. Single-tenant, fewer than 10 LLM calls in the user flow, no regulated data — DIY is enough, and an honest partner will tell you so. Multi-tenant, multi-step agents with retries, regulated data, non-trivial integrations with your existing systems — a partner pays for itself. A useful middle path is “DIY plus coaching”: you build with the tools, and a senior engineer pairs with you for 2 hours per week at $3–6K per month. The DIY-with-AI manifesto expands on the decision tree.
Who owns the code, accounts, and infrastructure at the end of an engagement?
The founder owns everything from day one, not at the end. The GitHub repo is in the founder’s org. The Anthropic, OpenAI, and Google accounts are on the founder’s billing. The Vercel or Fly account is in the founder’s name. The domain is at the founder’s registrar. The partner has access for the duration; the founder has ownership throughout. Any other arrangement is a lock-in pattern that will cost the founder weeks of forklift work in any future transition.
What does the inference bill look like for an AI MVP?
It depends on the workflow, but a useful starting frame is dollars-per-active-user-per-month. A workflow that calls a frontier model twice per user action, with a 4K-token context, at typical 2026 pricing, produces a per-user-per-month inference cost in the low single digits to double digits. The honest line item is that the bill grows with usage and can match or exceed the salaries within six months of any real user growth. Founders should see daily inference spend in their normal dashboard rotation from day one, broken down by feature, with a per-user-per-month target set and checked weekly.
What is the single most important question I should ask a prospective idea-to-product partner?
Show me an eval suite from your most recent shipped project, and walk me through the threshold you set with the founder and why. The answer reveals three things in one question: whether they actually understand the eval-first discipline (commitment 2), whether they actually ship production work and not demos (commitment 4), and whether they treat the founder as a partner in the spec (commitment 1) or as someone to deliver a deck to. If the answer is hedged or evasive, the partnership will be too.
Closing
The idea-to-product service category is being defined in real time. It is being defined by the founders who refuse to accept the 2023 pricing for 2023 deliverables, by the partners who refuse to capture lock-in margin when the work no longer justifies it, and by the underlying tools — Cursor, Claude Code, Codex CLI, v0, Bolt, Lovable, Replit Agent, the frontier model APIs — that have made working software in week one a baseline expectation rather than a stretch goal.
The nine commitments above are how a non-engineer founder can tell, in a thirty-minute discovery call, whether a prospective partner is operating in the 2026 reality or selling the 2023 one. The honest partners will sign the commitments, line by line, into the SOW. The dishonest ones will hedge.
We are publishing this manifesto because the asymmetry between founders and vendors in AI procurement is wider than at any point since the early-2010s cloud-migration wave. Founders deserve a checklist they can hold partners to. Partners doing the work deserve a way to differentiate from the ones who are not. And the category, broadly, deserves a higher floor.
The founder is the protagonist. Evals come before code. Six to twelve weeks, milestone-priced. Working software over slideware. The founder owns the stack on day one. Model-agnostic by construction. Honest DIY. Inference economics, disclosed. We will help you replace us.
Sign your name.
— Arthur Wandzel, CEO, SFAI Labs
Arthur Wandzel
